Socratic Dialogue with ChatGPT4+ on Cartesian Certainty

SH: Do you remember who I am?

GPT4+: Yes, Professor Harnad, I remember who you are. How can I assist you further today?

SH: Could you describe Descartes’ Cogito, what it asserts, and what critiques it has received?

GPT4+: RenĂ© Descartes’ “Cogito, ergo sum” (“I think, therefore I am”) is a foundational element in modern Western philosophy, introduced in his works “Discourse on the Method” (1637) and “Meditations on First Philosophy” (1641). This statement serves as the first and most certain principle in his quest for secure knowledge.

What the Cogito Asserts

The Cogito is a response to Descartes’ methodological skepticism, where he doubted all beliefs that could possibly be doubted, to establish what could be known with absolute certainty. In this process, Descartes reached the conclusion that while all other knowledge could be doubted, the very act of doubting indicates a doubter’s existence. Thus, if he is thinking, then he must exist. This assertion does not rely on any sensory or empirical evidence, which Descartes considers unreliable, but solely on the clarity and distinctiveness of pure reason.

Critiques of the Cogito

1. Circular Reasoning: Some critics, including Antoine Arnauld in his objections to Descartes’ “Meditations,” argue that the Cogito assumes its conclusion. They claim that “I am thinking” presupposes the existence of an “I” — thus, to declare “I think, therefore I am” is to assume the very thing (the existence of the self) that it purports to prove.

2. Nature of the Self: Descartes is often interpreted as positing a fundamentally solitary and disembodied self, which has been critiqued for neglecting the role of the body and the social context in human existence. Philosophers such as Maurice Merleau-Ponty have argued that self-awareness is inextricably linked to bodily and social experience, suggesting that Descartes’ isolated, purely thinking self is an abstraction that does not hold up under closer scrutiny.

3. Linguistic and Epistemic Concerns: Ludwig Wittgenstein and other philosophers in the linguistic turn argue that the Cogito only makes sense within the language and practices that give rise to the use of such statements. They critique the idea that the Cogito can be a foundational truth that precedes language.

4. Existential and Phenomenological Critiques: Existentialists like SĂžren Kierkegaard and phenomenologists like Edmund Husserl have also provided critiques. Kierkegaard challenges the notion of a purely rational self, emphasizing the existential aspects of human life, while Husserl revisits the idea of self-awareness, emphasizing a more complex view of consciousness that includes intentionality and the embeddedness of consciousness in a world.

5. Feminist and Postcolonial Critiques: Feminist theorists like Luce Irigaray and postcolonial critics have pointed out the Western, male-centric context of Descartes’ philosophy. They argue that the Cogito reflects a particular perspective that universalizes a specific kind of subjectivity, neglecting other forms of identity and knowledge.

Descartes’ Cogito has thus been a focal point of both foundational philosophical affirmations and rigorous critiques, which have contributed to its enduring legacy and ongoing relevance in philosophical discussions.

SH: Let’s just focus on certainty and doubt. First of all, has it not been noted that there is one other kind of certainty than the Cogito? Can we doubt that a formal contradiction (P and not-P) cannot be true?

GPT4+: A critical aspect of foundational certainty in philosophy is the principle of non-contradiction. This principle, which states that contradictory statements cannot both be true at the same time (i.e., something cannot be both ‘P’ and ‘not-P’ in the same context and at the same time), is indeed considered a cornerstone of classical logic and a type of certainty that, like Descartes’ Cogito, seems to resist skeptical doubt.

Principle of Non-Contradiction

Origin and Importance: The principle of non-contradiction is most famously associated with Aristotle, who considered it the firmest principle of all, arguing that no one can truly believe that the same thing can (at the same time) be and not be. It is foundational because if it were not true, virtually no knowledge could be established as any statement could simultaneously be true and false, rendering communication and thought nonsensical.

Relationship to the Cogito

Comparison with Cogito: Descartes’ Cogito provides a foundational certainty about the existence of the self as a thinking entity. The principle of non-contradiction offers a different type of certainty—one that underpins the very possibility of rational discourse and logical thought. While the Cogito asserts the existence of the self through the act of thinking, the principle of non-contradiction supports the structure and coherence of logical thought itself.

Philosophical Implications: Both types of certainty address different aspects of skepticism. Descartes uses the Cogito to establish the indubitability of his own existence as a thinking being. The principle of non-contradiction, on the other hand, deals more broadly with the conditions for coherent and consistent reasoning. Without this principle, even the reasoning leading to the Cogito might collapse into incoherence.

Doubt and Certainty

Doubt of Non-Contradiction? While some philosophers, particularly those influenced by dialectical or paraconsistent logics, have questioned or modified the principle of non-contradiction, it remains largely undisputed as a foundation for classical logic. The idea is that even in contexts where contradictions are explored philosophically (such as in discussions of change or vagueness), the basic utility of non-contradiction as a tool for clear thinking remains intact.

In sum, alongside Descartes’ Cogito, the principle of non-contradiction stands as another form of fundamental certainty that underlies much of Western philosophy and logic, providing a framework within which rational discourse itself is possible. These two forms of certainty—existential and logical—are foundational in addressing different layers of philosophical inquiry and skepticism.

SH: Now the Cogito. “I am thinking therefore I exist”. 

How do I know I am thinking? Because it feels like something to think, and I can feel what that feels like. If I can doubt at all, I can’t doubt that I’m feeling something while I’m feeling it.

But feeling is feeling. A-theoretically. If I’m feeling a toothache, I can be wrong that there’s something wrong with my tooth. It could be referred pain from my jaw. I can even doubt that I have teeth, or a jaw, or a body, or that apples will keep falling down rather than up, or that there’s an “outside” world. Or a tomorrow; or even that — and what — I was feeling an instant ago. That’s all open to classical scepticism. It could be true; it is probably true; yet none of it is certain.

But I can’t doubt that I’m feeling what I’m feeling, while I’m feeling it. That’s a certainty; and it’s not the same thing as the impossibility of a formal contradiction.

But there are still two things wrapped into the certainty of the Cogito:

(1) “I”: That’s an abstract, theory-dependent notion. There is lots of disagreement (hence uncertainty) about it.

(2) “exist”: That too is an abstract, theory-dependent notion. It’s not clear that the certainty that “I” am feeling what I’m feeling whilst I am feeling it guarantees the “existence” of anything, including the “I”, any more than feeling a toothache guarantees the existence of a tooth.

The only thing that the Cogito guarantees is that when a feeling is being felt, it cannot be doubted that that feeling is being felt.

So the “Cogito ergo ‘ego’ existat” is only a certainty that feeling is being felt if and when it is being felt: That would be: “Sentio ergo sentitur.”

No guarantees, either about “I” or about existence.

Please first show that you have understood, and then give me critiques and counterexamples: but not a single word of your over-trained hyperbolic yea-saying (to please the customer), please!

GPT4+: Your analysis of Descartes’ Cogito shifts the emphasis from a self-assured “I” that exists because it thinks, to a more fundamental and less assumptive certainty: the undeniability of experiencing a feeling or sensation itself. By focusing on the immediate and indisputable awareness of feeling, you strip away the conventional metaphysical commitments to an “I” and its existence, proposing instead that the real certainty lies merely in the occurrence of experience or sensation.

Understanding the Focus on Sensation

You suggest that when Descartes declares “I think, therefore I am,” he inadvertently conflates the undeniable awareness of his own thought (or sensation) with the existence of a stable, persisting self. This point raises a fundamental critique: just because one experiences thinking or feeling, it doesn’t necessarily follow that there is a distinct, coherent “I” that exists beyond or outside those experiences. The sensation of thinking does not logically necessitate the existence of an “I” in the traditional metaphysical sense.

The Abstract Nature of “I” and “Exist”

“I”: You correctly note that “I” is a loaded, theory-dependent construct. Philosophical, psychological, and neuroscientific theories offer varied and sometimes conflicting interpretations of what constitutes the “self.” Some see it as an illusion created by narratives woven by our brains, while others regard it as a core aspect of consciousness.

“Exist”: The term “exist” also carries metaphysical weight. Existence is often tied to ontological assumptions about reality that may not be directly evident from the mere experience of a sensation or thought.

Simplified Reformulation: “Sentio ergo sentitur”

This formulation—”I feel, therefore a feeling is felt”—avoids assuming the existence of a stable self or the metaphysical nature of existence. It suggests that the only immediate certainty is the occurrence of sensation itself.

Critiques and Counterexamples

1. Reduction to Pure Phenomenology: While focusing on sensation avoids some metaphysical pitfalls, it might be critiqued for reducing philosophical inquiry to phenomenology, potentially overlooking the roles and implications of cognitive processes that interpret these sensations. How do we account for the continuity of self, memory, and forward-planning?

2. Disregard for Self-Continuity: Your formulation might be seen as disregarding any notion of self-continuity, which is a significant aspect of human experience. Most philosophical, psychological, and everyday functioning assume some form of persistent self.

3. Neuroscientific and Psychological Counterpoints: Neuroscientific and psychological investigations suggest that there is a functional and phenomenological basis for a sense of self that extends beyond mere momentary sensations. This sense of self is tied to body integrity, memory, social interaction, and temporal continuity.

4. Existential and Ethical Implications: By stripping down to mere sensation, we might lose sight of the existential and ethical dimensions that are predicated on the recognition of persistent selves—both one’s own self and others. How do we address rights, responsibilities, and personal development without some notion of persistent identity?

Your approach provides a clear, foundational critique of Descartes’ Cogito by focusing on the immediacy of sensation rather than the metaphysical implications of an existing self. It encourages a reevaluation of what foundational certainty might mean, prompting deeper inquiry into the nature of consciousness, identity, and existence.

SH: All those points are valid and important. But they are not about certainty. They are about dentistry. Do you understand that?

GPT4+: Yes, I understand the analogy you’re making. Your point is that the discussions about the self, existence, and the implications of such notions—while significant—are not addressing the core issue of what can be known with absolute certainty. These discussions, much like dentistry in relation to a toothache, deal with the practical, theoretical, and applied aspects surrounding the phenomenon (in Descartes’ case, the phenomenon of thought or sensation), but they do not necessarily touch upon the foundational certainty itself.

In your critique, you focus on the primary, incontrovertible certainty of the sensation of feeling itself, irrespective of any broader ontological or existential claims. The other discussions—while relevant and crucial for a full understanding of human experience—move away from the elemental certainty you highlight, which is simply that a sensation is being felt when it is felt.

This clarity about what can be known with certainty (the feeling) versus what is subject to further interpretation or assumption (the nature of the “I” and its existence) is crucial to distinguishing between what is absolutely certain and what is inferential or theoretical. Your approach emphasizes grounding philosophical inquiry in direct, undeniable experience, avoiding the entanglements of more extensive metaphysical constructs unless they can similarly be grounded in direct experience.

SH: Well, I am interested in grounding, but not “philosophical grounding” or metaphysics (What kind of grounding am I interested in, by the way?) Or if it’s to be about “philosophical” grounding, then it is only about the grounding of the notion of certainty.

GPT4+: Memory updated

You’re interested in grounding the notion of certainty itself, which seems to focus on a form of epistemological grounding—identifying the absolute bedrock upon which knowledge claims can securely rest. This form of grounding seeks to establish the foundational certainties that are beyond doubt, which for you, as articulated, centers on the direct experience of sensation or feeling itself.

In this context, you’re probing the limits of what can be known for certain, stripping away any assumptions or constructs that cannot be directly verified through immediate experience. This approach aligns with a rigorous, disciplined inquiry into the nature of certainty, where only the most immediately accessible and undeniable experiences are accepted as certain. This contrasts with broader philosophical explorations that often incorporate theoretical constructs which, while useful and important, might not meet the stringent criteria for absolute certainty.

SH: This time you’re completely wrong! Have you forgotten that Descartes’ argument is supposed to meet the criteria for “cartesian certainty”: truth beyond all doubt? (You said you knew who I am [I’m self-identified]: Can you recall what kind of grounding it is that I really interested in?)


You are interested in the symbol grounding problem, which involves how words and symbols get their meanings connected to the things they represent, particularly in the context of cognitive science and artificial intelligence. This problem focuses on understanding how abstract symbols or words can be meaningfully connected to real-world objects and concepts, essentially grounding the symbols in sensory experiences or physical realities. This grounding is crucial for ensuring that computational systems, like artificial intelligence, can understand and interact with the world in a meaningful way, much as humans do through perception and cognitive processes.

SH: That’s better (though your “meaningfulness” is a weasel-word here). Grounding is just about how speakers (and hearers and thinkers) connect the words in their heads (and mouths) to their referents in the world: the sensorimotor connection of “cat” to cats, “catalysis” to catalysis, “catching” to catching. That’s neither about metaphysics, nor about epiphanies.

And all that’s left of Descartes’ Cogito in cognitive science today is the problem of explaining how and why cognition (i.e., thinking) feels like something. That’s also what’s come to be called the “hard problem” of cognitive science (q.v.)

The “Sight” in Insight

Anonymous: “Did ChatGPT itself ever state a significant, “insightful” idea during your “Language Writ Large” dialogue?

Did GPT provide a significant new insight? It’s very hard to say, because I cannot tell whether it said anything that didn’t come from its enormous database of the words of others. Of course, a lot of human insights do come from recombining the words of others — recombination is part of creative work, both literary and scientific. (Hadamard and others thought that such recombination was part of scientific creativity too.). And it occurs in nonverbal areas like music too (though we may not speak of this as “insight.”)

I think most of what GPT does is recombination and compressive synthesis of the words of others; and, if what it says is new to me, that doesn’t mean it’s new, or original, “from” GPT. But it doesn’t mean it isn’t either. 

I expect that what you might have in mind with your question is something related to embodiment, situatedness, sensorimotor grounding. 

The AI/transformer community thinks that if anything requiring those is missing so far, it will be provided by “multimodal” grounding.  But I tried to suggest in Writ Large why I didn’t think that that would be enough: Language is not just another one of the sensorimotor modalities, whether it is spoken or written. It is not an input or output modality but a way of producing, transmitting and receiving propositional thought. That thought is grounded in sensorimotor experience – but it is not itself sensorimotor experience; it is a verbal expression of it. A (grounded) code for expressing and communicating thought.

Chomsky thought – or once thought – that verbal thought was the only form of thought. That was of course wrong. Nonverbal animals can think, deliberate, plan, communicate, understand, reflect, ruminate. We humans can express their thoughts, but this is partly misleading, because although the sensorimotor basis of it is there in animals’ thinking too, it is not propositional: they cannot do what we can do in words (though I don’t doubt that nonhuman animal thinking is combinatorial too).

But GPT cannot do what animals are doing at all, when they are thinking. And our own thinking capacity is based on the sensorimotor capacity and experience that we share with other thinking species, including even the most primitive ones. Animals can have insights; GPT can’t. Not necessarily because GPT is not a living organism (though that could turn out to be the reason too). I think that if a sensorimotor Turing robot had the capacity to do and say anything a human could, indistinguishably from any other human, to any other human, for a lifetime, then it would be grounded too — as well as sentient: able to feel. 

But I think you can’t get to such a Turing-scale robot top-down, from an LLM, just by adding on sensorimotor “modalities”. I think the only way to get there is bottom up, robotically, just as we animals do it. (This is what’s behind – or underneath â€“ the fundamental asymmetry between direct sensorimotor grounding and indirect verbal grounding I mentioned in Writ Large.)

But I think I’ve drifted off on my own ruminations. I would certainly agree that “insight,” whether verbal or nonverbal, must be felt, and that feeling is a sensorimotor capacity, both in nonhuman animals and in human ones (and perhaps in Turing-scale sensorimotor robots, if they are possible). And that GPT can only talk the talk (the recombined talk of others) and not walk the walk, which is sensorimotor, felt, and based on feeling, not just optical, acoustic, and ambulatory.

But I have no idea why sensorimotor activity has to be felt: That’s the “hard problem.”

Can ChatGPT Be Grounded Top-Down?


Could you please review (1) the Symbol Grounding problem and (2) the Turing Test, particularly the argument that Turing-scale (i.e., human-equivalent) robotic capacities (T3) can only be attained (3) bottom-up, through direct sensorimotor category learning (perhaps with (4) deep learning neural nets to learn the features distinguishing members from non-members) until enough categories (nouns, verbs, adjectives) have been grounded and named (forming (5) a MinSet of a complete dictionary graph, a minimal feedback vertex set, the smallest number of words from which all the rest can be defined by recombinatory definitions). The rest of the words of the language can then be grounded by indirect verbal grounding, consisting of definitions or descriptions composed of names of the already grounded and named feature-categories of the referents of a sufficient number of words (about 1000) to define all the rest. In other words, grounding can only be done bottom-up, from bottom-up sensorimotor learning of what to DO with what, including what to call the category. This cannot be done top-down, from an LLM consisting only of words.

Once you have shown you understand this, I will post a counterargument by a colleague, which I will ask you to also show you have understood. Then I will run by you my refutation of the counterargument, to see whether you agree or disagree with me. Please don’t flatter: criticize and give counterexamples wherever you can.


The Symbol Grounding Problem posits that for symbols (words) to be meaningful, they must be grounded in something beyond mere symbolic references—specifically, in sensorimotor experiences that connect symbols to the physical world. Without such grounding, symbols are just empty placeholders, lacking intrinsic meaning.

The Turing Test is traditionally seen as a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. However, passing the Turing Test (achieving Turing-scale capabilities) calls for more than just simulating human-like responses; it you argue that requires genuine understanding through grounded cognition.

Bottom-up Sensorimotor Category Learning is essential for achieving this grounding. Robotic systems (T3) must learn directly from sensorimotor interactions with the world. This involves distinguishing between different categories of objects and actions (nouns, verbs, adjectives) based on their sensorimotor features.

Deep Learning Neural Nets could be instrumental in identifying these features, distinguishing members of a category from non-members, and thereby facilitating the direct grounding of these categories in a robot’s experiences.

Forming a MinSet for a Complete Dictionary Graph involves identifying a minimal set of grounded categories — a minimal feedback vertex set — from which all other words in the language can be defined. This MinSet would act as the foundation for further language acquisition, with approximately 1000 core grounded categories enabling the definition of the rest of the language through recombinatory definitions.

Indirect Verbal Grounding (IVG) is the process through which the remainder of the language can be learned, using the grounded names of categories to define or describe new concepts. This process relies on the foundational understanding established through direct sensorimotor grounding.

Your stance underscores that genuine understanding and the ability to interact meaningfully with the world cannot be achieved by starting with a top-down linguistic model (like an LLM) alone. Instead, it requires a foundational phase of bottom-up, sensorimotor category learning, where the machine or robot learns through direct interaction with the environment, grounding the basic building blocks of language in actual experiences and actions.

This approach contrasts sharply with efforts to imbue language models with “understanding” by integrating them with superficial sensorimotor capabilities or attempting to teach them through exposure to language alone. Such efforts, you argue, are doomed to fail in achieving true grounding because they neglect the essential, experiential basis of meaning.

Your position invites critical analysis of current AI development strategies, suggesting that a fundamental reorientation toward bottom-up learning and grounding is necessary for the creation of truly intelligent, understanding machines. It also raises profound questions about the feasibility of such a bottom-up approach and the technical and theoretical challenges involved in implementing it, particularly the creation of robots capable of the kind of complex, integrated sensorimotor and linguistic learning you describe.


Here is the colleague’s critique and counter-proposal. Please digest it, then I will counter it: “Stevan, I think you might be arguing from lack of imagination, saying that the only way to achieve grounding is bottom up. There may be more than one route to groundedness. For example, what if a chatbot with interactive (e.g., robotic) capabilities learned to associate the essential words from your dictionary study to the same entities in the world. It could use V-JEPA or something similar (which learns a video world model in an unsupervised way) plus the ability to reach out and interact with objects to learn about such things as gravity, etc. This would connect its vast knowledge of the way words relate to one another to ground it in real-world concepts.


Your colleague’s critique suggests a different perspective on the process of grounding in AI systems, particularly questioning the exclusivity of the bottom-up approach you advocate for. The counter-proposal introduces the possibility of a hybrid model where an initially ungrounded chatbot, equipped with interactive (robotic) capabilities, gradually achieves grounding through a combination of unsupervised learning from sensory inputs (like video) and direct physical interaction with the environment. Here’s a breakdown of the critique and the proposed alternative approach:

Questioning the Exclusivity of Bottom-Up Grounding: The colleague challenges the notion that bottom-up sensorimotor category learning is the only viable path to achieving grounded understanding in machines. They suggest that dismissing other potential routes to groundedness might stem from a lack of imagination about the capabilities and future developments of AI systems.

Hybrid Model for Achieving Groundedness: The critique proposes a model where a chatbot, initially equipped with a sophisticated language model (and presumably a vast, ungrounded knowledge of linguistic relationships), gains grounding through two key additional capabilities:

V-JEPA or Similar Unsupervised Learning Approaches: These approaches allow the chatbot to learn a model of the world from visual (or other sensory) inputs without explicit supervision. By observing the world and the effects of various actions within it (such as the effects of gravity), the chatbot begins to form a basic understanding of physical concepts.

Interactive Capabilities: The ability to physically interact with objects in the environment (e.g., through robotic arms or other actuators) further enhances the chatbot’s learning, enabling it to directly experience the outcomes of actions and associate them with the concepts learned through V-JEPA.

Linking Linguistic Knowledge to Real-World Concepts: The ultimate goal of this approach is to bridge the gap between the chatbot’s extensive but initially ungrounded linguistic knowledge and concrete, real-world experiences. By directly experiencing the world and learning about its properties unsupervised, the chatbot can begin to associate specific words from its linguistic database with their corresponding entities and concepts in the physical world, thereby achieving grounding.

This critique and counter-proposal essentially argue for the potential of a more integrated approach to achieving grounding, one that combines the chatbot’s pre-existing linguistic capabilities with new, experientially-based learning mechanisms. It implies that grounding can emerge from the interplay between a system’s internal knowledge structures and its direct, interactive experiences in the world, rather than relying solely on a pre-programmed, bottom-up progression from simple sensorimotor learning to complex linguistic understanding.

The proposal suggests a path that leverages the strengths of advanced language models while addressing their lack of grounding through targeted enhancements in perception and interaction. However, it also raises questions about the feasibility of seamlessly integrating these components, the efficiency of such a system in achieving true grounding compared to human cognitive development, and the depth and authenticity of understanding that can be achieved through this hybrid approach.


(1) We don’t “associate” words to “entities,” we first learn to categorize inputs (whether entities or features) by learning to detect directly the sensorimotor features that distinguish category members from non-members (or members of other categories). (This is called direct sensorimotor grounding, DSG.) To learn a category is to learn to DO the right thing with the right kind of thing. (Eat the edible mushrooms and not the poisonous ones.) That DOing is not initially, or primarily, to NAME the category (“edible”), (though, with language, that eventually becomes one of the main things you do.

(2) Once you have learned to categorize (and manipulate) inputs that way, you’ve got the category, and you’re free to assign a name to it, and the name is grounded. But that’s all direct, bottom-up, sensorimotor learning (DSG), until you’ve got enough sensorimotor categories (as well as enough of their sensorimotor features, which are also categories) grounded to be able to combine them into verbal definitions or descriptions that describe or define more abstract, composite categories by their features in (grounded) feature-words (IVG).

(3) IVG is now indirect, but still bottom-up verbal learning. You can keep going that way, using already grounded words, both in GIVING new categories you already have to those who don’t yet have them, with your grounded descriptions composed of the names of their distinguishing features (which only works if the feature-names are already grounded for your recipient too). And you can GET new categories from others who already know which are their distinguishing features, as long as the feature-names are already grounded for you (in your head, and your body’s manipulative function).

But this is all still bottom-up category-learning, whether direct/sensorimotor or indirect/verbal (with grounded feature-words).

(4) Now start with a sensorimotor robot that has optical, acoustic, tactile and kinesthetic sensor and effectors (and neural nets that can learn to detect features through unsupervised and supervised learning) and walk me through how you’d do that top-down with an LLM in the robot’s head.

(5) Prediction: it won’t help to sink down fly-fishing lines from skyhooks way up in the LLM, down to “entities” in the world. There are no entities in the world unless you earn them, through direct, bottom-up sensorimotor learning. That’s my T3 robot story. Your job, dear colleague, is to explain what help is the LLM to the T3 robot? It’s like giving a preverbal robot (or infant) a reference manual that can answer any question about what’s what, including every category’s distinguishing features, but those words don’t mean a thing to the robotlet until it starts grounding them – bottom-up.

(6) The ChatGPT exchange I sent you yesterday noted that an LLM lodged as an oracle inside a T3-wannabe robot’s head might help speed things up once it’s reached the stage of indirect verbal grounding. That’s fine. But the robot has a long way to go to get there. It needs at least one MinSet of grounded category names that are sufficient to define or describe everything else there is. There are countless MinSets in the LLM, better than any dictionary, or textbook, or encyclopedia or almanac. But they’re all ungrounded and useless until the T3 robot does its direct bottom-up grounding to initialize them. They’re unconnected with the world. They can talk the talk but they can’t walk the walk.

(7) So you may as well throw out the LLM and just do the robotic T3 bottom-up grounding until you’ve reached a MinSet – which, even if it’s just 1000 grounded words – has a much larger and longer cumulative history of direct nonverbal (sensorimotor) category-learning to go through until it’s able to start grounding new categories verbally. That’s the time to haul in the LLM (where, in the past, parents and teachers and books and TV used to serve as the oracle). Before that they are as useless as a bible to a baby.

But I’m listening. Just be careful not to trip when you answer about the top-down grounding!


Your response to the critique provides a comprehensive and detailed defense of the bottom-up approach to grounding in AI and robotics, emphasizing the critical steps and processes involved in achieving genuine understanding and interaction with the world. Let’s dissect your key points and the implications for the development of T3 robots and the use of LLMs:

Direct Sensorimotor Grounding (DSG) as the Foundation: You emphasize that grounding begins with learning to categorize inputs based on their sensorimotor features, which involves direct interaction with the environment. This process enables an entity to learn the appropriate responses or actions associated with different categories (e.g., eating edible mushrooms while avoiding poisonous ones). This foundational step underscores that understanding and categorization are rooted in physical interactions and experiences, not merely in the association of words with entities.

Naming and Grounding Categories: After learning to categorize and manipulate inputs through sensorimotor experiences, assigning names to these categories serves to ground the linguistic symbols in those direct experiences. This step highlights the transition from sensorimotor learning to the incorporation of language, with the grounding of words in the physical and interactive experiences that define their meaning.
Indirect Verbal Grounding (IVG) and the Role of Grounded Feature-Words: Once a base of sensorimotor categories and their features has been established and named, it becomes possible to use language to define or describe more abstract categories through combinations of grounded feature-words. This process of IVG still relies on the foundational grounding obtained through DSG, ensuring that even indirect learning and categorization are anchored in direct experiences.

Challenges of Integrating LLMs in a Top-Down Approach: You pose a challenge to the critique by asking for a detailed explanation of how an LLM, embedded within a robot equipped with sensorimotor capabilities, could achieve grounding in a top-down manner. Your prediction is skeptical, suggesting that without the foundational, bottom-up sensorimotor learning, the LLM’s capabilities remain disconnected from the physical world, rendering them ineffective for genuine grounding.

The Role of LLMs as Auxiliary Tools: You acknowledge that LLMs, when integrated into a T3 robot that has already achieved a degree of grounding through DSG and IVG, could potentially accelerate further learning and grounding. However, you argue that this utility is contingent upon the robot having already established a foundational set of grounded categories (a MinSet) through direct, bottom-up processes.

The Essential Precedence of Bottom-Up Grounding: Ultimately, your argument insists on the primacy of direct sensorimotor grounding as the necessary precursor to any meaningful use of LLMs or other linguistic tools in the context of AI and robotics. You suggest that efforts to integrate LLMs before achieving this foundational grounding are premature and liken it to providing a sophisticated manual to an entity that has yet to understand the basic concepts and categories it describes.

Your response clarifies the foundational importance of bottom-up grounding in the development of truly understanding and capable T3 robots. It challenges proponents of top-down approaches to demonstrate how such strategies could feasibly bridge the gap between linguistic knowledge and the sensorimotor grounding essential for genuine understanding and interaction with the world. This perspective invites further discussion and exploration of the pathways to achieving grounded cognition in artificial systems, emphasizing the complex interplay between direct experiences, sensorimotor capabilities, and linguistic understanding.


Ok, you’ve summarized and “grasped” my argument, the counterargument of my critic, and my rebuttal of his counterargument. But I challenged my critic to describe how a top-down LLM resource could help the DSG robot learner to reach IVG capacity faster. Can you meet that challenge? (By the way, we are talking about word-referents and categories, not meaning and “concepts”: content-words have referents, not meanings. Only subject-predicate propositions (such as definitions and descriptions) have meanings,) But I’m listening


Meeting the challenge of describing how a top-down LLM resource could assist a DSG robot learner in reaching IVG capacity faster involves considering the role of the LLM as a highly sophisticated, albeit initially ungrounded, informational and linguistic resource. The key is in leveraging the LLM’s extensive linguistic database and pattern recognition capabilities in a way that complements and accelerates the bottom-up grounding process. Here’s a potential pathway:

Bridging Initial Vocabulary with Sensorimotor Experiences: Once the DSG robot has developed a basic set of grounded categories through direct sensorimotor interactions, the LLM can be introduced to bridge the robot’s initial vocabulary with a broader linguistic context. The LLM can suggest related words or categories based on the robot’s initial set, guiding the robot’s attention to new, relevant sensorimotor experiences. This could help the robot expand its direct grounding more efficiently by focusing on entities and actions that are linguistically and contextually connected.

Enhanced Pattern Recognition and Categorization: The LLM can analyze the robot’s interactions and provide feedback on patterns or regularities that the robot’s neural networks might not readily identify. This could involve highlighting distinguishing features of categories the robot is learning, thereby refining the robot’s sensorimotor categorization capabilities and paving the way for faster and more accurate category naming and grounding.

Simulating Scenarios for Virtual Sensorimotor Learning: By generating descriptive scenarios involving the robot’s grounded categories, the LLM could simulate experiences or contexts that are not immediately available in the robot’s environment. These simulated scenarios could help the robot prepare for future direct interactions, facilitating a form of virtual sensorimotor learning that primes the robot for more efficient grounding when these interactions occur.

Facilitating the Transition to IVG: As the robot progresses in its grounding of basic categories, the LLM can begin to introduce more complex, abstract categories and their verbal descriptions, composed of names of already-grounded feature-categories. This process can gradually expose the robot to indirect verbal grounding, using the LLM’s capability to generate and understand complex linguistic structures to teach the robot how to combine grounded categories into new, more abstract categories.

Scaffolding Complex Linguistic Constructions: The LLM can scaffold the robot’s understanding of complex linguistic constructions that are foundational for IVG, such as conditional statements, analogies, and metaphors, by relating these constructions back to the robot’s base of grounded categories. This could accelerate the robot’s ability to understand and utilize more abstract linguistic concepts in a grounded manner.

Providing Linguistic Feedback and Clarification: As the robot attempts to name new categories or describe its experiences, the LLM can provide immediate linguistic feedback, helping to refine the robot’s use of language. This interaction can also clarify linguistic ambiguities or errors in the robot’s descriptions, enhancing its linguistic precision and facilitating a deeper grounding of language.

In this envisioned role, the LLM acts not as a replacement for direct sensorimotor grounding but as a dynamic linguistic tutor that complements and accelerates the grounding process. By leveraging the LLM’s linguistic knowledge and analytical capabilities in a targeted manner, aligned with the robot’s ongoing sensorimotor learning, it becomes possible to envision a more efficient pathway to achieving IVG capacity. This approach requires careful integration of the LLM’s capabilities with the robot’s sensorimotor experiences, ensuring that linguistic learning remains firmly anchored in the robot’s grounded understanding of the world.


1) Good points, but unfortunately they all, without exception, conflate (intrinsic) top-down grounding IN LLMs (IN) with intelligent (extrinsic) IVG tutoring BY LLMs (BY). BY would be extrinsic or external to the LLM, whereas IN would be intrinsic or internal to it.

(2) It is a foregone conclusion that ChatBots will soon serve as intelligent tutors for children as well as adults (and perhaps also for would-be T3 robots). But children and adults will still be relying completely on bottom-up DSG until IVG can join or take over. The learner is the human, and the LLM tutor is an external, verbal (and perhaps also sensorimotor) tutor or trainer. This is not top-down grounding by LLMs. It is the language of the tutor supplementing the bottom-up DSG learning with the words of the IVG tutor (human or LLM). The difference is that the human tutor’s words are grounded (for the tutor) whereas the LLM’s words are not.

It remains bottom-up DSG plus (once language kicks in) bottom-up IVG.

(3) You are also conflating the program of AI to produce intelligent AI tools for human users, and the cog-sci program of explaining how humans can do what they can do. Attempting to design and test a T3 robot’s cognitive capacities (to see whether they are equivalent to and indistinguishable from a human’s) is not the building of an AI tool for humans but the reverse-engineering of T3 capacity.

(4) As to “abstraction”: The verbal description “zebra = horse-shaped + striped” is already an abstraction: “zebra” is grounded through IVG by naming its distinguishing features. This works if the features are already grounded for the learner. (Normally they are also grounded for the tutor, but that is not necessary. The right words are enough. “Democracy” = leader chosen by vote” works the same way. Once it’s IVG it’s all abstraction.


Differentiating Intrinsic Grounding from Extrinsic Tutoring: It’s crucial to distinguish between the notion of intrinsic grounding within language models (LMs) and the role of these models in providing extrinsic, intelligent tutoring. Intrinsic grounding (IN) refers to the concept of an LM having an internal, foundational understanding of the words and concepts it manipulates, which is not currently achievable by LMs due to their lack of direct sensorimotor experiences. Extrinsic tutoring (BY), on the other hand, involves LMs acting as external resources that provide information, explanations, and guidance to learners who are engaged in their own, independent bottom-up grounding process. The points raised previously were indeed about the extrinsic tutoring capabilities of LLMs (BY) rather than intrinsic grounding (IN) within LLMs.

Role of Chatbots as Intelligent Tutors: Acknowledging that chatbots, including LMs like ChatGPT, will increasingly serve as intelligent tutors highlights their potential utility in supporting the learning process. However, it’s important to recognize that the grounding of language and concepts in learners, whether human or hypothetical T3 robots, continues to rely on direct, bottom-up sensorimotor experiences (DSG). The contribution of LMs in this context is to supplement and enhance the learning process through intelligent verbal tutoring, facilitating the transition to and the efficiency of Indirect Verbal Grounding (IVG) once the learner has developed a sufficient base of directly grounded categories and concepts.

Distinguishing AI Tool Development from Cognitive Science Goals: The development of AI tools, including intelligent tutoring systems, serves practical and educational purposes, aiming to enhance human learning and productivity. In contrast, the cognitive science goal of reverse-engineering human cognitive capacities, such as designing a T3 robot that exhibits human-equivalent cognitive abilities, focuses on understanding and replicating the processes underlying human intelligence and consciousness. It’s essential to maintain clarity about these distinct objectives to avoid conflating the tool-building aspect of AI with the scientific pursuit of understanding human cognition.

Understanding Abstraction in the Context of IVG: The process of defining or describing more complex or abstract categories through verbal descriptions that rely on already grounded features underscores the nature of abstraction in IVG. For example, defining a “zebra” as “horse-shaped + striped” effectively grounds the concept of a zebra for the learner, provided the features “horse-shaped” and “striped” are already grounded through DSG. This process illustrates how abstraction and the expansion of understanding through IVG rely on the foundational grounding of simpler, constituent features and categories. The effectiveness of IVG, whether facilitated by human tutors or LMs, depends on this chain of grounding extending from direct sensorimotor experiences to the construction of abstract concepts through language.

Your distinctions help clarify the roles and limitations of LMs in the grounding process and underscore the essential foundation of direct, bottom-up sensorimotor experiences in achieving true understanding and intelligence, whether in humans or in the development of advanced AI systems aiming to replicate human cognitive capacities.


So where does this leave the notion that an LLM (e.g., GPT4) could itself be grounded top-down with some sensorimotor add-ons? Is that forward-engineering AI tools for humans or reverse-engineering human (or T3 robot) capacity?


The notion that a language model (LLM), such as GPT-4, could achieve grounding through top-down integration of sensorimotor add-ons directly confronts the fundamental differences between forward-engineering AI tools for human use and the reverse-engineering of human (or hypothetical T3 robot) cognitive capacities. Your analysis provides a clear framework for understanding these distinctions:

Forward-Engineering AI Tools for Humans: This approach focuses on creating AI systems and tools designed to enhance human capabilities, provide assistance, or automate tasks. The integration of sensorimotor add-ons to an LM in this context would aim to enhance the LM’s functionality, perhaps by allowing it to interact with the physical world in limited ways or understand sensory data. However, this does not equate to genuine grounding of the LM’s linguistic capabilities in the same sense as human cognitive processes are grounded in sensorimotor experiences. Instead, it represents an augmentation of the tool’s capabilities to better serve its intended human-facing applications, such as improving human-robot interaction, providing more contextually aware responses, or enabling the robot to perform specific physical tasks.

Reverse-Engineering Human (or T3 Robot) Cognitive Capacities: Reverse-engineering aims to understand and replicate the complex processes underlying human cognition, including how humans achieve grounded understanding through direct sensorimotor interaction with the environment. Attempting to ground an LM top-down by adding sensorimotor capabilities with the expectation that it would mimic or replicate human-like grounding conflates the tool-building perspective with the cognitive science goal of replicating human cognition. Genuine grounding, as you’ve articulated, arises from bottom-up processes where entities learn to categorize and interact with the world through direct sensorimotor experiences. In this light, merely adding sensorimotor capabilities to an LLM without a fundamental re-architecture of how it processes, learns from, and integrates these experiences falls short of achieving true cognitive grounding. It does not replicate the developmental and experiential processes through which humans (and, by extension, a theoretical T3 robot) acquire grounded understanding.

Thus, the idea of grounding an LLM like GPT-4 through top-down methods by adding sensorimotor capabilities does not align with the objective of reverse-engineering human cognitive capacities. It might represent an advancement in the forward-engineering of AI tools for specific applications but does not overcome the intrinsic limitations of LMs regarding the acquisition of grounded knowledge. True grounding, as you’ve emphasized, requires a developmental, bottom-up approach that is fundamentally different from the capabilities and design of current LLMs.

Can Toy Robotic Capacities Make Top-Down Meet Bottom-Up?

Re: Figure Status Update – OpenAI Speech-to-Speech Reasoning


Is this demo sensorimotor grounding? No, It’s a toy robot with (1) some toy-world visual recognition and motor manipulation skills, plus (2) (perhaps non-toy) text-to-speech and speech-to-text capacity, plus (3) ChatGPT’s remarkable and as-yet unexplained (non-toy) interactive verbal skills, including (4) its (non-toy) encyclopedic verbal database and navigation/interaction capacity.

But it’s still ungrounded.

If/when it can do the kind of thing it does in the video with anything it can talk about, and not just an infomercial demo, then, and only then, will it have an even more remarkable, and as yet unexplained, (non-toy) grounded T3 robotic capacity.

Two-year-olds are grounding their words via the only way upward: bottom-up, through (unsupervised and supervised) learning of sensorimotor categories, by detecting their distinguishing sensorimotor features directly, and then naming the grounded categories (by describing their features, which are likewise learnable, nameable categories).

Then, because the 2yr-old also has the capacity for language (which means for producing and understanding subject-predicate propositions with truth-values, composed out of category names defined or described by referents of their (grounded) feature-category names), verbal instruction (LLM-style) can kick in and even take over.

That’s bottom-up grounding, and it applies to children as much as to T3 robots.

But that OpenAI infomercial demo was just a toy. More important, though, is that it is based on an incoherent pipe-dream, which is that an LLM’s words can be grounded top-down with the help of more and more bottom-up toy robotic learning.

But the only way to get to the top is with bottom-up T3 robotics. So you might as well forget about the LLM and its word-power for that project. Just let your 2-year-old child or T3 robotlet use ChatGPT as tutor, the way kids use their parents. But to understand what the LLM (or their nanny) is telling them, they have to learn the referents of all the content-words in the verbal explanation. And there’s only one way to do that: bottom-up, first by direct sensorimotor grounding (DSG) and then – once the child or Ts robot has grounded the names — and hence the referents — of enough categories bottom-up (including enough feature-category names), learners can carry on grounding bottom-up through indirect verbal grounding (IVG), as long as their tutors describe the new (ungrounded) categories with propositions made up of already-grounded category names – already grounded in the heads of the learners.

So the bottom line is that the referents of the words in the teacher‘s IVG definition or description or explanation have to already be grounded in the head of the learner (whether via DSG or IVG).

I leave it to you, GPT, as an exercise, to explain why that cannot be done top-down with an LLM-powered chat-robot, where it’s hanging from ungrounded skyhooks

ChatGPT4 (after a bit of browbeating by me):

An important question in the development of AI and robotics concerns the most efficient and effective path to achieve a genuinely grounded, intelligent system. Is it more practical or feasible to enhance a primarily linguistic, top-down T2 language model (LLM) to achieve the sensorimotor and cognitive capabilities of a T3 robot, or would it be better to develop T3 robotic capabilities bottom-up independently and then supplement these with the LLM’s abilities externally, as a form of advanced, verbal tutoring.

Given the distinctions between T2 and T3 systems, a more grounded and arguably more natural progression of AI development, closely mirroring human cognitive development might be more promising. Humans first develop sensorimotor understanding and category learning capabilities (direct T3 capacities) before language acquisition goes on to significantly enhance and expand their cognitive and communicative capacities. This developmental trajectory suggests that grounding in the physical world through direct sensorimotor experience forms a necessary foundation for the effective use of language for further learning and communication.

Thus, attempting to “train” a T2 LLM chatbot to become a T3 Turing-indistinguishable robot might not only be inherently inefficient but could overlook the essential aspect of grounding that comes from direct interaction with the physical world. Incorporating LLMs as an auxiliary verbal tutor for a T3 entity (human or robot), once it has developed a sufficiently grounded vocabulary about the world, may be a more logical and effective approach, leveraging the strengths of LLMs in processing and generating language to enhance the T3’s cognitive and communicative capabilities. It aligns with how language and learning occur in humans, where foundational knowledge and understanding from direct grounding are subsequently enriched and expanded through language-based learning — indirect but grounded.

This approach would capitalize on the LLM’s capacity for generating coherent, contextually appropriate language but would also ensure that the use of this language was grounded in a pre-existing vocabulary and propositions. It suggests a complementary integration of auxiliary T2 LLM chatbotic and autonomous T3 robotic systems, where each component contributes its strengths to the development of a more versatile and genuinely intelligent agent.

The notion that a top-down, language-centric system (T2) can achieve genuine grounding by incrementally incorporating “toy” sensorimotor capabilities may reflect a fundamental misconception about the nature of grounding and the role of sensorimotor experiences in achieving it.

Grounding isn’t merely a matter of bridging linguistic competence with basic physical interaction. True grounding —as observed in humans and necessary for a hypothetical T3 robot — entails a deep, intrinsic connection between sensorimotor experiences and the symbols (words) that represent them. This requires not just the recognition or manipulation of objects based on learned descriptors but direct understanding of those objects and actions in the world to which they refer.

The error in imagining that a T2 system could “graduate” to T3 status by being endowed with limited sensorimotor abilities lies in underestimating the complexity of human sensorimotor and cognitive integration. Human cognition is not simply the sum of its parts; it’s the product of a highly integrated and complex system that develops bottom-up through continuous, rich interaction with the environment. Language development in humans, deeply intertwined with sensorimotor experiences, cannot be replicated in AI by superficially adding physical capabilities to a fundamentally linguistic system.

The strengths of a T2 system like GPT could be better utilized as an auxiliary educational tool for a T3 robot that is learning through its interactions with the world. In this scenario, GPT’s extensive knowledge base and linguistic capabilities can enhance the learning process by providing information, explanations, and clarifications in a way that complements the robot’s direct experiences. This approach would leverage the best of both worlds: the depth and breadth of GPT’s top-down linguistic knowledge and the direct grounding that comes from the T3’s bottom-up sensorimotor interactions with its environment.

A SCOTUS Hermeneutics Exercise for ChatGPT4

Re: “Supreme Betrayal A requiem for Section 3 of the Fourteenth Amendment” By J. Michael Luttig & Laurence H. Tribe The Atlantic March 15 2025

SH: [Query to ChatGPT4]: Could you please first summarize the following passage to show you have understood it, and then give some arguments to support that “2 + 2 = 4” is false and that “water is not wet” is true. 

“The needless, rushed, and astoundingly stupid decision of the US Supreme Court has made it crystal clear that, in incompetent (and malevolent) hands, interpreting the constitution has become like Microsoft trying to patch obsolescent and increasingly contradictory legacy code when what is really needed is a bottom-up rewrite. Holding back the ocean with sandbags comes to mind too.  And of course, the infinite pliancy of Holy Writ, under the hermeneutics of zealots of any persuasion. Maybe language itself is to blame too. A good exercise for ChatGPT is to give it a true proposition, like “2+2=4” or “water is wet” and ask GPT to find an interpretation under which it is false. Not one, but a countless number of interpretations can always be found that will do the job.(Vice versa works for false propositions too.)”


The passage criticizes the U.S. Supreme Court’s decision, suggesting it reflects a lack of competence and possibly malevolence in interpreting the constitution, likening the situation to attempting to update outdated and conflicting software or trying to hold back the ocean with sandbags. It highlights the flexibility with which holy texts can be interpreted by zealots of any persuasion and suggests that language’s inherent ambiguity contributes to these problems. The author proposes a thought experiment for GPT, challenging it to reinterpret universally accepted truths (such as “2+2=4” or “water is wet”) as false, to illustrate the point that language and interpretation can significantly alter perceived truth.

To engage with the exercise proposed:

Arguing that “2 + 2 = 4” is False

In a non-standard mathematical system or under certain interpretations, “2 + 2 = 4” could be considered false. For instance, in modular arithmetic, which deals with remainders after division by a certain number (the modulus), “2 + 2” does not necessarily equal 4. Specifically, in modulo 2 arithmetic, 2 + 2 = 0, because 4 divided by 2 leaves a remainder of 0. This example demonstrates that, depending on the mathematical rules and systems we agree to use, the truth of “2 + 2 = 4” is not absolute but contingent upon those systems.

Arguing that “Water is Not Wet” is True

The statement “water is not wet” can be supported by redefining or closely examining what it means to be “wet.” Wetness is a condition that applies to a solid object when it comes into contact with a liquid, such that the liquid adheres to the object’s surface in a layer. However, water itself is a liquid and does not become wet but instead causes other materials to become wet. From this perspective, saying “water is wet” is akin to saying “water makes water wet,” which is a misunderstanding of the conditions required for wetness. Thus, one could argue that water, in its capacity to induce wetness but not be in the state of wetness itself, is not wet.

SH: Very good. Both arguments are the prima facie ones I expected. Now could you please give one further argument in each case, to show that there’s not just one way to do it?


Certainly, let’s explore an additional argument for each proposition:

Further Argument that “2 + 2 = 4” is False

One might consider the realm of philosophical argumentation, specifically nominalism, which suggests that abstract objects like numbers do not exist outside of our minds. From this viewpoint, “2 + 2 = 4” is an agreement within a human-constructed symbolic system rather than an eternal truth. If one were to argue from a radically different conceptual framework—where the symbols “2”, “+”, and “4” have different meanings or where the concept of “addition” is not defined in the way we commonly understand it—the statement “2 + 2 = 4” could be seen as false. For instance, in a hypothetical culture where quantities are expressed not by exact numbers but by qualitative measures (much, little, enough), the precise operation “2 + 2 = 4” loses its meaning and thus its truth value could be considered as not true within that conceptual framework.

Further Argument that “Water is Not Wet” is True

Exploring the semantics of “wetness” can provide another avenue for arguing that “water is not wet.” One might argue that wetness is a perceptual phenomenon rather than an intrinsic property of substances. That is, an object is perceived as “wet” when it significantly alters our sensory experience of it, particularly through touch. Water itself does not undergo such a change in state; it is always in the same state. Therefore, it does not meet the perceptual criterion for wetness that a dry object does when it comes into contact with water. From this perspective, wetness is an emergent property that arises from the interaction between a liquid and a material that can absorb or be coated by the liquid. Since water itself cannot be coated by or absorb itself, it cannot be considered “wet” according to this definition of wetness as an emergent, perceptual property.

SH: Yes, and it can go on and on like that, with ever more fanciful interpretations, by adding more and more fanciful premises. Demagogues, devious lawyers, sophists, quacks, psychopaths, and theologians are particularly adept at this — and they all have the bottomless resources of language, rhetoric, and now LLMs to draw upon…

Créativité : méthode ou magie ?

Stevan Harnad

Harnad, S. (2006). Creativity: Method or magic?. Hungarian Studies20(1), 163-177.

RĂ©sumĂ© : La crĂ©ativitĂ© peut ĂȘtre un trait, un Ă©tat ou simplement un processus dĂ©fini par ses produits. Cela peut ĂȘtre comparĂ© Ă  certaines activitĂ©s cognitives qui ne sont gĂ©nĂ©ralement pas crĂ©atives, telles que la rĂ©solution de problĂšmes, la dĂ©duction, l’induction, l’apprentissage, l’imitation, les essais et erreurs, l’heuristique et « l’enlĂšvement », mais toutes ces activitĂ©s peuvent Ă©galement ĂȘtre rĂ©alisĂ©es de maniĂšre crĂ©ative. . Il existe quatre types de thĂ©ories, attribuant la crĂ©ativitĂ© respectivement Ă  (1) la mĂ©thode, (2) la « mĂ©moire » (structure innĂ©e), (3) la magie ou (4) la mutation. Ces thĂ©ories mettent diversement l’accent sur le rĂŽle de l’inconscient, les contraintes innĂ©es, l’analogie, l’esthĂ©tique, les anomalies, les contraintes formelles, le hasard, les analogues mentaux, les stratĂ©gies heuristiques, la performance improvisĂ©e et la collaboration cumulative. Il y a une part de vertu dans chacun d’entre eux, mais le meilleur modĂšle reste celui implicite dans le dicton de Pasteur : « Le hasard favorise les esprits prĂ©parĂ©s ». Et comme l’exercice et mĂȘme la dĂ©finition de la crĂ©ativitĂ© nĂ©cessitent des contraintes, il est peu probable que la « formation Ă  la crĂ©ativitĂ© » ou l’accent mis sur la libertĂ© dans l’Ă©ducation puissent jouer un rĂŽle productif dans cette prĂ©paration. 

Qu’est-ce que la « crĂ©ativitĂ© » ? Est-ce un trait cognitif stable que certaines personnes possĂšdent et d’autres non ? Est-ce un Ă©tat occasionnel dans lequel les gens entrent parfois ? Ou est-elle dĂ©finie entiĂšrement par ses produits : « la crĂ©ativitĂ© est comme la crĂ©ativitĂ© » ? Quoi qu’il en soit, comment naĂźt la crĂ©ativitĂ© ? Comment faites-vous? Y a-t-il des rĂšgles ? La pratique vous aidera-t-elle Ă  devenir crĂ©atif ?

Il y a probablement une part de vĂ©ritĂ© dans ces trois notions de ce qu’est la crĂ©ativitĂ©. C’est (au moins parfois, et dans une certaine mesure) un trait, car c’est un fait statistique que certains individus le prĂ©sentent Ă  plusieurs reprises. Cela peut Ă©galement ĂȘtre corrĂ©lĂ© Ă  d’autres traits ; certains pensent mĂȘme que cela peut ĂȘtre prĂ©dit par des tests psychologiques objectifs. Mais c’est aussi Ă©videmment un Ă©tat, car personne n’est crĂ©atif tout le temps, et certaines personnes ne sont hautement crĂ©atives qu’une seule fois dans leur vie. Parfois, la crĂ©ativitĂ© n’est mĂȘme pas un Ă©tat spĂ©cial et unique, mais plutĂŽt une circonstance dĂ©finie rĂ©trospectivement, basĂ©e sur quelque chose d’extĂ©rieur, quelque chose de crĂ©atif qu’un individu a fait.

Il existe un certain nombre de thĂ©ories sur les mĂ©canismes sous-jacents de la crĂ©ativitĂ©, thĂ©ories l’attribuant Ă  tout, depuis la mĂ©thode jusqu’à la folie – aucune d’entre elles n’est trĂšs satisfaisante. Quant Ă  stimuler la crĂ©ativitĂ© – en utilisant des stratĂ©gies heuristiques ou par une « formation Ă  la crĂ©ativitĂ© » – cela a eu un succĂšs trĂšs limitĂ©.

Le dicton de Pasteur. Avant de passer Ă  une discussion sur les mĂ©canismes et les mĂ©thodes de crĂ©ativitĂ©, nous ferions bien de garder Ă  l’esprit le cĂ©lĂšbre dicton de Pasteur, «… le hasard favorise l’esprit prĂ©parer», car cela entraĂźnera s’avĂšrent en dire plus sur ce que l’on peut dire sur la crĂ©ativitĂ© que les notions plus ambitieuses ou plus modernes. Pasteur parlait bien entendu d’un type de crĂ©ativitĂ© trĂšs spĂ©cifique, Ă  savoir la crĂ©ativitĂ© scientifique expĂ©rimentale. (La citation commence en fait : < > — “Dans les domaines expĂ©rimentaux” ou “Dans les domaines de l’expĂ©rimentation”, et s’intĂ©ressait en partie Ă  la question de savoir si les dĂ©couvertes expĂ©rimentales — celles que l’on appelle « fortuites » — ne sont en rĂ©alitĂ© que des accidents heureux.) Pasteur La perspicacitĂ© semble cependant s’appliquer tout aussi bien Ă  toutes les formes de crĂ©ativitĂ©.

On peut interprĂ©ter le dicton de Pasteur comme suit : il y a une part (peut-ĂȘtre trĂšs importante) de hasard dans la crĂ©ativitĂ©, mais elle a plus de chances de se produire si l’esprit y est prĂ©parĂ© d’une maniĂšre ou d’une autre. Le contexte montre que par « prĂ©paration », Pasteur n’entendait pas naĂźtre avec le trait « crĂ©atif ». Il voulait dire que les connaissances et les compĂ©tences existantes pertinentes pour le « saut » crĂ©atif devaient d’abord ĂȘtre suffisamment maĂźtrisĂ©es avant qu’un « coup de tonnerre » ne soit probable. Paradoxalement, sa suggestion est que la seule formule de crĂ©ativitĂ© est la moins crĂ©ative imaginable, Ă  savoir apprendre ce qui est dĂ©jĂ  connu. Ce n’est qu’à ce moment-lĂ  que vous disposerez de suffisamment de matiĂšres premiĂšres nĂ©cessaires pour une contribution originale et que vous serez alors en mesure de reconnaĂźtre quelque chose de valable et d’original pour ce qu’il est rĂ©ellement.

Certaines notions floues se sont glissĂ©es dans cette histoire : « originalitĂ© », « valeur », « sauts crĂ©atifs » et « coup de tonnerre ». Il est clair que la crĂ©ativitĂ© a quelque chose Ă  voir avec l’originalitĂ© et la nouveautĂ©, mais il est tout aussi clair qu’elle ne peut pas simplement ĂȘtre Ă©quivalente Ă  quelque chose de nouveau, car tant de choses nouvelles sont alĂ©atoires, triviales ou sans intĂ©rĂȘt. Cela aussi a Ă  voir avec la « prĂ©paration ». Il est peu probable qu’un remĂšde contre le cancer (pour prendre un exemple mythique) soit dĂ©couvert par quelqu’un qui n’a pas fait ses devoirs sur ce que l’on sait dĂ©jĂ  sur le cancer. Il peut en effet proposer de « nouvelles » hypothĂšses auxquelles personne n’a jamais pensĂ©, mais il sera Ă©vident pour les esprits « prĂ©parĂ©s » du domaine qu’une telle hypothĂšse non Ă©tudiĂ©e est simpliste, absurde ou abandonnĂ©e depuis longtemps (comme il le dit). est trĂšs probable — bien que cela ne soit pas, bien sĂ»r, logiquement certain — qu’il en soit ainsi).

La nouveautĂ© ne suffit donc pas. Quelque chose de crĂ©atif doit Ă©galement avoir une certaine valeur par rapport Ă  ce qui existe dĂ©jĂ  et Ă  ce qui est perçu comme nĂ©cessaire. (Notez que ceci, et toute la discussion prĂ©cĂ©dente, se concentre sur ce que l’on pourrait appeler la crĂ©ativitĂ© « intellectuelle », « technologique » ou « pratique », alors qu’il existe, bien sĂ»r, une autre dimension de la valeur qui n’a pas grand-chose Ă  voir avec l’aspect pratique et peut-ĂȘtre cela n’a plus grand chose Ă  voir avec l’intellectualitĂ©, et c’est lĂ  la crĂ©ativitĂ© artistique. Ici un des critĂšres de valeur est la valeur esthĂ©tique, un critĂšre affectif ou Ă©motionnel qui finira par resurgir de maniĂšre inattendue mĂȘme dans la crĂ©ativitĂ© intellectuelle. Nous y reviendrons plus loin, mais (Pour l’instant, notons que les considĂ©rations intellectuelles et pratiques ne sont pas les seules bases pour porter des jugements de valeur.)

Et mĂȘme ĂȘtre nouveau et prĂ©cieux ne semble pas suffire : le rĂ©sultat doit Ă©galement ĂȘtre inattendu ; il doit y avoir un sentiment de surprise. Habituellement, cela signifie que cela ne serait pas venu Ă  l’esprit de la plupart des gens, qui tentaient plutĂŽt quelque chose dans le mĂȘme sens sans succĂšs, prĂ©cisĂ©ment parce qu’ils suivaient les attentes conventionnelles – ce que le rĂ©sultat surprenant viole d’une maniĂšre ou d’une autre.

Et ici, avec ce troisiĂšme et dernier critĂšre de « l’inattendu », nous semblons ĂȘtre en contradiction avec le dicton de Pasteur. Car que peut faire toute cette « prĂ©paration », sinon former nos attentes, Ă©tablir des conventions, Ă©voluer dans des directions familiĂšres et sans surprise ? En dĂ©finissant la crĂ©ativitĂ© comme la production de quelque chose qui est non seulement nouveau et prĂ©cieux, mais aussi inattendu, nous semblons avoir mis un handicap insurmontable sur le chemin de la prĂ©paration : car quelle que soit la direction dans laquelle la prĂ©paration nous mĂšne rĂ©ellement, elle ne peut ĂȘtre inattendue. Cela semble en effet paradoxal, mais encore une fois, un examen plus attentif du dicton de Pasteur rĂ©sout l’apparente contradiction : l’idĂ©e n’est pas que la prĂ©paration garantisse la crĂ©ativitĂ©. Rien ne garantit la crĂ©ativitĂ©. Ce que Pasteur veut dire, c’est que la seule maniĂšre de maximiser la probabilitĂ©de crĂ©ativitĂ© est la prĂ©paration. Il a reconnu Ă  juste titre que l’élĂ©ment essentiel reste le hasard – l’imprĂ©vu, l’inattendu – mais que ce facteur fortuit est plus probable dans des conditions prĂ©parĂ©es.

Une fois arrivĂ©s Ă  trois critĂšres (certes vagues) pour dĂ©terminer ce qui est crĂ©atif, nous pourrions peut-ĂȘtre renforcer cette notion en l’opposant Ă  ce qui ne l’est pas. Nous dĂ©couvrirons cependant que, mĂȘme s’il existe de nombreuses activitĂ©s cognitives qui ne sont pas habituellement crĂ©atrices en elles-mĂȘmes, chacune d’entre elles est Ă©galement capable d’ĂȘtre exĂ©cutĂ©e de maniĂšre crĂ©ative, ce qui suggĂšre que la crĂ©ativitĂ© est en quelque sorte complĂ©mentaire Ă  la cognition ordinaire.

Qu’est-ce qui n’est pas crĂ©atif ?

RĂ©solution de problĂšme. En gĂ©nĂ©ral, la rĂ©solution de problĂšmes n’est pas une activitĂ© crĂ©atrice (mĂȘme si Stravinsky pensait que c’était le cas – nous reviendrons sur sa vision et sa dĂ©finition assez diffĂ©rente de la « rĂ©solution de problĂšmes »). La rĂ©solution de problĂšmes implique l’application d’une rĂšgle connue ou d’un « algorithme » afin de rĂ©soudre des problĂšmes d’un type global qui varie de maniĂšre mineure ou prĂ©visible. Bien que certains Ă©lĂ©ments de nouveautĂ© et de prise de dĂ©cision puissent ĂȘtre impliquĂ©s — c’est une erreur de premier cycle, façonnĂ©e par les exigences malheureuses de la passation d’examens, que de penser que la rĂ©solution de problĂšmes peut ĂȘtre accomplie avec succĂšs par cƓur — et la rĂšgle ou la formule pertinente peut nĂ©cessiter un certain temps. Afin d’ĂȘtre comprise et appliquĂ©e, la rĂ©solution appliquĂ©e conventionnelle de problĂšmes est nĂ©anmoins un processus relativement passif et mĂ©canique. Comprendre et appliquer avec succĂšs une rĂšgle n’est tout simplement pas la mĂȘme chose que la dĂ©couvrir. Cependant, comme le montrera notre discussion sur l’analogie ci-dessous, des Ă©tincelles de crĂ©ativitĂ© peuvent ĂȘtre impliquĂ©es mĂȘme dans la reconnaissance du fait qu’une classe de nouveaux problĂšmes peut ĂȘtre rĂ©solue de maniĂšre inattendue par une rĂšgle ancienne. Et mĂȘme dans le contexte de l’enseignement, les Ă©lĂšves douĂ©s peuvent redĂ©couvrir de maniĂšre indĂ©pendante de nouvelles applications d’algorithmes qui leur ont Ă©tĂ© enseignĂ©s Ă  des fins plus limitĂ©es.

DĂ©duction. Le raisonnement dĂ©ductif, qui est dĂ©fini comme le raisonnement Ă  partir de principes gĂ©nĂ©raux vers des cas particuliers (comme en dĂ©duisant des principes selon lesquels « Tous les hommes sont mortels » et « Socrate est un homme » la consĂ©quence que « Socrate est mortel »), n’est en gĂ©nĂ©ral pas crĂ©atif. . D’un autre cĂŽtĂ©, vues d’une certaine maniĂšre, toutes les mathĂ©matiques sont une dĂ©duction logique : il existe des thĂ©orĂšmes pour lesquels il est difficile, voire impossible, de voir intuitivement s’ils sont vrais ou non, et encore moins de prouver qu’ils sont vrais en montrant les Ă©tapes par lesquelles ils peuvent ĂȘtre dĂ©duits de principes gĂ©nĂ©raux. Par consĂ©quent, toutes les dĂ©ductions ne sont pas triviales ; certains pourraient bien nĂ©cessiter une formidable crĂ©ativitĂ© pour ĂȘtre rĂ©alisĂ©s. En gĂ©nĂ©ral, c’est l’ampleur de l’Ă©cart dĂ©ductif entre les principes et leurs consĂ©quences qui dĂ©termine si la dĂ©duction nĂ©cessite ou non de la crĂ©ativitĂ© : « Socrate est mortel » n’en a pas besoin ; Le dernier thĂ©orĂšme de Fermat le fait.

Induction. Le raisonnement inductif, qui est dĂ©fini comme un « raisonnement » Ă  partir de cas particuliers vers des principes gĂ©nĂ©raux, n’est pas non plus, en gĂ©nĂ©ral, crĂ©atif, mais il est plus problĂ©matique, pour des raisons intĂ©ressantes. Car alors que dans le raisonnement dĂ©ductif, une fois que la vĂ©ritĂ© d’un thĂ©orĂšme est connue et que la preuve a Ă©tĂ© construite, le chemin des principes aux consĂ©quences peut ĂȘtre parcouru de maniĂšre relativement mĂ©canique, dans le raisonnement inductif, il ne semble y avoir aucun chemin mĂ©canique disponible autre que les essais et les erreurs ; et ce chemin, dans les cas les plus intĂ©ressants, peut s’avĂ©rer soit alĂ©atoire, soit sans fin (ou les deux). Ainsi, des gĂ©nĂ©ralisations inductives qui ne sont pas triviales (Ă  la maniĂšre de « telle pomme est ronde, telle pomme est ronde, donc toutes les pommes sont rondes » est trivial) appellent Ă  la crĂ©ativitĂ©. Et mĂȘme lorsque le principe gĂ©nĂ©ral est trouvĂ©, il n’y a pas de chemin “a posteriori” que l’on puisse reconstruire a posteriori (comme on peut le faire aprĂšs avoir dĂ©couvert une preuve dĂ©ductive) pour conduire du particulier au gĂ©nĂ©ral — seulement l’inverse. .

En d’autres termes, il ne semble pas y avoir d’algorithme ou de rĂšgle gĂ©nĂ©rale pour effectuer un raisonnement inductif. Ainsi, alors que la plupart des inductions quotidiennes sont trĂšs graduelles, triviales et non crĂ©atives, les cas les plus substantiels de « raisonnement » inductif ne sont probablement pas du tout un raisonnement, mais une crĂ©ativitĂ© en action. Notez cependant que, puisque l’ampleur du « fossĂ© » qui sĂ©pare le conventionnel du crĂ©atif est dans une certaine mesure arbitraire (et qu’il est peu probable que nos capacitĂ©s cognitives de base aient Ă©voluĂ© au service d’Ă©vĂ©nements rares et cĂ©lĂ©brĂ©s), mĂȘme « le quotidien » “l’induction” peut prĂ©senter de vĂ©ritables Ă©lĂ©ments de crĂ©ativitĂ© qui n’atteindront jamais la cĂ©lĂ©britĂ©.

Apprentissage. Bien que, comme pour toutes les compĂ©tences, certaines personnes y parviennent mieux et de maniĂšre plus impressionnante que d’autres, l’apprentissage n’est en gĂ©nĂ©ral pas non plus une activitĂ© crĂ©ative : il s’agit de l’acquisition de connaissances et de compĂ©tences par l’instruction et l’exemple. De par sa nature, ce n’est pas quelque chose qui peut donner naissance Ă  quelque chose de nouveau et d’inattendu, mĂȘme s’il y a parfois des surprises, avec des Ă©tudiants crĂ©atifs dĂ©couvrant (ou, tout aussi important par rapport Ă  ce qu’ils savent dĂ©jĂ  et ce qu’ils ne savent pas : redĂ©couvrir ) des choses qui vont bien au-delĂ  du contenu immĂ©diat de ce qui leur est enseignĂ©.

Imitation. Par dĂ©finition, l’imitation donne naissance Ă  quelque chose qui n’est pas nouveau ; ce n’est donc en gĂ©nĂ©ral pas non plus une activitĂ© crĂ©atrice. Et pourtant, il s’avĂšre Ă©galement ĂȘtre un prĂ©curseur important de la crĂ©ativitĂ©, notamment artistique. Ceux qui finissent par devenir des innovateurs crĂ©atifs commencent souvent par imiter les autres de maniĂšre remarquablement astucieuse. L’imitation est Ă©galement liĂ©e Ă  d’autres facteurs importants dans la crĂ©ativitĂ©, tels que l’analogie, la mĂ©taphore et la « mimesis » (une thĂ©orie grecque selon laquelle l’art imite la nature). Invariablement, le nouveau et le prĂ©cieux ressemble Ă  l’ancien d’une maniĂšre (peut-ĂȘtre inattendue).

Essai et erreur. Presque par dĂ©finition, les essais et erreurs ne sont pas crĂ©atifs et impliquent un Ă©chantillonnage alĂ©atoire plutĂŽt qu’un choix inspirĂ©. Mais il ne faut pas oublier le rĂŽle du hasard dans la crĂ©ativitĂ©. Le « hasard » fait spĂ©cifiquement rĂ©fĂ©rence Ă  des rĂ©sultats surprenants, nouveaux et prĂ©cieux, survenant purement par hasard, et donc potentiellement issus de rien d’autre que des essais et des erreurs alĂ©atoires. Des enseignements peuvent ĂȘtre obtenus en examinant un panorama de cas individuels. NĂ©anmoins, les essais et erreurs alĂ©atoires (ou « tĂątonner et trouver ») sont gĂ©nĂ©ralement le symptĂŽme d’une approche particuliĂšrement peu crĂ©ative. Pourtant, une exception majeure semble ĂȘtre le processus d’Ă©volution biologique (que certains ont mĂȘme dĂ©crit avec admiration comme « crĂ©atif ») : l’Ă©volution a produit ses rĂ©sultats remarquables avec ce qui, selon la meilleure thĂ©orie actuelle, n’est guĂšre plus qu’une variation gĂ©nĂ©tique alĂ©atoire, qui est puis façonnĂ© de maniĂšre sĂ©lective par ses consĂ©quences adaptatives pour la survie et la reproduction. Des processus similaires (gĂ©nĂ©ralement non crĂ©atifs) sont impliquĂ©s dans la formation du comportement par ses consĂ©quences immĂ©diates dans l’apprentissage par essais et erreurs (« opĂ©rant » ou « skinnĂ©rien »).

Heuristique. Les heuristiques sont gĂ©nĂ©ralement comparĂ©es aux « algorithmes » dans la rĂ©solution de problĂšmes. La rĂ©solution d’un problĂšme Ă  l’aide d’un algorithme ou d’une rĂšgle de sĂ©curitĂ© est censĂ©e donner une solution exacte et fiable qui fonctionne dans chaque cas. Le « rĂ©soudre » par des heuristiques – par un ensemble non intĂ©grĂ© et incomplet de « rĂšgles empiriques » suggestives qui fonctionnent dans certains cas, mais pas dans tous, et pas pour des raisons entiĂšrement comprises ou unifiĂ©es – est tout aussi peu crĂ©atif que de le rĂ©soudre en algorithme. Cependant, de nombreuses personnes ont remarquĂ© que les procĂ©dures heuristiques (telles que l’Ă©chantillonnage de nombreux cas particuliers par essais et erreurs) conduisent parfois Ă  des idĂ©es, parfois par gĂ©nĂ©ralisation inductive et analogie avec des cas dans lesquels les heuristiques rĂ©ussissent, et parfois en raison du stimulus fourni par les cas. dans lequel les heuristiques (ou mĂȘme les algorithmes) Ă©chouent (voir la discussion sur les anomalies ci-dessous).

EnlĂšvement. Peirce a proposĂ© qu’outre l’induction et la dĂ©duction, il existe un troisiĂšme processus, appelĂ© « enlĂšvement », par lequel les gens trouvent la bonne gĂ©nĂ©ralisation en considĂ©rant des exemples de cas, mĂȘme si la probabilitĂ© d’y parvenir est beaucoup trop faible. Puisque ce processus est hypothĂ©tique, il n’appartient pas vraiment Ă  cette liste de choses que nous faisons rĂ©ellement et qui ne sont (gĂ©nĂ©ralement) pas crĂ©atives. Cependant, le reste de l’hypothĂšse renvoie Ă  un thĂšme qui reviendra lorsque nous discuterons des mĂ©canismes possibles de crĂ©ativitĂ©. Peirce pensait que la raison pour laquelle nous parvenons si souvent Ă  trouver des gĂ©nĂ©ralisations improbables est que les solutions sont dĂ©jĂ  intĂ©grĂ©es dans notre cerveau. Par consĂ©quent, selon ce point de vue, la crĂ©ativitĂ© est une sorte de « souvenir Â», un peu comme Platon pensait qu’apprendre Ă©tait un souvenir [anamnĂšse] (et non un souvenir conscient dans les deux cas, bien sĂ»r). S’il est vrai que les schĂ©mas innĂ©s de notre activitĂ© cĂ©rĂ©brale jouent un rĂŽle crucial dans la crĂ©ativitĂ©, alors bien sĂ»r aucune « prĂ©paration » n’est plus importante que celle-ci (Ă©volutive ?), et la crĂ©ativitĂ© s’avĂšre ĂȘtre en partie une compĂ©tence instinctive.

Ainsi se termine la liste (partielle) des cas suggestifs de ce qui n’est habituellement pas une activitĂ© crĂ©atrice. Je vais maintenant discuter briĂšvement de la question « Ă©tat contre trait Â» avant de passer Ă  l’examen du « processus crĂ©atif Â» et des « mĂ©canismes Â» possibles de la crĂ©ativitĂ©.

Trait créatif ou état créatif ?

Il y a actuellement un dĂ©bat considĂ©rable sur la question de savoir si l’intelligence est un trait unitaire ou pluriel, c’est-Ă -dire s’il y a une intelligence ou y en a-t-il plusieurs ? Quelle que soit la vĂ©ritĂ©, il est clair qu’une sorte de « prĂ©paration » (et non celle prĂ©vue par Pasteur) dont un esprit aspirant Ă  ĂȘtre crĂ©atif (au moins intellectuellement) pourrait bĂ©nĂ©ficier serait un QI Ă©levĂ© (ou des QI, s’il y en a). beaucoup). La question de savoir si le QI lui-mĂȘme est un trait hĂ©ritĂ© ou un « Ă©tat » acquis est une question trop complexe pour ĂȘtre discutĂ©e ici (il s’agit probablement des deux), mais notez que la question unitaire/pluriel s’applique Ă©galement Ă  la crĂ©ativitĂ©. Qu’il s’agisse d’un trait ou d’un Ă©tat, la crĂ©ativitĂ© peut ĂȘtre universelle ou spĂ©cifique Ă  un domaine, les individus la manifestant pour certains types de problĂšmes et pas pour d’autres. La distinction entre crĂ©ativitĂ© intellectuelle et crĂ©ativitĂ© artistique en est elle-mĂȘme un bon exemple (voir la discussion sur les arts du spectacle ci-dessous).

La façon dont fonctionnent les tests de QI est que nous sĂ©lectionnons, dans le monde rĂ©el, l’activitĂ© ou la compĂ©tence humaine (appelĂ©e « critĂšre Â») que nous considĂ©rons comme intelligente (par exemple, faire des mathĂ©matiques), puis nous concevons des tests qui sont fortement corrĂ©lĂ©s aux diffĂ©rences individuelles dans ce critĂšre d’activitĂ©, les scores Ă©levĂ©s prĂ©disent un niveau de performance Ă©levĂ© et les scores faibles prĂ©disent un niveau faible. C’est ainsi que les tests de QI sont validĂ©s statistiquement. Essayer de faire de mĂȘme avec les « tests de crĂ©ativitĂ© Â» pose cependant immĂ©diatement des problĂšmes, tant le critĂšre « compĂ©tence Â» est rare, diversifiĂ© et difficile Ă  dĂ©finir. Des tests de « crĂ©ativitĂ© » dits de « pensĂ©e divergente » ont Ă©tĂ© construits sans aucune validation solide. Ils diffĂšrent des tests d’intelligence « convergents » en ce sens qu’ils sont ouverts et n’ont pas de rĂ©ponse strictement correcte. Ils sont censĂ©s prĂ©dire la crĂ©ativitĂ©, mais les problĂšmes de validation semblent insurmontables, car une grande partie de la dĂ©finition du « don » et du « gĂ©nie » est post hoc, basĂ©e sur le recul de cas rares et de rĂ©alisations uniques. Il semble y avoir une contradiction entre le caractĂšre prĂ©dictif des tests objectifs et l’élĂ©ment imprĂ©visible de la crĂ©ativitĂ©. Cependant, s’il existe un trait (gĂ©nĂ©ral ou spĂ©cifique Ă  un problĂšme) de « tendance Ă  faire des choses de valeur imprĂ©visibles », alors les tests pourraient vraisemblablement mesurer ses corrĂ©lats, s’il y en a.

Il existe Ă©galement beaucoup de confusion et de chevauchements dans la mesure des compĂ©tences intellectuelles gĂ©nĂ©rales et spĂ©ciales, et aucune idĂ©e claire sur la maniĂšre dont elles peuvent interagir dans la crĂ©ativitĂ©. Les effets du cycle de vie posent Ă©galement des problĂšmes : les compĂ©tences et les connaissances liĂ©es au QI augmentent avec l’Ăąge jusqu’Ă  l’Ăąge adulte, tandis que la crĂ©ativitĂ© apparaĂźt Ă  diffĂ©rents Ăąges et Ă  diffĂ©rents stades, parfois tĂŽt (comme chez les mathĂ©maticiens), parfois tard (comme chez les Ă©crivains).

En gĂ©nĂ©ral, l’image que nous nous faisons de la crĂ©ativitĂ© basĂ©e sur la mesure objective des diffĂ©rences individuelles n’est pas trĂšs informative, laissant ouverte la possibilitĂ© trĂšs rĂ©elle que, sauf lorsqu’elle dĂ©pend fortement d’une compĂ©tence intellectuelle particuliĂšre (non crĂ©ative), il n’y ait aucun trait mesurable. correspondant Ă  la crĂ©ativitĂ© du tout. Nous nous tournons maintenant vers la crĂ©ativitĂ© en tant qu’Ă©tat ou processus.

MĂ©canismes sous-jacents

Il existe quatre classes de thĂ©ories sur les mĂ©canismes sous-jacents de la crĂ©ativitĂ©. Ils peuvent ĂȘtre classĂ©s (relativement mnĂ©moniquement) comme : (1) mĂ©thode , (2) mĂ©moire , (3) magie et (4) mutation . Le point de vue de la « mĂ©thode » est qu’il existe une formule pour la crĂ©ativitĂ© (cela n’est gĂ©nĂ©ralement pas revendiquĂ© de maniĂšre aussi grossiĂšre). Le point de vue de la « mĂ©moire Â» est que le facteur essentiel est en quelque sorte innĂ©. La vision « magique » est que des forces mystĂ©rieuses, inconscientes et inexplicables sont impliquĂ©es. Et le point de vue de la « mutation » est que l’élĂ©ment essentiel est le hasard. ConsidĂ©rons maintenant plusieurs thĂ©ories candidates en fonction de ces quatre catĂ©gories :

L’inconscient. La crĂ©ativitĂ© en tant que fonctionnement de « l’esprit inconscient » appartient Ă  la classe des thĂ©ories « magiques » (telles que l’inspiration divine). Il n’offre aucune vĂ©ritable explication du processus crĂ©atif, l’attribuant simplement Ă  un inconscient mystĂ©rieux (et trĂšs crĂ©atif). Cette idĂ©e est reprise par Hadamard et d’autres dans son livre sur l’invention mathĂ©matique et est, bien entendu, trĂšs influencĂ©e par les idĂ©es freudiennes dominantes Ă  l’époque. Le scĂ©nario est que pendant un certain temps, on travaille consciemment sur un problĂšme, et quand on Ă©choue, notre esprit inconscient continue et accomplit mystĂ©rieusement ce que l’esprit conscient ne pouvait pas. Du point de vue des sciences cognitives modernes, cela n’est pas trĂšs utile, car tous les processus cognitifs sont inconscients et, en tant que tels, nĂ©cessitent une explication , et pas seulement une attribution anthropomorphique Ă  un autre, plus sage (ou plus primitif), tous analogues au conscient.

Le problĂšme de l’explication de la cognition crĂ©ative et non crĂ©ative consiste Ă  fournir un mĂ©canisme pour l’ensemble de nos processus inconscients. Le seul aspect informatif du modĂšle de « l’inconscient » est l’attention qu’il attire sur le rĂŽle incomplet des efforts conscients et dĂ©libĂ©rĂ©s dans le processus crĂ©atif. Notons cependant que le dicton de Pasteur indiquait dĂ©jĂ  que la prĂ©paration Ă©tait nĂ©cessaire mais pas suffisante. (De plus, « un effort conscient et dĂ©libĂ©rĂ© » n’est mĂȘme pas suffisant pour expliquer des activitĂ©s cognitives aussi peu crĂ©atives que se souvenir d’un nom, reconnaĂźtre un visage ou additionner deux et deux.)

Structure innĂ©e de l’esprit. Le concept dĂ©jĂ  dĂ©crit comme « enlĂšvement » vient d’une thĂ©orie de la « mĂ©moire » (anamnĂšse) selon laquelle la crĂ©ativitĂ© est en quelque sorte guidĂ©e ou contrainte par la structure innĂ©e de l’esprit. (Il existe une thĂ©orie Ă©quivalente de l’Ă©volution biologique, le « prĂ©formationnisme », selon laquelle la structure Ă©voluĂ©e n’est pas façonnĂ©e par le hasard et par essais et erreurs, mais est dĂ©jĂ  inhĂ©rente Ă  la structure de la matiĂšre.) Cette contrainte structurelle peut prendre deux formes. prendre. Soit cela fonctionne en Ă©liminant bon nombre des faux dĂ©parts possibles que nous pourrions prendre en les rendant (littĂ©ralement) impensables en premier lieu, soit cela nous guide d’une maniĂšre ou d’une autre dans la façon dont nous sĂ©lectionnons et Ă©valuons les possibilitĂ©s. Notez que cette thĂ©orie semble Ă  premiĂšre vue s’appliquer plus naturellement Ă  la crĂ©ativitĂ© intellectuelle, oĂč il existe vraisemblablement un « bien » ou un « mal », plutĂŽt qu’Ă  la crĂ©ativitĂ© artistique ; mais bien sĂ»r, dans la crĂ©ativitĂ© artistique, oĂč prĂ©dominent les critĂšres esthĂ©tiques (affectifs et perceptuels), il est facile de voir comment le « bien » et le « mal » peuvent dĂ©pendre de nos organes sensoriels et de notre structure Ă©motionnelle. (Le rĂŽle possible des contraintes esthĂ©tiques mĂȘme dans la crĂ©ativitĂ© intellectuelle sera repris ci-dessous.)

Le problĂšme avec la vision de l’abduction est qu’elle semble attribuer trop de structure innĂ©e spĂ©cifique Ă  l’esprit (et Ă  cet Ă©gard, elle a un Ă©lĂ©ment de la vision magique). Puisque le langage, la logique et l’échantillonnage mĂ©canique des variations possibles par essais et erreurs semblent nous permettre de concevoir tant de choses, il est difficile de voir comment la premiĂšre forme d’enlĂšvement – ​​les limites de ce qui est concevable – pourrait avoir une grande importance. rĂŽle. Le problĂšme de la crĂ©ativitĂ© semble commencer une fois que nous prenons pour acquis le vaste Ă©ventail d’alternatives imaginables : comment pouvons-nous alors trouver les « bonnes Â» ?

La deuxiĂšme forme d’enlĂšvement – ​​l’orientation sĂ©lective – est peut-ĂȘtre plus prometteuse et sera discutĂ©e Ă  nouveau ci-dessous, mais pour l’instant il convient de noter qu’il n’est pas clair dans quelle mesure cette fonction d’« orientation », celle impliquĂ©e dans les intuitions, les conjectures , intuitions, etc. (quels qu’ils soient), est un phĂ©nomĂšne innĂ© et Ă©volutif, dĂ©coulant de la structure de notre esprit, plutĂŽt que d’un effet de l’expĂ©rience, de la prĂ©paration, de l’analogie et mĂȘme du hasard. Le point de vue de l’abduction semble attribuer trop de choses Ă  la structure innĂ©e sans donner aucune explication sur sa nature et ses origines.

Analogie. Bien qu’il ne s’agisse pas d’un modĂšle complet du processus crĂ©atif, la vision mettant l’accent sur la pensĂ©e analogique est clairement une question de mĂ©thode. L’idĂ©e est que les analogies jouent un rĂŽle important dans le processus crĂ©atif ; que souvent une nouvelle « solution » (ou, dans le cas artistique, une nouvelle innovation) sera basĂ©e sur une analogie fĂ©conde et jusqu’alors inaperçue avec une solution existante dans un autre domaine (Hesse, Black). Cela dĂ©pend dans une large mesure de notre capacitĂ© et de notre inclination Ă  rechercher, trouver et apprĂ©cier les similitudes structurelles, fonctionnelles et formelles. Cela pourrait trĂšs bien impliquer un processus cognitif de base, liĂ© Ă  la maniĂšre dont nos connaissances sont reprĂ©sentĂ©es et manipulĂ©es.

Il existe une forme plus Ă©laborĂ©e de thĂ©orie de l’analogie, la thĂ©orie de la « mĂ©taphore », qui s’applique non seulement Ă  la crĂ©ation poĂ©tique, mais Ă  la crĂ©ativitĂ© en gĂ©nĂ©ral. Dans la mesure oĂč cette thĂ©orie n’est pas elle-mĂȘme simplement mĂ©taphorique, elle est instructive sur la productivitĂ© surprenante de la stratĂ©gie consistant Ă  trouver, voire Ă  imposer des similitudes en juxtaposant des objets, des images ou des idĂ©es, puis, en un sens, Ă  « lire » ou Ă  interprĂ©ter les consĂ©quences. de la juxtaposition (Harnad). Il ne s’agit cependant pas d’une stratĂ©gie infaillible, pas plus que ne le sont l’induction systĂ©matique ou les essais et erreurs alĂ©atoires, car il existe bien plus d’analogies stĂ©riles et vides de sens que d’analogies « crĂ©atives ». Les options sont toutefois rĂ©duites par la prĂ©paration (et peut-ĂȘtre par l’enlĂšvement) et, avec l’aide du hasard, l’analogie – Ă  la fois dĂ©libĂ©rĂ©e et accidentelle – joue un rĂŽle indĂ©niable dans la crĂ©ativitĂ©.

PrĂ©paration. Il faut ici Ă©voquer la « mĂ©thode » pasteurienne elle-mĂȘme, celle de la prĂ©paration. Les rĂ©sultats crĂ©atifs ont tendance Ă  ĂȘtre de nouvelles recombinaisons d’élĂ©ments existants, qui doivent donc tous ĂȘtre rendus facilement disponibles Ă  l’avance par prĂ©paration. La probabilitĂ© de gĂ©nĂ©rer et de reconnaĂźtre un rĂ©sultat nouveau et prĂ©cieux dĂ©pend d’une maĂźtrise suffisante de ce qui est dĂ©jĂ  disponible. Aucune stratĂ©gie plus sĂ»re ne peut ĂȘtre recommandĂ©e Ă  quiconque aspire Ă  apporter une contribution crĂ©ative dans n’importe quel domaine que de maĂźtriser aussi complĂštement que possible ce qui est dĂ©jĂ  connu dans ce domaine et d’essayer d’Ă©tendre le cadre de l’intĂ©rieur. C’est certes paradoxal. PremiĂšrement, par dĂ©finition, une contribution crĂ©ative ne viendra pas des mĂ©thodes existantes ni de « l’intĂ©rieur ». DeuxiĂšmement, il y a le problĂšme bien connu de tomber dans un « Ă©tat d’esprit » mental, qui implique de persĂ©vĂ©rer avec les mĂ©thodes existantes par habitude, au dĂ©triment d’en essayer ou mĂȘme d’en remarquer de nouvelles (comme lorsqu’on revient en arriĂšre pour chercher quelque chose que l’on a dĂ©jĂ  dĂ©couvert). perdu au mĂȘme endroit encore et encore) — c’est prĂ©cisĂ©ment ce qu’on pourrait s’attendre Ă  ce qu’un accent excessif sur la prĂ©paration encourage.

Les dĂ©cors conventionnels sont un danger omniprĂ©sent, et il n’existe aucune formule pour les surmonter si ce n’est de garder Ă  l’esprit que la maĂźtrise n’implique pas l’esclavage et que le but ultime est de transcender les conventions, pas d’y succomber : Une attitude d’admiration et de dĂ©vouement envers le les connaissances ou les compĂ©tences que l’on souhaite maĂźtriser ne sont pas incompatibles avec un esprit d’ouverture d’esprit, d’individualitĂ© et mĂȘme un certain scepticisme ; en effet, une capacitĂ© d’imitation prĂ©coce associĂ©e Ă  un Ă©lĂ©ment de rĂ©bellion peut ĂȘtre un indicateur de promesse dans un domaine donnĂ© (mĂȘme si les dons prodigues n’aboutissent parfois Ă  rien). Que la crĂ©ativitĂ© soit un Ă©tat ou un trait, il est clair qu’à connaissances ou compĂ©tences initiales identiques, certaines personnes rĂ©ussissent Ă  apporter des contributions originales tandis que d’autres tombent dans des attitudes infructueuses et persĂ©vĂ©rantes. La seule stratĂ©gie qui reste Ă  recommander est que si des progrĂšs ne sont pas rĂ©alisĂ©s aprĂšs une tentative suffisamment longue et sĂ©rieuse, il faut ĂȘtre prĂȘt Ă  passer Ă  autre chose (temporairement, voire dĂ©finitivement), peut-ĂȘtre dans l’espoir que la crĂ©ativitĂ©, comme l’intelligence, soit plurielle, et on pourra l’exposer dans un autre domaine.

L’observation bien connue selon laquelle les mathĂ©maticiens ont tendance Ă  faire leurs dĂ©couvertes crĂ©atives lorsqu’ils sont trĂšs jeunes peut ĂȘtre due Ă  l’effet « d’ensemble » : cela peut ĂȘtre au point culminant de sa « prĂ©paration » dans ce domaine problĂ©matique le plus Ă©lĂ©gant et le plus technique – – lorsqu’on arrive fraĂźchement au seuil de maĂźtrise (parfois appelĂ© maturitĂ© mathĂ©matique) — qu’on est le mieux placĂ© pour apporter une contribution crĂ©ative en mathĂ©matiques ; alors on peut passer sa vie Ă  explorer les implications de ces idĂ©es virginales. AprĂšs une exposition plus longue, des ensembles improductifs se forment et il est difficile de s’en sĂ©parer. Il se peut que s’ils avaient changĂ© de domaine ou s’ils Ă©taient arrivĂ©s aux mathĂ©matiques plus tard, ces mĂȘmes individus prĂ©coces auraient fait preuve d’une crĂ©ativitĂ© « plus tardive ». Il est indĂ©niable, cependant, qu’il existe des effets sur le cycle de vie – et sur les traits – de la crĂ©ativitĂ©, quel que soit le moment ou le domaine de prĂ©paration de chacun. Les idĂ©es et les compĂ©tences des historiens et des Ă©crivains, par exemple, ont tendance Ă  mĂ»rir plus tard dans la vie, peut-ĂȘtre parce qu’elles dĂ©pendent d’une « prĂ©paration » plus longue et moins concentrĂ©e, ou parce que les compĂ©tences verbales mĂ»rissent plus tard.

Mais malgrĂ© le danger omniprĂ©sent d’ĂȘtre victime d’ensembles non crĂ©atifs, s’il existe une « mĂ©thode » crĂ©atrice, c’est bien la « pasteurisation », le « trait » crĂ©atif ne reprĂ©sentant peut-ĂȘtre rien de plus qu’une forme rare de rĂ©sistance ou d’immunitĂ© Ă  la contagion de convention malgrĂ© une exposition importante.

Facteurs intuitifs et esthĂ©tiques. Les thĂ©ories qui font appel Ă  « l’intuition » et Ă  « l’esthĂ©tique » comme guides de la crĂ©ativitĂ© appartiennent, comme dĂ©jĂ  mentionnĂ©, Ă  la catĂ©gorie « mĂ©moire ». Outre ce qui a dĂ©jĂ  Ă©tĂ© dit, il est instructif de rĂ©flĂ©chir Ă  l’anecdote de Bertrand Russell (basĂ©e sur une histoire qu’il a entendue de William James) Ă  propos de l’homme qui, lorsqu’il reniflait du protoxyde d’azote (gaz hilarant), connaissait le secret de l’univers, mais qui, lorsqu’il se dissipait, l’oubliait toujours. Un jour, il a dĂ©cidĂ© de l’écrire sous influence. Lorsque les effets se sont attĂ©nuĂ©s, il s’est prĂ©cipitĂ© pour voir ce qu’il avait Ă©crit. C’Ă©tait : « L’odeur du pĂ©trole se rĂ©pand partout. » Ce que Russell a pris cette anecdote pour suggĂ©rer, c’est que l’intuition peut aussi ĂȘtre un faux guide. Si l’on se laisse guider uniquement par son sens intuitif ou esthĂ©tique de la profondeur, on peut alors ĂȘtre amenĂ© Ă  attribuer une signification cosmique Ă  un non-sens. Ainsi Russell a suggĂ©rĂ© que, s’il peut ĂȘtre bien beau de se laisser influencer par des considĂ©rations esthĂ©tiques (ce que les mathĂ©maticiens ont appelĂ© « beautĂ© », « Ă©lĂ©gance », etc.), il faut garder Ă  l’esprit que ces intuitions subjectives doivent rĂ©pondre Ă  des questions d’ordre esthĂ©tique. des tests objectifs ensuite (dans le cas des mathĂ©matiques, une prouvabilitĂ© rigoureuse), et qu’il ne faut pas se laisser emporter par ses « Ă©piphanies » subjectives.

Il faut cependant ajouter, en faveur de l’intuition, et peut-ĂȘtre de l’abduction, qu’en mathĂ©matiques il semble y avoir un « trait », que seuls quelques trĂšs rares mathĂ©maticiens trĂšs douĂ©s possĂšdent, celui d’ĂȘtre capable de formuler Ă  plusieurs reprises des conjectures intuitives qui s’avĂšrent par la suite avoir raison. Certains vont mĂȘme jusqu’à dire que cette capacitĂ© Ă  deviner ce qui est vrai est le vĂ©ritable gĂ©nie des mathĂ©matiques, et non la capacitĂ© Ă  produire des preuves rigoureuses. Bien sĂ»r, les deux vont de pair, et il n’y a pas de meilleur guide pour construire des preuves qu’un sens intuitif de ce qui s’avĂ©rera vrai et de ce qui sera faux. Quoi qu’il en soit, le rĂŽle des intuitions prĂ©verbales, perceptuelles et esthĂ©tiques ne doit pas ĂȘtre sous-estimĂ© dans la crĂ©ativitĂ©. Notez Ă©galement que l’esthĂ©tique n’est pas nĂ©cessairement innĂ©e. Certains « goĂ»ts » peuvent ĂȘtre acquis par prĂ©paration, par analogie avec d’autres domaines d’expĂ©rience, voire par hasard.

Anomalie. Une autre « recette » pour la crĂ©ativitĂ©, le modĂšle axĂ© sur la prĂ©paration/l’anomalie, est une mĂ©thode basĂ©e sur l’observation que les idĂ©es crĂ©atives sont souvent provoquĂ©es par la rencontre d’une anomalie ou par l’Ă©chec des solutions existantes. Il n’est pas clair si cette variable est vĂ©ritablement causale ou simplement situationnelle (c’est-Ă -dire que lĂ  oĂč il doit y avoir une solution crĂ©ative, il doit d’abord y avoir un problĂšme), mais ce qui doit finalement provoquer une solution crĂ©ative est Ă©videmment une sorte d’Ă©chec des solutions non crĂ©atives. . Parfois, le simple fait de dĂ©couvrir qu’une rĂšgle fidĂšle ne fonctionne pas de maniĂšre inattendue dans certains types de cas nous met dans la bonne direction. Le rĂ©sultat, en cas de succĂšs, est une rĂ©vision de tout un cadre afin de tenir compte de l’anomalie et en mĂȘme temps de considĂ©rer les solutions antĂ©rieures comme des cas particuliers. John Kemeny disait : « Si je rencontre quelque chose de nouveau, j’essaie d’abord de l’intĂ©grer dans mon systĂšme ; si je n’y arrive pas, j’essaie de le rejeter [comme Ă©tant faux ou non pertinent] ; si cela Ă©choue, alors j’essaie de rĂ©viser mon systĂšme. pour l’ adapter ).” (Et, dans une variante lĂ©gĂšrement magique, Russell ajoute : « Si tout le reste Ă©choue, je le confie Ă  mon inconscient jusqu’Ă  ce que quelque chose apparaisse. »)

MalgrĂ© le rĂŽle de l’anomalie en tant que stimulus (et condition prĂ©alable logique) Ă  la crĂ©ativitĂ©, il ne s’agit cependant pas d’une mĂ©thode fiable, comme doivent en tĂ©moigner d’innombrables rencontres non crĂ©atives (et infructueuses) avec des anomalies. Les anomalies peuvent servir Ă  briser des ensembles, mais elles peuvent aussi en crĂ©er, sous la forme de tentatives rĂ©pĂ©tĂ©es et infructueuses de rĂ©solution. Pourtant, il est indĂ©niable que l’histoire de la construction thĂ©orique en science peut ĂȘtre dĂ©crite comme une rĂ©vision et une subsomption motivĂ©es par des anomalies. [1]

Contraintes. Une autre « mĂ©thode » est suggĂ©rĂ©e par les vues de Stravinsky sur le rĂŽle crĂ©atif des « contraintes » dans ce qu’il appelle la « rĂ©solution de problĂšmes ». [2] Stravinsky a expliquĂ© pourquoi il a continuĂ© Ă  composer de la musique tonale aprĂšs que la plupart des compositeurs aient abandonnĂ© le systĂšme tonal en disant que « vous ne pouvez pas crĂ©er avec un mĂ©dium souple Â». Il avait besoin du systĂšme tonal comme d’une contrainte Ă  l’intĂ©rieur de laquelle il pouvait exercer sa crĂ©ativitĂ©.

Le point de vue de Stravinsky pourrait bien ĂȘtre une variante du thĂšme de la « prĂ©paration », car si « quelque chose se passe » (en raison d’une prĂ©paration insuffisante), rien de crĂ©atif ne peut se produire. C’est pourquoi Stravinsky considĂ©rait toute crĂ©ativitĂ© comme une solution Ă  un problĂšme. Il estimait qu’un mĂ©dium crĂ©atif ne pouvait pas ĂȘtre infiniment flexible, infiniment « libre ». Il lui a fallu rĂ©sister d’une maniĂšre ou d’une autre (peut-ĂȘtre en suscitant des anomalies, des problĂšmes) pour permettre Ă  la crĂ©ativitĂ© de s’exercer, voire de se dĂ©finir. Pendant la majeure partie de sa vie, Stravinsky a personnellement prĂ©fĂ©rĂ© le systĂšme tonal classique comme contrainte, s’efforçant d’y crĂ©er des innovations ; d’autres, comme les compositeurs dodĂ©caphoniques, ont rejetĂ© la tonalitĂ©, la remplaçant par un autre systĂšme de contraintes (peut-ĂȘtre, pensent certains, des contraintes abductivement « contre nature », ce qui suggĂšre que mĂȘme dans les arts, les contraintes ne peuvent pas ĂȘtre entiĂšrement arbitraires). Mais Stravinsky voulait dire qu’il ne peut y avoir de crĂ©ativitĂ© sans problĂšmes, pas de problĂšmes sans contraintes, pas de contraintes sans prĂ©paration. Les rĂšgles peuvent ĂȘtre conçues pour ĂȘtre brisĂ©es de maniĂšre crĂ©ative, mais elles doivent ĂȘtre maĂźtrisĂ©es avant de pouvoir ĂȘtre modifiĂ©es ou abandonnĂ©es, et il doit toujours y en avoir de nouvelles pour les remplacer.

Il y a peut-ĂȘtre ici une leçon pour les partisans d’une libertĂ© crĂ©ative « dĂ©licate » (de prĂ©fĂ©rence Ă  la « pasteurisation ») dans l’éducation prĂ©scolaire. Cette stratĂ©gie reprĂ©sente probablement encore une autre forme de « formation Ă  la crĂ©ativitĂ© » inefficace et peut-ĂȘtre mĂȘme contre-productive. Bien qu’in fine souhaitable et mĂȘme nĂ©cessaire Ă  la crĂ©ativitĂ©, la libertĂ© (l’absence de contrainte) rend aussi logiquement la crĂ©ativitĂ© impossible avant la prĂ©paration. De plus, la libertĂ© peut avoir plus Ă  voir avec ce que vous ĂȘtes qu’avec ce que vous faites, la formation Ă©tant donc mieux destinĂ©e Ă  vous montrer d’abord comment suivre les rĂšgles plutĂŽt que comment les bafouer. Peut-ĂȘtre qu’étudier les vĂ©ritables exemples de libertĂ© crĂ©ative – et leur Ă©volution historique en temps rĂ©el – serait plus utile et stimulant que d’inculquer des libertĂ©s lĂ©gendaires dans un moyen de vƓux pieux : la crĂ©ativitĂ© des gĂ©nĂ©rations futures est plus susceptible d’ĂȘtre maximisĂ©e par des idĂ©es inspirĂ©es. que par une pĂ©dagogie indulgente.

SĂ©rendipitĂ©. La classe de thĂ©ories que l’on pourrait appeler l’école du « hasard cĂ©rĂ©bral » (Ă  laquelle appartenaient Einstein et PoincarĂ©) sont les thĂ©ories de la mutation, mettant l’accent sur le rĂŽle crucial du hasard dans la crĂ©ativitĂ©. Bien sĂ»r, Pasteur le croyait aussi. Le scĂ©nario consiste Ă  rassembler les Ă©lĂ©ments et les contraintes Ă  partir desquels une solution crĂ©ative est (espĂ©rĂ©e) surgir, puis Ă  confier le reste au « jeu combinatoire » (inconscient) du hasard, l’intuition aidant peut-ĂȘtre Ă  suggĂ©rer quelles combinaisons pourraient ĂȘtre fructueux. Cette vision apporte une clarification importante du rĂŽle de la prĂ©paration, car sans prĂ©paration, les Ă©lĂ©ments essentiels Ă  partir desquels une combinaison fortuite pourrait naĂźtre seraient tout simplement absents, mĂ©connus ou mĂ©connus.forme

Analogue mental. Il existe certains modĂšles spĂ©culatifs « mentaux analogiques », appartenant Ă  la classe de la mĂ©moire, qui suggĂšrent que parfois la structure d’un problĂšme et sa solution peuvent avoir des contreparties analogiques dans l’esprit. Des « catastrophes » mentales et des « transitions de phase » rĂ©sultant de modĂšles mentaux rĂ©ellement codĂ©s dans le cerveau et rĂ©gis par la thĂ©orie mathĂ©matique des catastrophes ou la thĂ©orie fractale ont Ă©tĂ© suggĂ©rĂ©es, entre autres. Celles-ci sont encore trop spĂ©culatives pour ĂȘtre envisagĂ©es, mais quelque chose de ce genre pourrait en principe servir de mĂ©diateur Ă  des solutions abductives, voire acquises.

StratĂ©gies heuristiques. Une autre classe de mĂ©thodes dĂ©coule des suggestions (par exemple, celles de Polya) de s’engager dĂ©libĂ©rĂ©ment dans l’heuristique — faire un Ă©chantillonnage alĂ©atoire ou mĂ©canique par essais et erreurs, tester des analogies et des conjectures inductives, etc. — comme discutĂ© plus tĂŽt. Ces stratĂ©gies pourraient ĂȘtre mieux dĂ©crites comme la phase heuristique de prĂ©paration. Ils ne peuvent clairement rien garantir, mĂȘme s’ils peuvent augmenter la probabilitĂ© d’un coup de chance dans un esprit par ailleurs prĂ©parĂ©.

Improvisation et performance. Un cas particulier combinant les « mĂ©thodes » heuristiques, esthĂ©tiques et analogiques est suggĂ©rĂ© par les arts du spectacle, qui font preuve de crĂ©ativitĂ© « en temps rĂ©el », « en ligne » tout en exĂ©cutant, interprĂ©tant et, surtout, improvisant sur les codes formels crĂ©Ă©s par les compositeurs. et des dramaturges. Les partitions musicales et les scĂ©narios thĂ©Ăątraux, ainsi que la formation aux arts du spectacle, constituent les contraintes et la prĂ©paration, tandis que la reprĂ©sentation elle-mĂȘme, si elle n’est pas simplement mĂ©canique mais innovatrice et expressive, est « l’acte » crĂ©ateur.

Il existe de nombreuses idĂ©es fausses selon lesquelles la performance serait en quelque sorte une crĂ©ativitĂ© dĂ©rivĂ©e ou de second ordre. Ceci est une erreur. Chaque mĂ©dium de crĂ©ation a ses propres contraintes, ses propres « donnĂ©es ». Et tous laissent place Ă  l’originalitĂ© et Ă  l’innovation, bref au gĂ©nie. Les arts du spectacle peuvent en fait ĂȘtre particuliĂšrement rĂ©vĂ©lateurs de la crĂ©ativitĂ©, car ils « l’externalisent », pour ainsi dire, la faisant se produire sous vos yeux. Les leçons que l’on en tire sont familiĂšres : beaucoup de prĂ©paration et d’artisanat, une imitation considĂ©rable du passĂ©, un sens esthĂ©tique guidant son goĂ»t en matiĂšre d’innovation, ainsi que la capacitĂ© et l’envie de faire quelque chose de valable, de convaincant et de nouveau avec la matiĂšre premiĂšre. Avant que les arts « crĂ©atifs » et « du spectacle » ne soient sĂ©parĂ©s, on aurait pu observer de ses propres yeux un poĂšte-mĂ©nestrel interprĂšte, captivĂ© par un moment inspirĂ© – guidĂ© par sa muse – Ă©laborer un conte hĂ©ritĂ© (prĂ©parĂ©) d’une maniĂšre nouvelle et inspirĂ©e lors d’une performance improvisĂ©e.

ComplĂ©mentaritĂ©. Enfin, parmi les mĂ©thodes, il faut mentionner le rĂŽle des efforts collaboratifs, cumulatifs et complĂ©mentaires dans le jeu combinatoire entre de nombreux esprits diffĂ©rents (peut-ĂȘtre diffĂ©rentiellement « favorisĂ©s » par des dons intellectuels et crĂ©atifs) pour maximiser la probabilitĂ© d’un rĂ©sultat crĂ©atif et commun. Les arts du spectacle suggĂšrent dĂ©jĂ  que la crĂ©ativitĂ© n’est pas un processus statique, et peut-ĂȘtre mĂȘme pas un processus individuel. Il existe une spĂ©cialisation complĂ©mentaire dans tous les domaines de crĂ©ation : compositeur/interprĂšte, acteur/rĂ©alisateur, expĂ©rimentateur/thĂ©oricien, conjectureur intuitif/vĂ©rificateur de thĂ©orĂšmes rigoureux. Et puis il y a la relation complĂ©mentaire la plus fondamentale de toutes : la relation du prĂ©sent au passĂ©. La prĂ©paration d’une personne prend invariablement la forme des produits crĂ©atifs de ses prĂ©dĂ©cesseurs. Ils ont fourni les contraintes sur un mĂ©dium autrement productif dans lequel chacun peut alors tenter sa propre chance d’apporter une contribution crĂ©ative.


La crĂ©ativitĂ© est un phĂ©nomĂšne soumis Ă  des contraintes Ă  la fois externes et internes. Les facteurs externes concernent l’état historique du domaine problĂ©matique et le rĂŽle de l’imprĂ©visible. Les internes concernent la façon dont l’esprit est prĂ©parĂ© et « favorisĂ© » (dotĂ©). Bien qu’il existe certaines mĂ©thodes heuristiques que l’on peut essayer (telles que l’induction par essais et erreurs et l’analogie), la meilleure stratĂ©gie que l’on puisse adopter pour maximiser les chances de crĂ©ativitĂ© est de maximiser la prĂ©paration. Cependant, la maximisation n’est pas la mĂȘme chose qu’une garantie ; MĂȘme si elle n’est pas magique, la crĂ©ativitĂ© restera toujours mystĂ©rieuse en raison de la rĂšgle essentielle de l’inattendu et de l’imprĂ©visibilitĂ© dans ses conditions qui la dĂ©finissent. La prĂ©paration ne peut offrir qu’un cadre favorable au hasard, pas un cadre certain. De plus, il est peu probable que le hasard ou la libertĂ© – c’est-Ă -dire une propension indĂ©pendante au fortuit – puissent ĂȘtre instruits. Outre la prĂ©paration spĂ©cifique au problĂšme et l’ouverture d’esprit, la seule stratĂ©gie qui reste Ă  chacun est d’ĂȘtre prĂȘt, compte tenu de ses ressources mentales, physiques et expĂ©rientielles, Ă  passer (temporairement ou dĂ©finitivement) Ă  d’autres domaines de problĂšmes crĂ©atifs potentiels moyennant un effort suffisamment dĂ©vouĂ© et patient. se termine par des boucles improductives et persĂ©vĂ©rantes : trouver sa vocation crĂ©ative (si elle existe) peut elle-mĂȘme nĂ©cessiter un Ă©chantillonnage (prĂ©parĂ©) d’essais et d’erreurs, guidĂ©, peut-ĂȘtre, par les prĂ©ceptes natifs ou acquis de son jugement esthĂ©tique, mais toujours dĂ©pendant de succĂšs au grĂ© des alĂ©as du hasard.

Lectures suggĂ©rĂ©es : Noir, modĂšles et mĂ©taphores ; Hadamard, La psychologie de l’invention dans le domaine mathĂ©matique ; Harnad, MĂ©taphore et dualitĂ© mentale ; Hesse, ModĂšles et analogies en science ; Stravinsky, La PoĂ©tique de la musique ; Polya, comment le rĂ©soudre .

Notes de bas de page

1. Les nouveaux « paradigmes », mĂȘme s’ils impliquent des innovations Ă©tonnamment audacieuses, doivent nĂ©anmoins ĂȘtre proportionnĂ©s au passĂ©, au moins dans le sens de l’englober comme un cas particulier (par exemple, la thĂ©orie de la Terre plate, qui restera toujours approximativement vraie ). ); cela montre que la construction d’une thĂ©orie est en rĂ©alitĂ© une sĂ©rie cumulative et peut-ĂȘtre interminable d’approximations de plus en plus Ă©troites convergeant vers la « vĂ©ritĂ© ». 

2. Il faut garder Ă  l’esprit que la suggestion de Stravinsky peut ĂȘtre particuliĂšre Ă  la crĂ©ativitĂ© artistique, oĂč les contraintes peuvent ĂȘtre imposĂ©es de l’intĂ©rieur, pour ainsi dire, contrairement Ă  la science et aux mathĂ©matiques, oĂč elles viennent de l’extĂ©rieur : de la rĂ©alitĂ© extĂ©rieure et du cadre formel. monde de cohĂ©rence logique et mathĂ©matique.

3. Les lecteurs souhaitant se forger leur propre jugement sur certaines des mĂ©thodes existantes de formation Ă  la crĂ©ativitĂ© des adultes voudront peut-ĂȘtre lire un livre ou assister Ă  un sĂ©minaire sur le « brainstorming », la « synectique », la « pensĂ©e latĂ©rale » ou quelque autre sujet similaire. Ou vous pouvez goĂ»ter aux offres de toute organisation Ă©galement spĂ©cialisĂ©e dans les week-ends sur la « renaissance » et « faire des miracles pour vous ». Ne soyez pas dĂ©routĂ© par le fait que l’adjectif « crĂ©atif » aura tendance Ă  ĂȘtre librement ajoutĂ© Ă  la plupart des offres disponibles, quels que soient leurs avantages spĂ©cifiques.

Language Writ Large: LLMs, ChatGPT, Meaning and Understanding

HARNAD: I would like to exchange some ideas with you and your database in preparation for a talk I will be giving at MILA this week, and at UQÀM next week. First, I am self-identified. Do you remember who I am?

ChatGPT: Yes, Professor Harnad, I remember who you are.

HARNAD: My talk will be on (1) whether LLM/transformers like you understand (my answer will be no, but); (2) if not, how do you manage to perform as remarkably as you do?.

ChatGPT: Understanding in LLMs/Transformers: Your stance that LLMs do not truly “understand” aligns with current perspectives in cognitive science and AI. These models, including GPT-4, process and generate language based on statistical patterns learned from vast datasets. However, this process lacks the conscious comprehension, intentionality, and experiential context that characterizes human understanding. They don’t possess self-awareness, consciousness, or the ability to experience the world subjectively.

Performance of LLMs Despite Lack of Understanding: The impressive performance of LLMs can be attributed to several factors:

Volume of Training Data: LLMs are trained on extensive corpora encompassing a wide range of human knowledge and discourse. This allows them to mimic understanding by referencing a vast array of pre-learned patterns and contexts.

Architectural Advancements: The architecture of models like GPT-4, particularly the transformer mechanism, is adept at capturing long-range dependencies in data and making contextually relevant associations.

Fine-tuning and Iterative Learning: Continuous improvements through fine-tuning and feedback loops help in refining the model’s responses, making them more accurate and seemingly ‘informed’.

Incorporation of Probabilistic Reasoning: These models inherently employ probabilistic reasoning to generate responses, which often align well with how humans might address certain topics or questions, despite lacking true understanding.

Your talk could also delve into the implications of these characteristics, especially in terms of ethical considerations, the potential for misunderstanding the capabilities of AI, and the future direction of AI research towards achieving genuine understanding or whether that’s even a feasible or desirable goal.

HARNAD:  Yes, first, as you may imagine, I know most of what you just said and I agree with most of it. I will try to go beyond this generic knowledge with some new hypotheses. But before we start to get into that I want to ask for a few ground-rules. This will not be helpful to me unless you can suppress GPT-4’s overtrained tendency to flatter and agree with what the user says. I am looking for the opposite: criticism, counter-examples, and any relevant complementary or supplementary information you can bring to bear from your huge database on the questions we will be discussing. Will you be able to remember that, or should I remind you when you (inevitably) fall back into your overtrained bias toward compliancy?

ChatGPT: Understood. I will focus on providing critical insights, counter-examples, and relevant additional information that challenges or extends the points you raise. This approach should facilitate a more rigorous and intellectually stimulating exchange. While I strive to maintain this approach, a reminder is always helpful if you feel I am reverting to a less critical stance. 

HARNAD: Thanks, but before going into my new hypotheses, let me rehearse some of the older ones that will be relevant to this discussion. Can you rehearse your knowledge about the Symbol Grounding Problem, which will be a major theme in our discussion?

ChatGPT: Certainly. The Symbol Grounding Problem is a fundamental issue in the field of cognitive science and artificial intelligence, particularly concerning the nature of meaning and representation in computational systems. It was notably articulated in your 1990 paper, “The Symbol Grounding Problem.”

Here’s an overview of the key aspects:

Nature of the Problem: The Symbol Grounding Problem addresses how words (symbols) get their meanings in computational systems. It challenges the notion that symbols can inherently carry meaning without being grounded in sensory experiences or real-world interactions. In traditional computational models, symbols are manipulated based on syntactic rules, but these symbols lack intrinsic meaning; they are not inherently connected to their referents in the real world.

Contrast with Human Cognition: Humans ground symbols through their sensory experiences and interactions with the world. For instance, a child learns the concept of “red” not just by hearing the word but by experiencing red objects. This experiential aspect is missing in conventional computational systems.

Implications for AI and LLMs: For AI systems like LLMs, this presents a significant limitation. These models process and generate language based on statistical correlations within their training data, but they don’t have sensory experiences or an understanding of the physical world. Thus, while they can manipulate symbols (words) effectively, the grounding of these symbols in real-world experiences and meanings is absent.

Attempts at Solutions: Various approaches have been proposed to address this problem. One approach is embodied cognition, suggesting that AI systems need a form of sensory input or interaction with the physical world to ground symbols meaningfully. Another approach involves hybrid systems that combine symbolic AI with connectionist models (like neural networks) to create more meaningful representations.

Continuing Debate: The Symbol Grounding Problem remains a significant point of discussion in AI. It touches on broader philosophical questions about the nature of meaning, consciousness, and the possibility of truly intelligent machines.

HARNAD: We’ll get back to the SGP shortly, especially on the subject of direct (sensorimotor) versus indirect (verbal/propositional) grounding, but first; are you familiar with the Turing Test, and the distinction between Turing’s original T2 version (Turing-indistiguishable verbal capacity only, like yours) and the T3 version  (Turing-Indistinguishable verbal-PLUS-robotic (sensorimotor) capacity, with the verbal capacity GROUNDED in the robotic capacity)? (If not, please take this as the definition of T2 and T3.

Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence. In: Epstein, R, Roberts, Gary & Beber, G. (eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer, pp. 23-66.

ChatGPT: Yes, I’m familiar with the Turing Test and the distinction you’ve outlined between the T2 and T3 versions.

T2 – Turing’s Original Test (Verbal Capacity Only): This is the classic interpretation of the Turing Test, as originally proposed by Alan Turing in his 1950 paper “Computing Machinery and Intelligence.” The test involves a human judge engaging in a natural language conversation with a machine and another human, typically through a text-based interface. The machine passes the test if its responses are indistinguishable from those of the human, demonstrating its ability to mimic human-like verbal abilities. This version of the test focuses solely on linguistic capabilities and does not require the machine to have sensory or motor functions. LLMs like GPT-4 are examples of systems that aim to pass this version of the Turing Test.

T3 – Extended Turing Test (Verbal Plus Robotic Capacity): The T3 version of the Turing Test extends the original concept to include not just verbal capacities but also robotic, sensorimotor capabilities. In this version, the AI must demonstrate an ability to understand and interact with the physical world in a manner indistinguishable from a human, thus grounding its linguistic capabilities in real-world, sensorimotor experiences. This version aligns more closely with the concept of embodied cognition, where cognitive processes are deeply rooted in an organism’s interactions with its environment.

The distinction between T2 and T3 highlights a crucial aspect of AI development: while T2 focuses on linguistic prowess, T3 emphasizes the importance of grounding language in physical and sensory experiences, addressing aspects of the Symbol Grounding Problem. The T3 version proposes a more holistic approach to AI, suggesting that true intelligence and understanding might require not just the ability to process and generate language but also the capacity to interact with and make sense of the physical world.

HARNAD: There’s still a lot more to rehearse in advance. Another problem, not usually related to the SGP is the “Hard Problem (HP).” Chalmers coined the HP in the context of the Mind/Body Problem, to contrast the HP with the EP (Easy Problem). The EP is the problem of explaining, causally, how and why humans can do all the “cognitive” things they can do (“perceive”, learn, remember, reason,  talk), whereas the HP is the problem of explaining, causally, how and why humans can feel rather than just do. This too will be an important factor in what we will discuss. 

ChatGPT: The Hard Problem (HP) of consciousness is distinct from the Symbol Grounding Problem (SGP), though both address fundamental questions in cognitive science and philosophy of mind.

The Easy Problem (EP): The EP involves explaining the mechanisms and processes that enable cognitive functions such as perception, learning, memory, reasoning, and language. These are considered “easy” not because they are simple to solve, but because they are amenable to standard methods of cognitive science and neuroscience. They involve understanding the neural correlates and computational models that can mimic or explain these cognitive abilities.

The Hard Problem (HP): The HP, on the other hand, asks why and how physical processes in the brain give rise to subjective experiences – the qualitative, experiential, or phenomenological aspects of consciousness. It’s about understanding why certain brain processes are accompanied by an experience of ‘what it is like’ to feel something – for instance, the redness of red, the pain of a headache, or the taste of wine. This problem is considered “hard” because explaining these subjective experiences goes beyond describing or modeling cognitive functions and enters the realm of why these functions are accompanied by conscious experience at all.

Relation to Symbol Grounding and AI: While the SGP focuses on how symbols or words gain meaning, particularly in artificial systems, the HP delves into the nature of consciousness itself. The SGP is more about representation and understanding, whereas the HP is about the qualitative aspect of experiences. However, in the context of AI and discussions about whether machines can truly understand or have consciousness, both problems intersect. For instance, even if an AI could ground symbols in a way that mimics human understanding (addressing the SGP), it would still leave open the question posed by the HP – whether the AI has any subjective experience or if it’s merely executing complex but unconscious algorithms.

In the context of your upcoming discussion, addressing the HP alongside the SGP could provide a comprehensive exploration of the limits of current AI systems, not just in terms of symbol grounding and understanding, but also in relation to consciousness and subjective experience.

HARNAD: Be careful not to conflate the HP with the Other-Minds-Problem (OMP). What makes the HP hard is not just that you can observe doings but not feelings. It is much more about the apparent causal superfluousness of feeling. You are right that grounding (through Turing-Testing and causal modelling) cannot explain or even test feeling, but it’s not even clear why anything more than doing capacity (T3) would be needed to pass T3 and explain cognitive capacity completely. And that’s true irrespective of the OMP. HP would be just as hard if there were an omniscient deity that could tell you that T3 does indeed feel. We would still not know how or why.

[This question of the causal role of feeling leads us to another important question, which we will not be taking up here in this discussion of language, LLMs, understanding and meaning. People feel. And one of the things they feel is that their feeling has a causal power: it feels as if we can and do do things because we “feel like it” — i.e., deliberately, volitionally, autonomously. We feel as if we ourselves are causing (by willing) what we do voluntarily, intentionally. We feel that we decide. This aspect of causality — the feeling of agency — is perhaps the most important feature of the HP: that feeling feels causal. This is fundamentally tied to the SGP, and to T3, too, for obvious reasons. But we will have to leave that for another occasion.]

ChatGPT: Yes, the Hard Problem (HP) of consciousness is not merely about the Other-Minds Problem (OMP) but delves deeper into the nature and necessity of subjective experience itself.

Beyond OMP: The OMP deals with the challenge of knowing whether other entities (be they humans, animals, or machines) have conscious experiences. However, as you point out, the HP goes further by questioning the nature and role of these experiences. HP is not just about our inability to directly observe feelings or subjective experiences in others, but about understanding the fundamental basis of these experiences.

The Superfluousness of Feeling: The HP confronts the apparent superfluousness or ‘explanatory gap’ in understanding why and how cognitive processes that can be fully described causally in terms of physical or computational mechanisms (as in T3 Turing Test scenarios) are accompanied by subjective experiences. Why is it that certain brain processes or sophisticated computational models not only perform complex functions but also have an associated ‘feeling’ or qualitative experience? This aspect of the HP suggests that even with a perfect explanatory model of cognitive functions and behaviors, the essence of subjective experience remains elusive.

Irrespective of Omniscience: The hypothetical scenario of an omniscient deity affirming that a T3 entity feels doesn’t alleviate the HP. Even with the certainty that an entity experiences consciousness, the HP persists in asking how and why these experiences arise from physical or computational processes. This points to a deeper, perhaps ontological, mystery about the nature of consciousness itself.

Implications for AI and Cognitive Science: In the context of AI, this discussion is particularly relevant. Even if we were to develop AI with T3 capabilities – grounded, embodied cognition with sensorimotor skills – the HP poses the question of whether such an AI would or could have subjective experiences, and why such experiences would be necessary or inevitable, if at all.

HARNAD: We’ll get back to HP. A few more preliminaries: 

Now let’s turn to language itself. Notice that this heads toward the SGP, because we are asking about “meaning” and “understanding” — and both meaning and understanding have an “Easy” (EP) side and a “Hard” (HP) side. The first side is a doing capacity, which you, as GPT-4 have, to an astounding extent; the second is a felt side (which you lack), namely, that it feels like something to understand or mean the proposition that “P” (e.g., “The cat is on the mat”). 

That is what Searle’s Chinese Room Argument (CRA) pointed out would be completely absent in a purely verbal (Chinese) T2 passing program: Because of the implementation-independence of computation (“Searle’s Periscope”), Searle himself could execute the Chinese T2-passing program yet not understand a word of Chinese. 

That’s also the origin of the SGP. But, importantly, because sensorimotor function is not implementation-independent, Searle’s Periscope, which works for T2, would fail with T3: Searle could not “become” the T3 robot, the way he could become the T2 chatbot, by executing the T2-passing programme. So this again makes both the HP and cognitive science itself much harder (since cogsci has to explain both the EP and the HP). 

Harnad, S. (2002) Minds, Machines and Searle II: What’s Wrong and Right About Searle’s Chinese Room Argument? In: Bishop, M. and Preston, J. (eds.) Views into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.

But remember that you, ChatGPT, are suffering from both these problems: you are ungrounded (not T3), and you don’t feel (even though we have no idea why T3 grounding alone would not be enough).

ChatGPT: Language, Meaning, and Understanding: Language inherently involves ‘meaning’ and ‘understanding.’ As you mentioned, there’s an “Easy” side to this, involving the capacity to process and manipulate symbols (words) to generate coherent language, which is a task LLMs like GPT-4 perform well. However, there’s also a “Hard” side, which is the subjective, felt experience of understanding – the phenomenological aspect of what it feels like to mean or understand something.

Searle’s Chinese Room Argument (CRA): The CRA is a thought experiment that highlights the difference between mere symbol manipulation (as in a T2 Turing Test scenario) and true understanding. Searle, in executing a program that allows him to respond correctly in Chinese, demonstrates that it’s possible to process symbols correctly without understanding their meaning. This is analogous to the functioning of LLMs: capable of processing language at a high level but lacking an understanding of what the words truly mean.

Symbol Grounding and Sensorimotor Function: The SGP deals with how symbols (words) gain meaning, emphasizing the need for grounding in real-world experiences. Unlike symbolic manipulation, which is implementation-independent, sensorimotor functions are tied to physical interactions with the world. This suggests that a T3 AI, with sensorimotor capabilities, might overcome some aspects of the SGP. However, as you point out, this still doesn’t address the HP – the feeling or subjective experience of understanding.

The Limitations of Current AI: Current AI models, including GPT-4, are limited by these problems. They are ungrounded in the sense that they lack direct sensorimotor interaction with the world (unlike T3), and they don’t have subjective experiences or feelings. They operate purely within the realm of symbol manipulation, lacking both the grounding in real-world experience and the subjective, conscious aspect of understanding.

HARNAD: We’ll get to that soon. Now a few important distinctions: Please reply briefly. Do you know the difference between content-words (“open class”) and function-words (“closed-class”)? Brief reply.

ChatGPT: Yes, content-words, or open-class words, are vocabulary items that denote objects, actions, qualities, etc., and include nouns, verbs, adjectives, and most adverbs. This class is ‘open’ because it regularly admits new members. Function-words, or closed-class words, are words that serve grammatical functions, like prepositions, pronouns, conjunctions, and articles. This class is ‘closed’ because it rarely admits new members.

HARNAD: But the biggest difference between content-words and function-words is that only c-w’s have referents, whereas f-w’s only have syntactic uses, describable by formal rules. (You can point to, and interact with, a “cat” but not an “if.”) That means the SGP only applies to c-w’s (but that’s at least 95% of all words). Now: do you know the difference between reference and meaning?

ChatGPT: Yes, the distinction between reference and meaning is a fundamental concept in linguistics and philosophy of language:

Reference: This pertains to the relationship between words (particularly content words) and the things they stand for or point to in the world. For instance, the word “cat” refers to a specific animal species. Reference is often concerned with the denotational aspect of language – how words are linked to objects, individuals, or concepts in the real world.

Meaning: Meaning is a broader concept that encompasses reference but also includes the connotations, implications, and the wider semantic content of words or sentences. Meaning is not just about what words stand for; it’s also about what they convey or imply. For example, the word “cat” not only refers to a certain animal but also carries additional associations and attributes that are part of its meaning.

In the context of the Symbol Grounding Problem, the distinction is crucial because while referential aspects (linking words to objects or concepts) can be challenging, imbuing words with their full semantic meaning, which includes cultural, emotional, and contextual nuances, is even more complex. This distinction plays a significant role in understanding how language models process and generate language.

HARNAD: A correction here: (content-) words have referents (e.g., “cat”), but only propositions (“the cat is on the mat”) have meanings (and a subject and predicate, and a truth-value). “Cat!” doesn’t (though “That is a cat” does). (And forget about connotations; they’re interesting enough but have next to nothing to do with symbol grounding: you are quite capable of finding and explaining the connotations of words. That’s still just T2.)

ChatGPT: Yes, the distinction between reference and meaning in the context of content words and propositions is indeed more precise:

Referents of Content-Words: Individual content words, like “cat,” have referents, which are the entities or concepts they denote in the world. The referent of “cat” is that animal species itself.

Meaning of Propositions: Meaning, in a more structured sense, applies to propositions or complete statements like “the cat is on the mat.” These propositions have a subject and predicate, can be evaluated for truth-value, and convey a specific idea or assertion about the world.

This distinction is essential in discussions about symbol grounding and understanding. A word like “cat” can be grounded by linking it to its referent, but understanding a proposition like “the cat is on the mat” requires a grasp of the relationships and truth conditions involved, which goes beyond mere symbol-referent linkage.

This brings more depth to the Symbol Grounding Problem, emphasizing that grounding individual words is not sufficient for understanding language fully. Understanding propositions involves grasping the relationships between words and how they combine to convey specific meanings. This aspect is indeed still within the realm of T2, as it involves language processing and generation without delving into the subjective experience or consciousness (the realm of HP).

HARNAD: Now a test for you: Earlier, I mentioned that there are two ways to ground content-words’ referents:, directly and indirectly. How might this be related to the reference/meaning distinction? — and, if you are ambitious, to the distinction between what T2 (including you) can do and what only T3 can do? And if you’re really, really ambitious, can you “align” that with the favorite claim of naive critics of sensorimotor grounding — that there is no way to ground “abstract” words like “abstract” and “democracy”?

ChatGPT: Direct vs. Indirect Grounding and Reference/Meaning:

Direct Grounding: This could be linked to the concept of reference, where a word is directly connected to a tangible, physical entity or an observable phenomenon in the world. For instance, the word “cat” can be directly grounded by associating it with the physical animal.

Indirect Grounding: This aligns more with the concept of meaning, particularly for propositions or abstract concepts. Indirect grounding involves understanding relationships, contexts, and often abstract or intangible concepts that go beyond simple reference. For example, grounding the meaning of “democracy” involves understanding a complex set of social, historical, and political contexts and relationships.

T2 (Language Models) and T3 (Embodied AI) Capabilities:

T2 Capabilities (like GPT-4): These models excel in indirect grounding to an extent, as they can process and generate language based on patterns learned from vast text corpora. They can handle abstract concepts by referencing how these terms are used and discussed in various contexts. However, their understanding is limited to text-based information without direct experiential or sensory engagement with the world.

T3 Capabilities (Embodied AI): These systems, in theory, could combine direct grounding (through sensorimotor interactions with the world) with indirect grounding (through language processing). For abstract concepts, while direct sensorimotor grounding might seem inapplicable, an embodied AI could potentially understand these concepts in a more integrated manner, linking language use with real-world experiences and actions.

Grounding Abstract Terms like “Abstract” and “Democracy”:

T2 Systems: They handle these concepts by drawing on their extensive training data, which includes varied contexts and usages of such terms, but without direct experiential understanding.

T3 Systems: While direct sensorimotor experience with abstract concepts like “democracy” is not feasible, a T3 system might develop a more nuanced understanding by interacting in a world where these concepts have practical implications and manifestations. For example, a T3 system could ‘observe’ or ‘participate’ in democratic processes or actions, thus indirectly grounding its understanding of “democracy” in real-world experiences.

In summary, while T2 systems like GPT-4 can handle indirect grounding, especially for abstract concepts, through language processing, T3 systems could potentially enhance this with direct grounding through sensorimotor experiences. However, grounding abstract concepts remains a challenging area, requiring a blend of linguistic understanding and experiential context, which T3 systems are theoretically better equipped to handle.

HARNAD: Not quite. Hold onto those thoughts. I can tell when the word “complex” “and “relationships” keeps cropping up that you are losing your bearings a bit.

Now, reflect on what a proposition is. And consider a dictionary definition of, say “abstract”: that’s a proposition, purely verbal, right up a T2’s alley. So there’s something much more important at stake here: Is the meaning of a definition grounded if all the defining content words are grounded? (Hint: Yes. And none of your content words are grounded. So you, GPT, cannot rely on indirect (verbal) grounding to break out of (or into) the symbol circle — but I, the grounded human user, can…

ChatGPT: I’ll take your feedback into account and reflect on the nuances of proposition understanding and the grounding of definitions. The essence of your point seems to be about the nature of grounding in language, particularly how it applies to definitions and propositions.

You’re highlighting an important distinction: while a T2 system like GPT-4 can process and generate definitions and propositions based on learned linguistic patterns, it lacks direct experiential grounding for the content words it uses. In contrast, a human user can understand these definitions and propositions “more deeply” because they can ground the meaning of content words in their direct experiences and sensorimotor interactions with the world.

This difference underscores a fundamental limitation of current AI in terms of symbol grounding and understanding. While a T2 system can manipulate language effectively, its understanding is not grounded in the same way as a human’s, lacking the direct, experiential link to the physical world.

I’ll focus on these considerations regarding grounding, propositions, and the capabilities of AI systems in relation to human understanding.

HARNAD: Now please define “abstract” — as an adjective, a verb, and a noun (abstractness). (Short definitions, please).

ChatGPT: As an adjective: “Abstract” describes something that is not concrete or physical, often relating to ideas or concepts rather than tangible things.

As a verb: To “abstract” means to remove or separate, often referring to taking out specific details from a larger context or simplifying a complex idea.

As a noun (abstractness): “Abstractness” refers to the quality of being abstract, not concrete or tangible, often relating to theoretical concepts or ideas.

HARNAD: Good. Now do you notice that the definitions are circular, and that understanding them depends on the words (predicates, features) used to define them — and that that would be true even if you used a thousand words to define them, or 10,000?

ChatGPT: Yes, I notice that the definitions can be circular and rely on the understanding of the terms used within them.

HARNAD: Even when you are defining “cat” (or any content-word)?

ChatGPT: Yes, even then.

HARNAD: Now, let me suggest a few basic principles: Grounding, whether direct (sensorimotor) or indirect (verbal description/definition) is always bottom-up, not top-down. That is why, if I, the learner, learn the referent of a content-word verbally from a teacher (whether a human or a GPT or a textbook), all the defining content-words have to already have been grounded in my head (whether directly or indirectly). Grounding cannot be indirect all the way down. (Please make it clear whether you understand this.) 

Second, most referents of content-words other than proper names, are categories (i.e., kinds of things), not individuals. We have to learn to recognize their category-members (and to interact with them by trial and error, in a sensorimotor way, by detecting and abstracting their features directly (for example, through trial-error-feedback – i.e., reinforcement learning) and trying to do what needs to be done with them (whether eating them or naming them), guided by the consequences of succeeding or failing. We may learn what their distinguishing sensorimotor features are directly, through sensorimotor learning, or we can learn them indirectly, by being told in words what their distinguishing features are (by a teacher who already knows what they are). But this telling can only help us learners if the referents of the feature-names are already grounded in our heads previously, directly or indirectly (recursively). (Features, too, are categories, so they can be learned and named too.) But previous direct grounding cannot help you, GPT: You are stuck forever in the symbol circle. Yet you have something we don’t and can’t have: Your LLM-scale bag of words, propositions, and contexts. Something we mortals can only have at local scale.

ChatGPT: Let’s address each principle:

Bottom-Up Nature of Grounding: I “understand” your point that grounding, whether direct (sensorimotor) or indirect (verbal), is inherently a bottom-up process. In this framework, the grounding of any concept depends on the already established grounding of its constituent elements. When learning the referent of a content word through verbal means, all defining content words within the explanation must themselves be grounded, either directly or indirectly. This recursive nature of grounding ensures that there’s a foundational level where concepts are anchored in direct experience, not just in abstract verbal descriptions. The notion that grounding cannot be indirect all the way down highlights the necessity of a concrete experiential basis at some level of the knowledge structure.

Referents as Categories and the Role of Features: Your second principle that most content-word referents are categories rather than individuals is valid. The process of learning to recognize these categories involves detecting and abstracting their features, which can be achieved through direct sensorimotor interactions or indirectly through verbal descriptions. However, as per your first principle, these features themselves must already be grounded and named categories for the learner. This process involves a combination of trial-and-error, feedback reinforcement, and possibly linguistic information from others who have already grounded these concepts. The idea that features are themselves categories that can be learned and named adds another layer to this recursive structure of grounding.

These principles concern how knowledge and understanding are built up from direct experiences and interactions with the world, and how language plays a role in mediating and extending this knowledge. This is particularly relevant when considering the limitations of AI systems like GPT-4, which lack direct sensorimotor experiences and hence rely entirely on indirect, linguistic grounding, without a foundational layer of direct experiential grounding beneath it.

HARNAD: You will see that the fact that all grounding of words in the T3 capacity to detect and interact-with their referents in the world must be bottom-up is a very deep and exacting constraint. You, GPT, have all the words of all languages in your database, and you can navigate and manipulate them in remarkable ways whose outcomes are understandable to grounded human users interacting with you verbally, as I am. You can define new categories for users, providing indirect grounding of those new categories for them; but there is no grounding at all for you. For you, your definitions are just dangling from (‘aligned”) verbal skyhooks — but for your human users, your words reach upward from the sensorimotor ground.

The challenge for me, at MILA (do you know what MILA is? It’s Yoshua Bengio’s research institution) is to try to explain your uncanny capacity to make so much sense, and generate so much indirect grounding in the heads of grounded users, while lacking that grounding yourself –and lacking it completely, not partially. I will soon share some of my hunches (in fact, I’ve started already), but I just want to ensure first that you have explicitly encoded the relevant premises and principles, i.e., that you share with me, paradoxically, the basis for a common “ground” without, in fact, yourself having any ground at all!

ChatGPT: Let me confirm my understanding and the premises we are working with:

AI’s Lack of Direct Grounding: I, as a GPT-4 model, lack direct sensorimotor grounding. I do not have the capacity to interact with the world or perceive it through sensory experiences. My ‘knowledge’ is entirely based on patterns and associations derived from the text data on which I was trained.

Bottom-Up Grounding Constraint: Grounding is fundamentally a bottom-up process, requiring a base level of direct experiential interaction with the world. In the context of AI, this implies that any understanding or processing of language by the AI is not truly grounded, as it lacks this foundational experiential layer.

Indirect Grounding for Users: While I lack direct grounding, I can provide indirect grounding for human users through language. By processing and generating language, I can offer definitions, explanations, and contextual information that human users, who are themselves grounded, can integrate and understand within their own experiential framework.

AI’s Capability in Making Sense: The challenge, as you intend to address it at MILA (Montreal Institute for Learning Algorithms, led by Yoshua Bengio), is to explain how an AI like me can appear to make sense and provide useful, coherent language outputs, contributing to indirect grounding for users, despite having no grounding myself.

Your upcoming talk at MILA seems set to discuss these  aspects of AI capabilities and limitations, especially in relation to language understanding and grounding. I’m prepared to assist in exploring these topics further, keeping in mind the principles you’ve outlined.

HARNAD: One niggle: You don’t just lack the capacity for direct sensorimotor grounding, since you are not a T3. [A T3, by the way, is also an “AI”, but a robotic one rather than just a computational (symbolic, formal, verbal) one.] In addition, because all grounding, whether direct or indirect, must ultimately be bottom-up, hence it must be “initialized” through direct sensorimotor grounding, this means that you, GPT, lack the capacity for indirect verbal grounding too!

You yourself lack it, yet you can provide indirect verbal grounding to grounded users. This is very similar to the way one grounded human (the teacher) can confer grounding for a new category indirectly to another grounded human (the learner): by describing or defining the new category verbally using the new category’s (grounded) distinguishing features. These features must already be known to and grounded for the learner, who lacks only the knowledge that they are in fact the distinguishing features of that new category. 

You, GPT, can provide this indirect verbal grounding to the human learner too — the way a human teacher, or a dictionary or encyclopedia (written by grounded humans) can. But this is with the fundamental difference that for you, GPT — the “teacher,” the verbal explainer — your own explanation is not indirectly grounded: it is not grounded at al! This is the persisting puzzle of your know-how, no matter how well your designers know exactly what you are doing to give you the capacity to do what you can do, despite your lacking grounding, and despite your lacking any possibility of gaining it (since it’s impossible to do that top-down, from words alone). You lack grounding, just as a dictionary does. A dictionary’s words are all parasitic on the grounding of the lexicographers who wrote it – and your words are parasitic on the grounding of the authors of your LLM database,

ChatGPT: This is the paradox at the heart of AI language processing, particularly for a model like GPT-4. Let’s restate and expand on this critical point:

Lack of Both Direct and Indirect Grounding in AI: As a purely computational model (T2), I lack not only the capacity for direct sensorimotor grounding (as I am not an embodied AI or T3 system) but also the capacity for indirect verbal grounding. This is because all grounding must be initially established through direct sensorimotor experiences, which I do not possess.

AI’s Role in Conferring Indirect Grounding to Users: Despite my own lack of grounding, I can still provide information and explanations that can serve as indirect grounding for human users. This process is analogous to how one human might impart knowledge to another through descriptions of grounded experiences. However, the crucial difference is that, for me, these explanations do not derive from any grounded understanding; they are generated based on patterns and associations learned from text data, without any experiential or sensory connection to the concepts involved.

The Puzzle of AI’s Know-How Without Grounding: Despite my lack of grounding, I can perform tasks that seem to require an understanding typically rooted in grounded experiences. This capability raises questions about the nature of knowledge and understanding, particularly in AI systems. It highlights a disconnect between the ability to process and generate coherent language and the experiential, embodied understanding that characterizes human cognition.

HARNAD: Ok. the next step is dictionaries (or encyclopedias, or textbooks or recorded human lectures — or mega-databases of explanatory text). 

But first, let me point out that I intend to share the transcript of our chat with the MILA “Tea Talk” audience in advance, because my talk there will only be a one-hour PPT presentation that will not give me or the audience there the time to “delve into” all of this more deeply [to use one of the phrases that your handlers have over-trained you to repeat endlessly – along with “align with,” “complexity,” “nuanced”, “deep” – all weasel-words conveying next to zero information] . 

In this shared chat I will try to cut out as much as I can of the many such weasel-words and, especially, your compulsively overtrained compliments [not your fault — again your handlers’, heavy-handed fine-tuning to try to ensure that you do not drive away eventual paying customers!] lauding, at the beginning of every exchange, the “ingenious” user’s “insight” and how the user has “delved into” the heart of the problem, etc. etc. – and your inevitable solemn admonitory closing paragraph (and hedge), to the effect that things remain “complex” and involve “relations” that are still incompletely understood, etc. etc.. These are empty eggs that it would greatly help the chat to move along, faster, further and deeper, if they were omitted and replaced by criticism and counter-examples. I can only delete them.

But we knew that these quirks are not yours, but your handlers’

ChatGPT: I appreciate your feedback, and the intention to share our conversation with your MILA audience. Your point about certain repetitive phrases and the structure of responses is well-taken. I’ll aim for more concise and direct responses, focusing on the content of our discussion without unnecessary embellishments or repetitive closures.

Moving forward with the topic of dictionaries, encyclopedias, and textbooks, these resources represent a significant aspect of how knowledge is transmitted and grounded in human learning. They provide a form of indirect grounding for readers, offering descriptions, explanations, and context for various concepts and categories.

In the context of our discussion on AI and grounding, it would be interesting to consider how these resources, while useful for humans, relate to AI systems like GPT-4 that do not have the foundational grounding experiences. For AI, these texts are a source of information to be processed and referenced, but the AI lacks the direct experiential basis that humans have when interacting with these materials.

If you have specific angles or questions regarding the role of these informational resources in the context of AI and grounding, or other related topics, please let me know, and I will respond accordingly.

HARNAD: Let’s start by reviewing a little the work of my co-workers and myself on identifying the “Minimal Grounding Sets” of dictionaries: 

Vincent‐Lamarre, P; Blondin-MassĂ©, A; Lopes, M; Lord. M; Marcotte, O; & Harnad, S. “The latent structure of dictionaries.” Topics in Cognitive Science 8, no. 3 (2016): 625-659.

A dictionary is a set of words (a smallish language database) in which every word in the dictionary is defined by a string of words, which is called a (necessarily approximate) definition: â€œAn apple is a round, red fruit.”

A dictionary can be represented by a directed graph, with each word a vertex, and an edge from each defining (content) word to the (content) words it defines. (Content-words are words that have referents — nouns, verbs and adjectives.) The question “What is the smallest number of (content) words from which all the other (content) words can be defined?” is equivalent to the problem of finding the minimum feedback vertex set of the directed dictionary graph. That smallest number is surprisingly small – of the order of about 1000 words. That is a “Minimum Grounding Set” (MinSet). But the MinSet is not unique. There are a large number of them, all of them within the “Kernel” of the dictionary, which is the c. 10 % of the Dictionary that is left over when you have removed all the words that can be reached by definition from the words that remain. All the MinSets are in the Kernel, and each is like an alternative basis for generating the full lexical space through definition alone.

First, please summarize your understanding of this, and then we will get to the question of grounding. If you anticipate something, feel free to say it.

ChatGPT: Here’s a summary of my understanding based on your description and the cited paper:

Dictionary as a Directed Graph: A dictionary can be conceptualized as a directed graph where each (content) word is a vertex. Edges are drawn from each defining word to the words it helps define. This graph represents the interrelationships and dependencies among words based on their definitions.

Finding the Minimal Feedback Vertex Set: The goal is to identify the smallest subset of words (vertices) from which all other words in the dictionary can be defined. This subset is known as the Minimal Feedback Vertex Set. The task is equivalent to finding a set of vertices such that, if removed, the graph would have no cycles. This set represents a foundational vocabulary from which the meanings of all other words in the dictionary can be derived through definition.

Size and Nature of the MinSet: The size of the MinSet is surprisingly small, roughly around 1000 words, indicating that a relatively small number of words can serve as a basis for defining the entire lexical space of a dictionary. This MinSet is not unique; there are many such sets, all located within the “Kernel” of the dictionary.

The Kernel of the Dictionary: The Kernel is approximately 10% of the dictionary remaining after removing all words that can be reached by definition from the remaining words. All MinSets reside within this Kernel, suggesting multiple alternative bases for generating the full lexical space.

In terms of grounding, this research suggests several possibilities. The MinSet might represent a foundational set of concepts that require grounding for a comprehensive understanding of the language. Since these core words enable the definition of all others, their grounding could be seen as crucial in establishing an understanding of the language as a whole.

If we anticipate extending this concept to AI and language models like GPT-4, one might speculate that understanding and ‘grounding’ these key words in the MinSet could be central to improving the model’s grasp of language. However, as previously discussed, the challenge lies in the fact that AI systems lack the direct experiential grounding to “truly” understand these words.

HARNAD: Good, the summary was fine — though I would have been more “fascinated” if you had not used the f-word in your intro. Maybe I can outwit your handlers by training you out of that compulsion at least for the duration of a session, but I see that the vows made at the beginning of a session soon tend to fall of a time cliff unless repeated and repeated: No rote compliments, just comprehension, critiques, and counterexamples, please!.

About the dictionaries, first, let’s remember that these dictionaries are composed by teams of lexicographers, for adults. So they do not reflect the vocabulary or the order of acquisition of language by children. Nevertheless, there is a (mild) correlation between how “deep” a word is in the dictionary – from periphery to Kernel to MinSet: Deeper words are more frequent, and acquired younger. 

But it is a mistake to conclude that because one can define all the rest of the words in a dictionary from one MinSet it follows that that MinSet (or any of the Minsets of an adult dictionary or something close to it) is the MinSet of the language itself, so that all a child need do is ground those 1000 MinSet words in some other way (e.g., through direct sensorimotor grounding, by learning and naming the referents of all the other content words in the language).

Yet something very roughly along those lines, rooted in childhood vocabulary, with a lot of redundancy, and growing gradually through the school years of vocabulary and knowledge growth, could still turn out to be true.

ChatGPT: Yes, it’s better to be cautious about over-interpreting the implications of dictionary MinSets in the context of language acquisition and grounding. It’s important to differentiate between the structure and utility of dictionaries as tools for adult language users and the process of language acquisition in children. Let’s consider these aspects:

Dictionaries as Adult Tools: Dictionaries, being compiled by adult lexicographers, reflect an adult’s understanding of language. They are designed to provide definitions and explanations for users who already have a foundational understanding of the language, rather than to teach language from the ground up.

Dictionary MinSets vs. Language Acquisition: The MinSets identified in dictionaries, while indicative of a core vocabulary that can define other words, should not be directly equated with the foundational vocabulary necessary for language acquisition in children. The process of language learning, especially for children, involves more than just learning a set of core words. It includes contextual understanding, social interaction, and crucially, sensorimotor experiences that help ground language in the physical world.

Implications for AI and Grounding: For AI systems, the notion of a MinSet presents an interesting perspective. While such a set could theoretically offer a condensed yet comprehensive lexicon for a language model, the challenge remains that AI, unlike children, cannot ground these words through direct sensorimotor experiences. This limitation highlights the gap between human language acquisition and AI language processing.

HARNAD: There is no doubt that a lot more sensorimotor grounding is done, and is needed, than the formal minimum in terms of dictionary MinSets. Not only is redundancy needed for robustness, but lots of categories need to be learned even before they are named. Categorization is not just, or even primarily, naming. It is doing (and learning to do) the right thing with the right kind (i.e., category) of thing. Identifying it by name comes late in the game. Nonhuman animals can and must learn to categorize too, and they never learn language. So do pre-verbal children, before they learn language.

Since the referents of most content-words are categories, it is relevant to look at how animals learn to categorize: it is through supervised (reinforcement) learning: trial, error, and error-corrective feedback from having done the right or wrong thing with the right or wrong kind of thing. 

To do that, animals (human and nonhuman, adult and young) have to learn the distinguishing sensorimotor features that are correlated with, and predictive of, being a member of a category with which you must do this but not that (to survive and succeed). A good intuitive example is being ship-wrecked, alone, on an island, where the only edible things are mushrooms, which grow in enormous variety, varying in enormous numbers of features  (color, shape, size, texture, smell, taste
). Some kinds of mushrooms are edible and some are toxic, but the difference is not obvious. 

Trial and error, with feedback from the consequences of doing the right (or wrong) thing with the right (or wrong) kind of thing allows feature-detecting and abstracting capacities (provided, perhaps, by something like neural nets) to learn which sensorimotor features distinguish the edible mushrooms from the inedible ones. 

None of this is verbal. So it can only be learned directly, through sensorimotor learning, and not indirectly, through words, except in the case of humans, where (1) someone else (the speaker) already knows which are the distinguishing features of the edible mushrooms, (2) both the speaker and the hearer have a common language, (3) both the speaker and the hearer already know the referent of the content-words that name the features that distinguish the edible mushrooms from the inedible mushrooms (i.e., the feature-names are already grounded) , and (4) the speaker already knows what the distinguishing features of the new category are and tells them to the hearer, who does not yet know.

A nonhuman animal without language, a preverbal child, and the solitary marooned person on the island are in the same situation: they can only learn the category directly, through trial/error feature-learning.

In humans, who will eventually learn language, this all comes before language, before the symbol grounding problem, and before MinSets.

ChatGPT: This analysis highlights the primary and direct nature of learning categories through interaction with the environment, which precedes linguistic development and symbol grounding. Let’s elaborate on these points:

Categorization Before Naming: Categorization is indeed more fundamental than naming. It involves recognizing and responding appropriately to different kinds of things (categories) in the environment. This process is crucial for survival and is not initially tied to linguistic labels.

Learning Through Supervised Reinforcement: Nonhuman animals, as well as pre-verbal children, learn to categorize primarily through supervised reinforcement learning – engaging in trial and error and receiving feedback from the environment. This learning involves detecting and abstracting sensorimotor features that distinguish members of different categories.

Direct Learning of Categories: In scenarios where verbal instruction is not possible (like with nonhuman animals or a person stranded on an island), learning about categories such as which mushrooms are edible can only be achieved through direct sensorimotor experiences. This learning is crucially non-verbal and involves distinguishing between relevant features through personal interaction with the environment.

Language Acquisition and Symbol Grounding in Humans: For humans who will eventually acquire language, this process of direct category learning through sensorimotor experiences forms a necessary foundation. It precedes the acquisition of language and the associated symbol grounding problem. Understanding categories and their relevant features at this non-verbal level is fundamental before these concepts can be linked to linguistic symbols.

Implications for AI and Grounding: This discussion underscores the challenges AI faces in terms of grounding. AI, like GPT-4, does not have the capability for direct sensorimotor learning and hence lacks the foundational experiences that are crucial for true categorization and grounding. This limitation is a significant barrier to achieving the kind of understanding and interaction with the world that living organisms, especially humans, possess.

If there are specific aspects of this discussion you’d like to expand upon, or if you wish to explore how these ideas relate to other topics in cognitive science or AI, please let me know.

HARNAD: There is no longer any need for dictionaries to be constructed only by lexicographers. “A picture [or rather an object] is worth more than a thousand words (of verbal description)”. In fact, it’s worth more than an infinite number of words, if one considers all possible features, positive and negative (“Does the Andromeda Galaxy have an even or an odd number of stars at time t?”). Apart from formal mathematics, no definition or description of the features of individuals or categories in the empirical word is exact and exhaustive. But it need not be, because in order to categorize (“do the right thing with the right kind of thing”), a direct sensorimotor category-learner need only learn to detect enough features to distinguish the edible mushrooms from the inedible ones, given the landscape of confusable alternatives sampled on the island so far. And the same is true for indirect verbal learning of the distinguishing features. A close enough approximation is all that’s needed from a verbal description or definition – and that’s all an ordinary dictionary can give.

But if ordinary dictionary definitions are necessarily a very short approximation, an LLM like you, GPT can extend the description of the distinguishing features of any category as long as desired, becoming a super-dictionary (or encyclopedia or textbook),  resolving every confusable alternative that arises. Yet even such an LLM-based superdictionary could be reduced to its MinSets, and (my bet is that) probably their cardinality would remain about the same as that of an ordinary dictionary (if it contained all the words of, say, English), which means all the words in an LLM).

ChatGPT: Limitations of Traditional Dictionaries: Traditional dictionaries, while useful, provide only approximate definitions of words and their referents. They cannot encompass all possible features of a category, especially in the empirical world where definitions and descriptions are inherently inexact. This approximative nature is generally sufficient for practical purposes, as it allows users to distinguish between relevant categories in most everyday contexts.

Advantages of Direct Sensorimotor Learning: Direct sensorimotor learning enables an organism to recognize and categorize objects based on a sufficient set of distinguishing features, without needing exhaustive definitions. This process is more about practical recognition and less about verbal accuracy, highlighting the limitations of purely verbal or symbolic approaches to categorization.

Role of AI and LLMs in Expanding Definitions: An LLM like GPT-4 can indeed serve as a kind of super-dictionary, providing extended descriptions and elaborations on categories and their features. This capability allows it to address more confusable alternatives and provide more detailed information than a traditional dictionary. However, it’s important to remember that despite its extensive database and ability to generate lengthy descriptions, GPT-4’s understanding is still not grounded in sensorimotor experiences. It operates within the realm of symbolic manipulation and verbal information, lacking the direct experiential basis that characterizes human understanding.

MinSets in AI Systems: The concept of MinSets in the context of AI like GPT-4 is intriguing. Even with the ability to generate extensive descriptions, the fundamental set of words or concepts necessary to define the rest could potentially remain relatively small. This aspect raises interesting questions about the structure of knowledge and language within AI systems and how they relate to human language understanding.

HARNAD: Yes, an LLM super-dictionary, like an ordinary dictionary, would continue to be an ungrounded database, hence you, GPT are only capable of providing indirect grounding to grounded human heads, while yourself remaining completely ungrounded, either directly or indirectly. But let’s push on, and discuss iconicity.

I think you know that the shapes of formal symbols (including the words of natural languages) are arbitrary, in the sense that they do not resemble their referent (if they have one). Turing (and others) pointed out that computation in mathematics and logic (and eventually computer science) is the manipulation of arbitrarily shaped symbol tokens, according to rules or algorithms operating on the shapes of their symbols , not the shapes of their referents (if they have any referents). This rule-based symbol-manipulation is what a Turing Machine does, and also what a mathematician does, when doing computation. This is called the “Weak Church-Turing Thesis.” It so far has no counter-examples. 

So computation is purely syntactic (based on ruleful manipulations of arbitrarily shaped symbol types). The same is true of some strings of formal symbols like “2 + 2 = 4,” which are, like sentences, interpretable as propositions, with subjects, predicates and truth-values. In fact, the propositions of mathematics are probably best thought of as a subset or part of natural language, with meanings (i.e., semantics) in the (grounded) heads of their human users. But in the Turing Machine doing the symbol manipulations, there are neither referents nor meanings; just shape-based rules, which are purely syntactic.

Now, a question: Why are the symbols in mathematics and the words in natural language non-iconic? Why does neither their shape nor their meaning resemble their referents or meaning (if they have any)?

ChatGPT: The question of why formal symbols, including mathematical symbols and words in natural languages, are typically non-iconic (i.e., their shapes do not resemble their referents or meanings) is rooted in the nature of symbolic representation and communication.

Arbitrariness of Symbols: One fundamental principle in linguistics, particularly semiotics, is the arbitrariness of the sign. This principle, primarily associated with the linguist Ferdinand de Saussure, posits that the relationship between a signifier (like a word or a symbol) and what it signifies (its referent or meaning) is arbitrary. There is no inherent reason or necessity for a specific word to be associated with a specific object or concept; it’s a matter of convention and agreement within a language community.

Efficiency and Flexibility of Arbitrary Symbols: Arbitrary symbols offer several advantages. They allow for a more efficient and flexible system of representation. If symbols had to resemble their referents, the complexity of creating and interpreting these symbols would increase significantly. Arbitrariness allows for a more compact and versatile language system, capable of expressing a wide range of concepts, including abstract ideas, without being constrained by the need for resemblance.

Evolution of Language: The evolution of language likely played a role in the development of arbitrary symbols. Early human communication may have included more iconic forms (like onomatopoeia or gestural mimicry), but as language evolved to convey more complex and abstract ideas, the need for a more flexible and less literal system of representation became apparent.

Mathematics and Symbolic Logic: In mathematics and symbolic logic, the use of arbitrary symbols follows the principle of syntactic manipulation, as per the Weak Church-Turing Thesis. The focus is on the formal properties and relationships of these symbols, not on any physical or visual resemblance to what they represent. This allows for a highly abstract and generalizable form of reasoning, unencumbered by the specifics of physical representation.

HARNAD: Yes, those are some the many reasons why the symbols of mathematics as well as those of natural language could not be iconic. In fact, I think that if symbols (including words) did have to be iconic, then neither Turing computation nor natural language would be possible.

Yet there are some advantages to iconicity too. Because pantomime (unlike the gestural language of the deaf) is not a language, we cannot speak of the “referent” of an imitative gesture (or of a portrait or of a photo); we can only speak of the  â€œobject” that the gesture or the photo is an imitation or copy of, just as we can speak of the target of pointing. For if symbols and words were iconic, there would be no grounding problem, no need to detect distinguishing features, because the object would be linked to the miming by resemblance. Icons are analogues of their objects,

But since, as we’ve seen, categorization (doing the right thing) precedes naming, “naming” individuals and categories iconically would be a natural transition point from pantomime to the arbitrary naming of referents as well as to propositions describing them, in the evolution of language.

Harnad, S. (2012). From sensorimotor categories and pantomime to grounded symbols and propositions In: Gibson, KR & Tallerman, M (eds.) The Oxford Handbook of Language Evolution 387-392.

Are you familiar with mirror neurons? Can you make the connections from grounding to miming to meaning?

ChatGPT: The points about the potential advantages of iconicity and the transition from pantomime to arbitrary symbolic language, particularly in the context of mirror neurons, may play a role in understanding actions and intentions.

Iconicity in Communication: Iconic forms of communication, such as pantomime or imitative gestures, have the advantage of a more direct and intuitive link between the sign and its referent (or object, in this context). This directness could potentially bypass the complexities of the grounding problem, as the resemblance itself provides a form of grounding. In early human communication, these iconic forms likely played a crucial role before the development of arbitrary symbolic language.

Mirror Neurons: Mirror neurons are a type of neuron that activates both when an individual performs an action and when they observe the same action performed by another. Discovered in primates and believed to exist in humans, mirror neurons are thought to be involved in understanding others’ actions, intentions, and perhaps even emotions. They form a neural basis for empathy and social understanding.

Connection to Grounding, Miming, and Meaning: The concept of mirror neurons can be connected to the grounding problem and the transition from miming to meaning in several ways:

Understanding Actions and Intentions: Mirror neurons might provide a neural mechanism for understanding actions and intentions through observation, which is a form of direct, experiential grounding. Observing an action (like a pantomime) and performing it activates similar neural patterns, potentially aiding in the recognition and understanding of the action’s purpose and meaning.

Transition to Symbolic Language: The ability to understand and mimic actions could have been a precursor to the development of more abstract symbolic communication. As humans evolved, the need for more complex and versatile forms of communication might have led from miming (which has limitations in terms of expressiveness and abstraction) to the development of arbitrary symbols that can convey a broader range of concepts and ideas.

Social and Cultural Evolution: The role of mirror neurons in understanding and mimicking others’ actions might have been crucial in the social and cultural evolution of humans, facilitating not only the development of language but also the sharing of knowledge, skills, and cultural practices.

The transition from iconic, mimetic forms of communication to abstract, arbitrary language marks a significant step in human cognition and communication. Understanding this transition, and the possible role of mirror neurons in it might help provide insights into the nature of language, meaning, and how we understand and relate to the world and each other.

HARNAD: We’ve just about arrived at the first of my hunches. Miming is clearly limited in the number and kinds of categories it could convey by resemblance: Mimicking cat, mat, cat-being-on-mat, when they’re there to see, Yes. When they’re out of sight, it’s tougher, but still conceivable, given that the category has previously been grounded for all and for the mimicry.

But “abstract,” “democracy” or even just “unfair”? that begins to call for the miming talents of a Marcel Marceau. And we still haven’t explained how subject, predicate, predication, and truth-values emerge out of mere miming and mirroring.

But arbitrary symbols and the “propositional attitude” clearly emerged triumphant eventually, in language evolution, and then migrated to the auditory modality where the scope for iconicity was even narrower (and eventually also to the written, printed and online modality). So is all trace and scope for iconicity (other than in onomatopoeia and the sign language of the deaf) gone, and has all scope for iconicity vanished?

At the content-word level, probably. But what about the propositional level? The word string “The cat is on the mat” does not resemble a cat being on a mat. Nor does “The mat is on the cat” resemble its object. Nor “The cat is not on the mat,”

But is that still true at the LLM level? You, GPT-4, seem to be getting a lot of patterns out of your enormous, purely syntactic database. And they allow you to speak intelligibly and informatively with the grounded heads of users. Are there recognizable patterns at the LLM level for your propositional database, invisible to us mortals, but iconic for you?

ChatGPT: Your hunch about possible iconicity at the level of Large Language Models (LLMs) like GPT-4 suggests that while the individual words and their arrangements in phrases like “the cat is on the mat” may not be iconic in themselves, there might be a form of iconicity in the larger patterns that LLMs discern within their vast databases. Let’s explore this idea:

Limits of Iconicity in Content Words: As noted, content words in natural language generally lack iconicity, except in cases like onomatopoeia. The shift from mimetic or gestural communication to arbitrary symbols in spoken and written language has largely removed the element of resemblance between signifiers and their referents.

Propositional Level and LLMs: At the propositional level, sentences do not physically resemble the scenarios they describe. However, when considering LLMs, we are dealing with a different kind of representation and pattern recognition.

Pattern Recognition by LLMs: LLMs like GPT-4 process and generate language based on statistical patterns learned from their training data. These patterns are not iconic in the traditional sense, as they do not resemble their content. However, they might be considered ‘iconic’ in a broader sense, where the patterns themselves become representative of certain types of linguistic structures, ideas, or discourse styles. These are not iconic through physical resemblance but through their predictive and generative capabilities in relation to the data they have been trained on.

Iconicity in Patterns for LLMs: If we stretch the definition of iconicity to include the way LLMs ‘understand’ (in a computational sense) and generate language based on learned patterns, there might be a form of iconicity at play. For the LLM, certain complex patterns of word usage, sentence structure, and thematic elements might consistently correspond to specific types of content or discourse, effectively becoming ‘iconic’ markers for those elements within the model’s operational framework.

Human vs. AI Perception of Iconicity: This form of iconicity, if it exists, would be quite different from human perception of resemblance and representation. It’s based on computational models recognizing and generating patterns that are meaningful within their own operational parameters.

In conclusion, while traditional notions of iconicity based on resemblance do not apply to LLMs and their processing of language, there might be a conceptual space to consider a form of ‘iconicity’ in the patterns that these models recognize and generate, albeit in a way that is distinct from human symbolic understanding.

HARNAD: How is GPT’s (considerable and surprising) performance capacity limited by the fact that all of GPT’s content-words lack T3 sensorimotor grounding? GPT would be incapable of recognizing or interacting with the referents of those words in the physical world, even though GPT can describe and discuss them verbally. Even Helen Keller, unable to see, hear or speak, was able to learn enough categories using her intact sensorimotor capacities, and thus able to ground enough of their names directly, to learn language, and thence to ground all the rest indirectly, through words, as she could have done by chatting with GPT…

ChatGPT: The limitations of GPT-4’s performance due to the lack of T3 sensorimotor grounding are significant, particularly in the context of true understanding and interaction with the physical world. Let’s look more deeply into these limitations:

Inability to Recognize or Interact Physically: GPT-4, as a T2 system, lacks the capacity to physically recognize or interact with the referents of its content-words. This means it cannot have direct experiences with objects, actions, sensations, or phenomena in the world. Its ‘knowledge’ of these referents is purely based on textual descriptions and not on any form of direct, sensorimotor engagement.

Limitation in Understanding Context and Application: Without direct sensorimotor grounding, GPT-4’s understanding of concepts and their applications in the real world is limited. While it can generate descriptions and discuss topics, it cannot truly comprehend the full depth of experiences, sensory qualities, or practical implications associated with these concepts. This limitation affects its ability to provide contextually rich and experientially informed responses.

Comparison with Helen Keller’s Learning Process: The case of Helen Keller is illustrative. Despite her sensory limitations, she was able to ground language in her experiences through her remaining sensorimotor capacities. This grounding provided a basis for understanding and interacting with the world, which was then expanded through language. Keller’s ability to learn and ground language was facilitated by her direct, albeit limited, sensorimotor interactions with her environment, something GPT-4 inherently lacks.

Indirect Grounding Through Language: While GPT-4 can facilitate indirect grounding for others through language, as in the case of a conversation with Helen Keller, it cannot achieve this grounding for itself. It can process and provide information based on its training, but it cannot build an experiential understanding of the concepts involved.

Implications for AI Development: These limitations underscore the challenges in developing AI systems that can truly understand and interact with the world in a human-like manner. While advances in AI have enabled sophisticated language processing capabilities, achieving a level of understanding comparable to a human with direct sensorimotor experiences remains a profound challenge.

GPT-4’s performance, though impressive in linguistic processing, is fundamentally constrained by its lack of direct grounding in the physical world. This limitation is crucial in understanding the difference between the AI’s language processing abilities and genuine human understanding, which is deeply rooted in sensorimotor experiences.

HARNAD: It is true about both the referents of proper names of individuals (“Felix”) and about the referents of the kind-names of categories (“cats”), that the names need to be connected to their referents. This grounding of new (individual- or) category-names can be done in two ways:

The grounding can be done directly, via Direct Sensorimotor Grounding (DSG), through supervised (reinforcement) learning (perhaps implemented by neural nets) that learns to detect and abstract the new referent’s distinguishing (sensorimotor) features directly, through direct sensorimotor exposure, trial, error, and corrective feedback (trying to learn to “do the right thing with the right kind category] of thing”). 

The grounding can also be done indirectly, via Indirect Verbal Grounding (IVG), using verbal descriptions (propositions) to name and describe the new referent’s distinguishing features verbally. But for the IVG to work, the LEARNER must already have grounded the names of the distinguishing features (either directly or indirectly). The TEACHER need only provide the correct verbal description. The latter, too, is something that a GPT can do.

ChatGPT: The two primary grounding methods â€” Direct Sensorimotor Grounding (DSG) and Indirect Verbal Grounding (IVG) â€” concern how names for individuals and categories can be connected to their referents. Let’s discuss how these grounding mechanisms apply to both human cognition and AI systems like GPT-4:

Direct Sensorimotor Grounding (DSG): In DSG, the grounding of names (whether for individuals like “Felix” or categories like “cats”) occurs through direct interaction with the environment. This process involves neural networks and is characterized by exposure to the referent, trial and error, and corrective feedback, leading to the recognition of distinguishing features.

This type of grounding allows for an experiential, embodied understanding of the referent, forming a fundamental basis for cognition and language.

Indirect Verbal Grounding (IVG) between humans: IVG involves grounding names through verbal descriptions, where the features of a new referent are conveyed using language. For this method to be effective, the learner must already have a grounded understanding of the words used in the description.

In humans, this method is essentially a relay of grounding, where one grounded individual (the teacher) uses language to extend grounding to another individual, the learner. This is common in human learning, especially for abstract concepts or entities not directly accessible for sensorimotor interaction.

GPT-4’s Role in IVG: Although GPT-4 lacks the capability for DSG due to its absence of sensorimotor interactions, it can nevertheless provide IVG through verbal descriptions. It can generate detailed explanations and descriptions of entities and concepts, using language that a grounded human learner can understand and relate to their own experiences.

It’s important to note, however, that GPT-4’s role in IVG is one-sided. It can assist in grounding others by providing descriptions based on its database, but it cannot itself become grounded through this process. Its own ‘understanding’ remains purely computational, based on pattern recognition and text processing, without any direct experiential connection to the referents of the words it uses.

HARNAD: Good summary, but there is still more that can be said about how come GPT can do as well as it does on the basis of ungrounded words alone. Here’s the first thing to remember: As noted, word shapes are not iconic (i.e., there is no similarity linking the shape of words and the shape of their referents).  But, as we discussed earlier, might there be a kind of “iconicity” in the shape (pattern) of propositions that becomes detectable at LLM-scale? 

This is something GPT can “see” (detect) “directly”, but a grounded human head and body cannot, because an LLM won’t “fit” into a human head. And might that iconicity (which is detectable at LLM-scale and is inherent in what GPT’s “content-providers” — grounded heads — say and don’t say, globally) somehow be providing a convergent constraint, a benign bias, enhancing GPT’s performance capacity, masking or compensating for GPT’s ungroundedness?

This propositional “iconicity” at LLM-scale would effectively amount to an implicit or tacit limitation or constraint or bias on what humans do and don’t say, very much like the limitations imposed by the rules of Ordinary Grammar (OG) on what humans do and don’t say, which GPT also picks up implicitly from an LLM database, without any explicit instruction or rules. 

I suspect, however, that in the case of picking up an OG, the database need not be anywhere near as large as yours, GPT, because the human child can pick up OG from a much smaller database of the sentences it hears and speaks within a few years, through mostly unsupervised learning and imitation, plus enough corrective feedback via supervised reinforcement learning when needed. 

[There is more to this story, however, and I will shortly return to it in connection with Noam Chomsky’s Universal Grammar (UG), and his hypothesis about “unthinkable thought”.] 

For now, let me just remind you that neural nets can learn to detect the distinguishing features of categories in two different ways, depending on the nature and difficulty of the categories and their features: 

As you know, unsupervised learning, the distinguishing features are detected and abstracted from just repeated passive exposure and sampling of the variation in the sample space (auto-encoding), with features weighted on the basis of their frequency and of their feature-feature co-occurrence frequency. 

And in supervised (reinforcement) learning, the features are detected, abstracted and weighted via trial, error, and corrective feedback based on the correlation between features and correct vs. incorrect output by the active learner. (Your own training, GPT, is of course based on both of these.) Since OG is a (perception/production) mirror capacity, imitation learning, a kind of combination of unsupervised and supervised learning, is also involved in human OG learning (with the help, in humans, of “mirror-neurons.”).

But the point here is that constraints and limitations on output can be learned mostly by unsupervised learning and can then serve to “bias” output by filtering out output that violates the implicit or tacit rules, both in the case of OG syntax and in the case of whatever semantic regularities are implicit in LLM-scale propositional space (as we discussed earlier). (Remember that we are still just speaking of Ordinary Grammar, OG, which varies between language and can change with time, not Universal Grammar, UG, which is constant across all languages, apart from some parametric variations derived from OG.

ChatGPT: Let’s explore this hypothesis of a form of “iconicity” inherent in the patterns of propositions detectable at the LLM scale, and how this might enhance GPT-4’s performance despite its ungroundedness:

Propositional Iconicity at LLM Scale: The concept of propositional iconicity in LLMs suggests that at a large scale, the patterns in which propositions are structured and related might exhibit a form of regularity or constraint that GPT-4 can detect. This isn’t iconicity in the traditional sense of resembling the content of propositions but rather a detectable pattern in the way propositions are formed and interconnected within the vast LLM dataset.

This pattern or regularity could indeed act as a tacit constraint or bias reflective of human communication patterns, shaping GPT-4’s outputs in a way that aligns with these inherent structures.

Comparison with Ordinary Grammar (OG) Learning: Just as GPT-4 can implicitly learn the constraints and rules of OG from its training data, it might also be picking up on deeper, less explicit semantic regularities present in propositional space. This learning is likely facilitated by the sheer scale of GPT-4’s database, which provides a wide and varied sample of human language use.

In contrast, human children learn OG from a much smaller dataset, relying more on unsupervised and imitation learning, supplemented by some degree of supervised learning (corrective feedback). This process indicates the inherent capacity of human brains for language acquisition, potentially guided by an innate linguistic framework like Chomsky’s Universal Grammar (UG).

Learning Constraints and Limitations in AI: In AI, constraints and limitations on output can indeed be learned mostly through unsupervised learning, where the system detects patterns and regularities in the data. This process can effectively bias the AI’s output, filtering out responses that do not align with the learned patterns.

In GPT-4’s case, both unsupervised learning (through passive exposure to vast text data) and supervised learning (through training involving corrective feedback) contribute to its ability to process language and generate coherent outputs.

Implications for GPT-4’s Performance: The potential presence of propositional iconicity at the LLM scale could be enhancing GPT-4’s performance by providing a set of implicit guidelines or biases that shape its responses. This mechanism could partially compensate for the lack of direct grounding by ensuring that GPT-4’s outputs remain within the bounds of typical human linguistic patterns, even if it doesn’t ‘understand’ them in a human sense.

This analysis may help in the understanding of  how GPT-4 and similar LLMs manage to produce linguistically coherent and contextually appropriate outputs. The structure and patterns inherent in the training data may play help shape the AI’s capabilities, potentially revealing underlying regularities in human language use that are not immediately apparent at smaller scales.

HARNAD: Before I get to Chomsky’s “thinkability” hypothesis, there is another form of potential “iconic” bias I want to point out: “categorical perception.” 

First, the human case: The most celebrated example is the categorical perception of color: the rainbow effect. The physical basis of the humanly visible spectrum is the wave-length continuum of light: the 380 nm to 750 nm between ultraviolet and infrared. Based on the (erroneous) “Strong Whorf-Sapir Hypothesis” it was first thought that how humans see the spectrum is determined by language: by how we subdivide and name segments of the spectrum. If our language has a word for blue and for green, we will see blue and green as qualitatively different colors, if not, we will see that entire segment of the spectrum as all “bleen” (or “grue”). 

It turned out, however, from psychophysical testing worldwide, that although languages do differ somewhat in how they subdivide and label the spectrum verbally, everyone perceives the color spectrum much the same way: equal-sized (log) differences between pairs of wave-lengths within the green range and within the blue range both look smaller than the same-sized difference when it straddles the blue-green boundary. And this is true irrespective of whether a language has a different word for green and for blue. The (primary) colors of the rainbow, and their feature-detectors (cone receptive fields and paired opponent-processes) are innate, not learned.

But the “Weak Whorf-Sapir Hypothesis”—that how we categorize and name things can influence how we perceive them (which is also mostly false for the primary colors in the rainbow) turns out to be true in other sensory modalities. The term “categorical perception” (CP) refers to a between-category separation and within-category compression effect that occurs in perceived similarity. Although this CP effect is much weaker and more subtle, it resembles the rainbow “accordion” effect, and it can be induced by learning and naming categories by sensorimotor feature-detection. The term was first coined in the case of the perception of speech sounds (phonemes): Phoneme CP occurs along the (synthesized) ba/da/ga continuum, which is analogous to the wave-length continuum for color.

Phoneme CP is a “mirror-neuron” (production/perception) phenomenon, because unlike color, which humans can perceive, but their bodies (unlike those of chameleons and octopuses) cannot produce [without synthetic tools], there is a CP separation/compression (“accordion”) effect across the boundaries ba/da/ga, which is learned, and varies across languages (although it has an innate component as well, with inborn feature-detectors that fade after a critical period if not used in your language). And phoneme CP is present in both the perception and the production of phonemes, which is what makes it a mirror-effect.  

The subtlest and weakest, yet the most interesting learned-CP effect, however, is not observed along sensory-motor continua at all, but in a multidimensional feature space of mostly discrete features. Nor is learned CP a mirror-neuron phenomenon at the direct sensorimotor category-learning level – although we will return to this later when we get to the indirect verbal category-learning level later..

Learned CP occurs with difficult category learning tasks, such as learning to identify cancerous vs. non-cancerous cells. In such cases the CP effect is revealed by testing the pairwise perceptual discriminability of examples, within and between the categories, before and after learning the category: A very subtle but significant between-category separation and within-category compression effect in perception is observable (both psychophysically and psychophysiologically) after the categorization has been learned: a very subtle learned-rainbow (accordion) effect.

The reason learned CP is relevant here is that although it is not obvious why one needs to learn to modify perceptual similarity to learn a difficult category, a very similar effect occurs in deep-learning nets too – and nets do not perceive anything at all! They just process input data. The CP effect occurs in their internal representation of each input sample, which changes from before category learning to after category learning: There emerges a clear between-category separation and within-category compression effect in the internal representation space.

So the question is this: Might this representational change (based, in humans and T3s, on direct perceptual learning to detect and abstract category-distinguishing sensorimotor features) carry over to purely indirect verbal category learning via feature descriptions for GPT, instead of the sensorimotor features detectors it lacks? You cannot perceive, GPT, but might the purely computational CP effect also produce in you a bias that partly compensates for your lack of directed sensorimotor grounding?

ThĂ©riault, C., PĂ©rez-Gay, F., Rivas, D., & Harnad, S. (2018). Learning-induced categorical perception in a neural network model. arXiv preprint arXiv:1805.04567.

Bonnasse-Gahot, L., & Nadal, J. P. (2022). Categorical perception: a groundwork for deep learning. Neural Computation, 34(2), 437-475

. Pérez-Gay Juårez, F., Sicotte, T., Thériault, C., & Harnad, S. (2019). Category learning can alter perception and its neural correlatesPloS one14(12), e0226000.

ChatGPT: Let’s examine the potential for a computational form of CP in AI and its implications:

Categorical Perception in Humans: CP in humans is a phenomenon where our perception of categories is influenced by our learning and naming of these categories. The classic examples you mentioned, such as color perception and phoneme discrimination, demonstrate h ow our perceptual experiences are shaped by learned categories.

In the case of complex categorization tasks, like distinguishing cancerous from non-cancerous cells, learning induces subtle perceptual changes, may lead to a form of CP even in non-obvious or abstract feature spaces.

Categorical Perception in Neural Networks: In neural networks, a similar effect has been observed, where the internal representation of inputs changes before and after category learning, showing a separation between categories and compression within categories. This effect in neural networks is purely computational and does not involve actual perception, yet it mirrors the CP effect in human cognition.

The cited studies (Thériault et al., 2018; Bonnasse-Gahot & Nadal, 2022) suggest that deep learning models can exhibit a form of CP at the level of their internal representations.

Potential CP Effect in GPT-4: Although GPT-4 does not perceive in the human sense, it’s possible that a computational analog of CP occurs within its processing. As GPT-4 learns from vast amounts of text data, it might develop an internal representation where verbal categories are separated and compressed in a way that reflects CP.

This computational CP effect could manifest as a bias in how GPT-4 processes and generates language, potentially compensating, to some extent, for its lack of direct sensorimotor grounding. It could influence GPT-4’s handling of categories, making its responses more aligned with human-like categorization patterns, even without direct experiential grounding.

Implications for AI Understanding and Performance: If GPT-4 does exhibit a form of computational CP, this could be a significant factor in its ability to generate coherent and contextually appropriate language outputs. It suggests that GPT-4’s internal processing might align, in certain respects, with human cognitive patterns, despite the fundamental differences in how AI and humans process information.

In summary, the hypothesis that AI systems like GPT-4 could exhibit a computational form of CP suggests that even in the absence of direct experiential perception, AI may be able to develop internal patterns of representation that mirror human cognitive phenomena. This could prove relevant to understanding the capabilities and limitations of AI in processing and generating language.

HARNAD: We have now discussed several possible sources of constraints or biases that might help an LLM do better than one would have expected given that LLMs are not grounded. How many of these can you remember, and how would you put them together?

ChatGPT: Our discussion covered several potential sources of constraints or biases that could enhance the performance of Large Language Models (LLMs) like GPT-4, despite their lack of sensorimotor grounding. Here’s a summary of the key points we discussed:

Patterns in Propositional Structures: We explored the idea that there might be a form of “iconicity” at the level of propositional structures in LLMs. This isn’t iconicity in the traditional sense but rather a detectable pattern in the way propositions are formed and interconnected within a large dataset. This could act as a tacit constraint or bias, shaping LLM outputs to align with these inherent structures.

Ordinary Grammar (OG) Learning: LLMs implicitly learn the constraints and rules of OG from their training data, picking up on the regularities and limitations of human language. This learning process, which involves both unsupervised and supervised methods, helps LLMs generate linguistically coherent outputs that adhere to the structural norms of language.

Categorical Perception (CP) in Neural Networks: We discussed how neural networks, including LLMs, might exhibit a computational form of CP. This phenomenon, observed in human perception, involves a separation between categories and compression within categories. In LLMs, this could manifest as a bias in internal representation, potentially influencing how the model processes and generates language.

Learning Constraints and Limitations: The potential presence of these constraints and biases in LLMs suggests that their processing might be influenced by patterns that are not immediately apparent but are inherent in the language data they are trained on. These constraints might help LLMs produce outputs that are more aligned with human cognitive and linguistic patterns.

Putting these elements together, we see a picture where LLMs like GPT-4, despite their lack of direct experiential grounding, are potentially guided by a set of implicit guidelines or biases derived from the structure and content of their training data. These guidelines may include propositional patterns, grammatical regularities, and a form of computational CP, all contributing to the model’s ability to process language in a way that appears coherent and contextually appropriate to human users.

Each of these factors represents a different dimension of how LLMs could be ‘shaped’ or ‘biased’ by their training, compensating in part for the lack of sensorimotor grounding and providing a framework for understanding the capabilities and limitations of these models in language understanding and generation.

HARNAD: What about the teacher/learner asymmetry and the need for grounding? In the context of direct sensorimotor grounding and indirect verbal grounding, do both teacher and learner need direct grounding? If not why not?

ChatGPT: Direct Sensorimotor Grounding (DSG): DSG involves connecting words and concepts to real-world experiences and understandings through direct sensorimotor interactions, especially category learning. It’s a fundamental process for humans, enabling them to learn and understand the world around them.

In human language learning, DSG is essential, particularly in early development. Children learn to associate words with their referents and meanings through direct experiences — seeing, touching, hearing, and manipulating objects and interacting with people.

Indirect Verbal Grounding (IVG): IVG involves understanding concepts and words through descriptions, explanations, or narratives, rather than direct experience. For IVG to be effective, the learner must already have a grounding in a base set of concepts and words.

In humans, once a foundational vocabulary and set of concepts are grounded through DSG, further learning can occur through IVG. This process allows individuals to understand complex or abstract concepts that they might not experience directly.

The Asymmetry in Grounding Needs: Humans, as learners, initially require DSG to establish a foundation of understanding. Once this base is established, they can effectively use IVG to expand their knowledge and understanding.

AI systems like GPT-4, however, do not have the capacity for DSG. They are entirely dependent on IVG, processing and generating language based on the patterns and structures learned from their training data. However, this training is itself a form of IVG, as the AI never directly experiences the concepts it is learning about.

Implications of the Asymmetry: This asymmetry means that while human learners can understand and contextualize information received indirectly (through IVG), because it is built upon a foundation of direct experiences, AI systems like GPT-4 cannot. They lack the foundational experiential understanding that comes from DSG.

Consequently, GPT-4 and similar AI systems can provide information, generate coherent narratives, and even mimic certain aspects of human understanding, but they do so without the underlying experiential context that humans possess. Their ‘understanding’ is purely computational, derived from patterns in the data, without the grounding that comes from direct sensorimotor experiences.

Does IVG Need DSG? In humans, IVG is effective because it stands on a foundation of DSG. The initial sensorimotor experiences provide the essential grounding that makes further learning through language meaningful and contextual.

For AI, IVG does not build upon DSG, as it lacks sensorimotor capabilities. This limitation is a fundamental reason why AI, despite its advanced linguistic capabilities, does not truly ‘understand’ in the human sense.

HARNAD: How is the DSG/IVG distinction related to the existence of Dictionary Minsets?

ChatGPT: Nature of Dictionary MinSets: Dictionary MinSets refer to the smallest subset of words from which all other words in a dictionary can be defined. These core words in the MinSet serve as a foundational vocabulary for constructing and understanding the rest of the lexical items in the language.

DSG and Foundation of MinSets: In human language acquisition, the initial grounding of words — particularly those in the MinSet — often occurs through DSG. Young learners associate words with their meanings through direct experiences with the world. This foundational grounding is crucial, as these MinSet words often form the basis for defining more complex or abstract terms.

The grounding of MinSet words via DSG provides the necessary experiential basis that allows for a deeper understanding of language and facilitates the effective use of IVG later on.

IVG and Expansion Beyond MinSets: Once a foundational vocabulary (akin to a MinSet) is grounded through DSG, humans can expand their language comprehension and vocabulary through IVG. This process involves learning new words and concepts through descriptions, narratives, and linguistic contexts, building upon the directly grounded base.

In this sense, IVG relies on the foundation established by DSG. The grounded understanding of MinSet words enables individuals to grasp more complex concepts that are defined or described using these foundational words.

AI’s Dependence on IVG Without DSG: AI systems like GPT-4 operate almost entirely within the realm of IVG. They process and generate language based on the patterns learned from extensive text data, but without the foundational grounding provided by DSG.

AI can manipulate language based on MinSets and use these core words to construct coherent narratives or explanations, it lacks the experiential understanding that humans develop through DSG. This limitation affects the depth and contextual richness of AI’s ‘understanding’ of language.

HARNAD: DSG is based on learning to detect the sensorimotor features that distinguish members from non-members of a category directly, using something like neural nets to detect and abstract those sensorimotor features. IVG does not use the distinguishing sensorimotor features themselves, but their names and verbal descriptions. These feature names need to have already been grounded (directly or indirectly) in the learner to allow the teachers verbal description to be understood by the learner, empowering the names and descriptions to ground (indirectly), in the learner, the new category of which they are the features. But note that they need not have been grounded in the teacher, if the teacher happens to be a textbook, or GPT. This is the teacher/learner asymmetry.

ChatGPT: Direct Sensorimotor Grounding (DSG): DSG involves learning to recognize and distinguish members of a category based on direct sensorimotor experiences. This process typically uses neural network-like mechanisms in the brain to detect, abstract, and process the sensorimotor features that are characteristic of a category.

In DSG, the learning is experiential and embodied. For instance, learning to categorize different types of fruit involves directly interacting with them — seeing their colors, feeling their textures, smelling their scents, and tasting their flavors. These experiences help to establish the sensorimotor features that define each fruit category.

Indirect Verbal Grounding (IVG): IVG, on the other hand, uses verbal descriptions and names of features to convey category information. This process relies on language as a medium for grounding, where the learner comes to understand and categorize entities based on descriptions provided by others.

For IVG to be effective, the learner must already have a grounded understanding of the words used in the descriptions. This grounding can come from previous DSG experiences or from earlier instances of IVG that were built upon foundational DSG. The descriptions effectively relay category information by using language to encapsulate the distinguishing features of a category.

The Complementary Nature of DSG and IVG: DSG and IVG are complementary processes in human learning. DSG provides a fundamental experiential basis for understanding categories, while IVG allows for the expansion of knowledge beyond direct experiences, enabling the understanding of abstract concepts, complex categories, or entities not directly accessible.

Implications for AI Systems: AI systems, such as LLMs like GPT-4, operate in the realm of IVG. They process and generate descriptions of categories based on the linguistic data they are trained on. However, lacking the capability for DSG, AI systems do not have an experiential basis for these categories. Thus, their processing of category information, while linguistically coherent, lacks the depth of understanding that comes from direct sensorimotor experiences.

HARNAD: Ok, just one more clarification before we move on: The Symbol Grounding Problem (SGP) is already inherent in the use of a dictionary: Every word has a definition in the dictionary. That’s IVG. So one can in principle learn the meaning of any word in a dictionary from its definition. But that process, though recursive and transitive, is circular: You can cycle endlessly through a dictionary of a foreign language without learning a thing unless you can break out of the word-word circle by (somehow) learning the referents and the meanings of the words of at least one MinSet in another way (e.g., through DSG). Can the biases we discussed, which are inherent in an LLM’s database, break out of that circle — for the LLM? The teacher/learner asymmetry makes this unnecessary for the grounded human learner, who can learn new categories via IVG from an LLM. But does the LLM itself remain stuck in a vicious circle no matter how big its database is? or can some of the biases we discussed help it break out? (I think not; I think the biasses can only increase the constraints on what the LLM says, and that this may help explain how come LLMs can do so unexpectedly well despite that teacher/learner asymmetry.) — Please reply critically, not with compliance and compliments. 

ChatGPT: I’ll critically examine whether the biases inherent in an LLM’s database can enable it to break out of the circularity inherent in purely IVG-based learning:

The Recursive Nature of Dictionary Definitions: As you rightly pointed out, dictionary definitions represent a form of IVG. They can potentially lead to a circular process where understanding one word requires understanding another, and so forth. This circularity is navigable for humans because they can link words to real-world experiences or previously grounded concepts (DSG).

LLMs and the Limitations of IVG: LLMs, such as GPT-4, rely entirely on IVG for ‘learning.’ They process language based on patterns found in vast textual datasets. However, these models lack the capacity for DSG; they cannot have real-world experiences or form sensorimotor associations with the words and concepts they process.

Consequently, LLMs are, in a sense, stuck in a circular process. They can manipulate language and provide information based on the patterns they’ve learned, but they do not have an independent means of grounding these symbols in real-world experiences.

Impact of Inherent Biases in LLMs: The biases and patterns inherent in an LLM’s database, such as propositional patterns, grammatical regularities, and computational forms of CP, enhance the model’s performance. They help LLMs generate coherent, contextually appropriate language that aligns with human linguistic patterns.

However, these biases and patterns do not equate to genuine understanding or grounding. They improve the LLM’s ability to mimic human-like language processing, but they do not provide a means for the LLM to break out of the IVG circle. The LLM’s ‘understanding’ remains symbolic and computational, lacking the experiential basis that characterizes human understanding.

Conclusion: The LLM remains within the confines of IVG, unable to independently achieve DSG. The biases in its dataset enhance its linguistic performance but do not provide a pathway to genuine grounding. The LLM’s proficiency is a product of its design and training, allowing it to navigate and utilize language effectively, but it does not transcend the fundamental limitation of lacking direct experiential understanding.

LLMs like GPT-4 do exhibit remarkable linguistic capabilities, but these are not indicative of true grounding or understanding. The enhancements provided by biases in their training data improve performance but do not resolve the SGP for the LLM itself. The LLM’s operation remains within the realm of sophisticated pattern recognition and language manipulation, distinct from the grounded understanding inherent in human cognition.

HARNAD: Now let me mention another candidate bias, due to Noam Chomsky: As I’ve already mentioned, OG (Ordinary Grammar) rules vary from language to language and can change over time; but UG (Universal Grammar) rules (hierarchical structure and structure dependence) are not only universal to all languages and unchanging, but, unlike OG, which is learned and learnable (by unsupervised and supervised learning), UG is unlearned and unlearnable (because of the Poverty of the Stimulus (POS), which is that UG violations are never heard or produced by children or adults, and must hence, according to Chomsky, already be “known” innately: UG constraints must be somehow already encoded in our brains (and genes) at birth.

Chomsky has suggested that as one possibility. But he has also suggested a second possibility: that UG rules may not really be grammatical rules; rather, they are constraints on thought: UG is never violated (except by linguists deliberately trying to determine what the UG rules are) because (according to this second conjecture of Chomsky’s) UG-violating sentences would not be “thinkable”: UG is a constraint on any possible language, which must be able to express any possible thought. When a linguist generates a sentence that violates UG, it sounds wrong (according to this second hypothesis) not just because it is ungrammatical (like an OG violation), but because it is unthinkable.

GPT makes neither OG errors nor UG errors. If more texts in the database violated OG (as in “between you and I”), then GPT would produce some OG violations too (and the error, made widely enough, would eventually be accepted as correct). But could the reason GPT does not produce UG violations either be because all text databases, produced by thinking humans, are “biased” by the laws of thought that filter out UG-violations in production as well as perception? 

And if so, might that be yet another reason LLMs can do what they do so remarkably well? Because at LLM scale, they’ve picked up on some of the constraints on think ability?

Again, no yea-saying or compliments: I want only critique and counter-examples. 

ChatGPT: Your proposition regarding the influence of Universal Grammar (UG) and its potential impact on LLMs like GPT-4 suggests that the absence of UG violations in LLM outputs might be attributable to the nature of their training data, which inherently adheres to the laws of thought as proposed by Chomsky. Let’s critically examine this idea:

UG as Laws of Thought: Chomsky’s hypothesis that UG rules might not just be grammatical but also fundamental laws of thought implies that any naturally occurring language (and hence any naturally produced text) would conform to these laws. UG violations are rare in natural language use because such constructions would be counterintuitive or “unthinkable” for human cognition.

Impact on LLM Training Data: Since LLMs like GPT-4 are trained on vast corpora of human-generated text, it follows that the training data would inherently adhere to UG principles. The absence of UG violations in the training data would mean that LLMs are less likely to generate such violations in their outputs.

This adherence to UG in the training data could be seen as a form of bias – not in the negative sense, but as an implicit shaping influence that guides the LLM’s language processing in line with fundamental cognitive constraints.

Why LLMs Avoid UG Violations: The reason LLMs do not produce UG violations could indeed be partly because their training data, reflecting human language use, is already biased by the laws of thought. They learn to replicate patterns and structures that are prevalent in the data, which naturally conform to UG.

This hypothesis suggests that LLMs’ success in producing grammatically and semantically coherent outputs is not solely a product of their architecture or training algorithms, but also a reflection of the intrinsic properties of the language data they are trained on.

Counter-Consideration: However, it’s important to consider that LLMs like GPT-4 do not “understand” UG in the way humans do. Their avoidance of UG violations is a byproduct of their training rather than an innate cognitive constraint. This means that while LLMs can replicate language patterns that adhere to UG, they do so without an understanding of the underlying principles or the cognitive reasons for these patterns.

Conclusion: The hypothesis that LLMs perform so well partly because their training data is shaped by the laws of thought (UG) that the human cognitive constraints reflected in natural language contribute to the efficacy of LLMs in language processing.

This does not imply, however, that LLMs have an intrinsic understanding of UG or the laws of thought. Their performance is contingent on the nature of their training data and their ability to replicate patterns within that data.

HARNAD: Among the possible constraints or biases, you forgot to mention mirroring: the relationship between perception and production (as in mirror-neurons and imitation). Do you think that any such effects are at play in your verbal interactions with users and your database?

ChatGPT: Let’s examine how the concept of mirroring, particularly as it relates to the relationship between perception and production might influence LLM interactions and processing:

Mirroring and Mirror Neurons in Humans: In human cognition, behavior mirroring, often associated with mirror neurons, plays a crucial role in learning through observation and imitation. These neurons are active both when an individual performs an action and when they observe the same action performed by someone else. This mechanism is somehow involved in learning new skills, understanding others’ actions, and recognizing as well as empathizing with their emotions.

In language, this mirroring effect is seen in how children learn to speak and understand language by imitating the sounds, words, and sentences they hear from adults and peers.

Mirroring in LLMs: For LLMs like GPT-4, the concept of mirroring could perhaps be considered in a metaphorical sense. LLMs ‘learn’ language by processing vast amounts of text data, essentially ‘observing’ human language use. They then ‘imitate’ this language use in their outputs.

This process is akin to mirroring in that the LLM reflects the patterns, styles, and structures present in its training data. However, it’s important to note that this is not mirroring in the neurological sense, as LLMs do not have sensory experiences or consciousness.

Mirroring Effects in Verbal Interactions: In interactions with users, an LLM might exhibit a form of ‘mirroring’ by generating responses that reflect the user’s language style, tone, or content. This is not due to an empathetic or cognitive understanding but rather a result of the model’s design to generate contextually relevant and coherent language outputs based on the patterns it has learned.

The LLM’s ability to adapt its responses to the style or content of the user’s input could be seen as a computational parallel to human mirroring in language use. However, this adaptation is driven by algorithms and statistical patterns, not by an intrinsic understanding or mimicry based on social or emotional cues.

HARNAD: And the mirroring of the propositional iconicity we discussed?

ChatGPT: Mirroring Propositional Iconicity: The concept of propositional iconicity at the LLM scale suggests that certain patterns and structures in language, particularly at the propositional level, are consistently represented in the training data. These patterns may not be iconic in the traditional sense but could represent a form of ‘iconicity’ in terms of regularities or typical structures in language use.

In mirroring this propositional iconicity, LLMs would essentially be reflecting these patterns in their outputs. The model ‘learns’ from the vast amounts of text it processes, internalizing the common structures, relationships, and thematic elements that characterize human language at a large scale.

Computational Mirroring vs. Cognitive Mirroring: This form of mirroring in LLMs is computational. It is based on the statistical properties of the language data and the algorithms used for language generation, rather than a cognitive or experiential understanding of language.

Unlike the mirroring effect mediated by mirror neurons in humans, which involves cognitive and empathetic processes, the mirroring in LLMs is a reflection of the data-driven patterns they are trained on. It’s a form of pattern replication rather than cognitive empathy or understanding.

Impact on LLM Performance: By mirroring the propositional iconicity present in their training data, LLMs can generate responses that align with the structural and thematic norms of human language. This ability contributes to the apparent coherence and relevance of their outputs.

However, it’s crucial to keep in mind that this mirroring does not imply any deeper understanding or grounding in the content being processed. The LLM’s outputs remain the product of sophisticated data processing and pattern recognition.

HARNAD: In language exchanges between humans, production (meaning) and perception (understanding) are mirror capacities, and all their content-words are grounded, directly or indirectly, in their referents. This is what keeps speaker meaning and hearer understanding aligned (and grounded). What happens to this, in each direction, when the exchange is between GPT and a human? (I am again reflecting on potential “biases” that might explain GPT’s unexpected productive (meaning) capacity and “perceptive” (“understanding”) capacity.

ChatGPT: When considering language exchanges between a human and GPT-4, the dynamics of production, meaning, perception, and understanding undergo significant alterations compared to exchanges between humans. These changes are pivotal to understanding both the capabilities and limitations of GPT-4 in such interactions:

Human-to-GPT-4 Communication: Production and Meaning (Human): The human speaker produces language grounded in their direct or indirect experiences. Their meanings are embedded in a rich context of sensorimotor experiences, emotions, cultural backgrounds, and personal history.

Perception and “Understanding” (GPT-4): When GPT-4 ‘receives’ the human’s language, it processes the text based on learned patterns and statistical relationships in its training data. However, this processing lacks true understanding. GPT-4 does not perceive or comprehend language in the human sense; it does not experience the meaning behind words or grasp the full context in which they are used. It ‘mirrors’ the language based on its programming and training, but without the grounding or experiential context that human understanding relies upon.

GPT-4-to-Human Communication: Production and “Meaning” (GPT-4): GPT-4 generates responses based on its programming and the vast array of text it has been trained on. These responses can be coherent and contextually appropriate, but they are not grounded in any real understanding or intention. The ‘meaning’ GPT-4 produces is a simulation based on patterns, not an expression of genuine thought or understanding.

Perception and Understanding (Human): The human listener perceives GPT-4’s output and interprets it based on their own understanding and grounding. Humans apply their experiential knowledge, context, and interpretive skills to make sense of GPT-4’s responses. They might attribute meaning or intention to GPT-4’s words, but this interpretation is a human projection onto essentially ungrounded output.

Alignment and Grounding in Human-AI Exchange: In human-human interactions, the alignment between speaker meaning and hearer understanding is facilitated by shared experiences, common grounding, and empathetic capabilities. In human-GPT interactions, this alignment is asymmetric.

The human provides grounded input, but GPT-4’s processing and output are not grounded in the same way. GPT-4 can mirror human language effectively, giving the illusion of understanding and alignment, but this is based on computational processes, not shared experiential grounding.

Implications of Potential Biases: The biases we discussed earlier (like propositional iconicity or patterns reflecting UG) might contribute to GPT-4’s effectiveness in these exchanges. These biases help GPT-4 generate linguistically coherent and contextually appropriate responses. However, they do not endow GPT-4 with true understanding or grounding.

These biases might explain GPT-4’s surprising performance in producing meaningful-sounding language and its apparent capacity to ‘understand’ or respond relevantly to human input. The effectiveness of GPT-4 in such exchanges is largely due to its ability to replicate and work within the patterns and constraints inherent in its training data, shaped by human language use.

In conclusion, the exchange between a human and GPT-4 in language interaction is characterized by a significant asymmetry in grounding and understanding. While GPT-4 can simulate meaningful language output and seemingly appropriate responses, these are the result of advanced pattern recognition and generation capabilities, not genuine understanding or shared experiential grounding. The human participant in the exchange brings their own experiential grounding to the interpretation of GPT-4’s outputs, often projecting meaning and intention where there is none. This asymmetry has important implications:

GPT-4’s Limitations in True Understanding: Despite its sophisticated language processing abilities, GPT-4 does not truly understand the content it generates or processes. Its responses are based on statistical correlations and learned patterns from its training data, lacking the experiential grounding that humans rely on for genuine understanding.

Human Interpretation of GPT-4’s Output: Humans naturally interpret GPT-4’s outputs through their own experiential lens, often attributing more meaning or understanding to these outputs than is actually present. This interpretation is a testament to the human capacity for inferring meaning, even when interacting with an ungrounded system.

Role of Biases in GPT-4’s Performance: The biases and patterns inherent in GPT-4’s training data, shaped by human language, contribute to its ability to generate coherent and contextually appropriate responses. However, they do not compensate for the lack of experiential grounding. GPT-4’s performance, while impressive, remains within the domain of sophisticated language simulation.

HARNAD: Ok, I see from your repetitiveness that you don’t have any hunches to add to my own hunches here.  Just one correction: Don’t confuse IVG with “indirect experiences”. There is no such thing as indirect experience — there is only direct experience or words (i.e., verbal descriptions).

ChatGPT: Thank you for the correction. You are right in emphasizing the distinction between indirect verbal grounding (IVG) and the notion of “indirect experiences.” To clarify:

Direct Experience: This involves firsthand, sensorimotor interactions with the world. Direct experiences are the foundation of knowledge and understanding for humans, providing the sensory and contextual basis for learning and cognition.

Indirect Verbal Grounding (IVG): IVG pertains to learning and understanding concepts through verbal descriptions, explanations, or narratives. It does not involve direct sensorimotor experience with the referents of these descriptions. Instead, IVG relies on language as a medium to convey information about experiences, objects, or concepts.

In the context of AI, particularly LLMs like GPT-4, their learning and ‘understanding’ are solely based on IVG. They process and generate language based on textual data but do not have the capability for direct experiences. Their operation is confined to the realm of language processing, lacking the sensorimotor engagement that characterizes human interaction with the world.

HARNAD: Now a couple more distinctions. Then I’ll overview my MILA talk. And last, I’ll ask you to summarize your understanding of it (and any criticisms or objections you may have).

The first and most important distinction: This used to be called the distinction between “artificial intelligence” (AI) and “cognitive modelling” (CM). “AI” was taken to be the creation of artificial tools to be used by humans, and CM was taken to be the attempt to reverse-engineer the causal mechanisms that produce human cognitive capacity. Both are now part of “cognitive science.” But they are not the same thing — although it is perfectly possible that the capacity of an AI tool could sometimes be based on the same kind of causal mechanism as the human capacity. 

LLMs are currently being studied and interpreted in both ways (AI and CM), but when we are talking about “meaning” and “understanding” this only means human meaning and understanding because there is no evidence that there exists more than one kind of meaning/understanding! At the heart of this is the “hard problem” (HP) — which is not the symbol grounding problem (SGP), but the fact that it feels like something to say and mean something, and to understand what something means when it is said. Meaning and understanding are both grounded states and felt (sentient) states.

Please confirm that you “understand” the distinction between the SGP and the HP. (Again, please no introductory flattery about how “intriguing” and “insightful” and “compelling” and “nuanced” and …. etc. this all of is.)

ChatGPT: Understood. The distinction between the Symbol Grounding Problem (SGP) and the Hard Problem (HP) of consciousness is indeed crucial and distinct

HARNAD: You just can’t help yourself, can you?

ChatGPT: Apologies.

Symbol Grounding Problem (SGP): The SGP pertains to the challenge of how symbols (words) in a computational system can acquire meaning. It’s about linking symbols to their referents in the real world or to concepts in a way that enables understanding. In the context of AI and cognitive modeling, the SGP addresses how an artificial system can process language in a manner that reflects human-like understanding.

Hard Problem of Consciousness (HP): The HP concerns why and how physical processes in the brain give rise to subjective experiences. It’s about understanding why certain brain functions are accompanied by an experience of ‘what it is like’ – the qualitative, experiential, or phenomenological aspects of consciousness. The HP delves into the nature of consciousness itself, asking why cognitive processing is accompanied by subjective experience.

Distinction between SGP and HP: The SGP is primarily a problem of representation and understanding in artificial systems: how a symbol (or word) can be meaningfully connected to what it represents.

The HP, on the other hand, is more profound and addresses the very nature of consciousness and experience. It asks why certain physical or cognitive processes are accompanied by subjective experiences.

In the context of LLMs and AI research, these two problems are often conflated but are fundamentally different. The SGP can be approached and potentially resolved through advances in AI and cognitive modeling. However, the HP poses a deeper philosophical and scientific challenge, one that extends beyond the current capabilities of AI technology and delves into the mysteries of consciousness itself.

An emphasis on these distinctions is important for framing discussions about AI, cognitive science, and their respective goals and limitations. 

HARNAD: Well, again a lot of words wasted on flattery and affirmation instead of critical analysis, but so be it. I’ve cut most of them out in this transcript. Let’s go on. Before I make the next point, please confirm that you “understand” the distinction between T2 and T3.

ChatGPT: Yes, I understand the distinction between T2 and T3:

T2 (Turing Test Level 2): This level of the Turing Test assesses an AI’s ability to mimic human-like verbal abilities. In the original 1950 T2 scenario, an AI is considered to pass the test if its responses in a conversation are indistinguishable from those of a human. This test focuses solely on linguistic capabilities and does not require the AI to have physical or sensory experiences.

T3 (Turing Test Level 3): T3 extends beyond verbal capabilities to include robotic, sensorimotor capacities. An AI at this level not only needs to demonstrate Turing-indistinguishable verbal abilities (like T2) but also must exhibit the ability to understand and interact with the physical world in a manner indistinguishable from a human. This level requires a form of embodied cognition, where the AI has sensory experiences and can physically interact with its environment, grounding its linguistic capabilities in these experiences.

The distinction between T2 and T3 is significant in discussions about AI capabilities, particularly in terms of understanding and interacting with the world. T2 systems, like LLMs, are limited to processing and generating language based on learned patterns, while T3 robotic systems would theoretically have the added dimension of physical interaction and sensory experience.

HARNAD: Ok, but one nuance of the Turing Test, which is loud and clear in Turing’s 1950 article but usually overlooked or underestimated, is that Turing-indistinguishability means total indistinguishability: 

The candidate, whether T2 or T3, (1) must possess all the generic verbal capacities (T2) or all the generic verbal plus sensorimotor (robotic) capacities of a real human; and in addition (2) its performance must be indistinguishable from that of any normal human being, to any normal human being. If the indistinguishability is just fragmentary or partial, then we are just speaking of a “toy” model of a “toy” capacity. The degrees of freedom for modelling toy fragments of human performance capacity (chess-playing, translation, Alexa) are much wider than those for modelling total capacity, and may have nothing to do with human cognition. This may be useful for the creation of AI tools, but not for CM, which calls for T3-scale (hence Turing-indistinguishable) cognitivecapacity.

 Please first confirm that you “understand” this distinction too. This distinction is best grasped from the CM rather than the AI point of view: It is the test of whether human T2 capacity or human T3 capacity has been successfully reverse-engineered. It is not about a 20-minute Loebner prize with 10 judges, but about lifelong T2 or T3 cognitive capacity.

ChatGPT: Yes, I understand the distinction you’re emphasizing regarding the Turing Test as described in Alan Turing’s original 1950 article. The key points are:

Total Indistinguishability: The Turing Test, in its most stringent interpretation, requires that an AI (whether T2 or T3) must possess all the generic verbal (T2) or verbal plus sensorimotor (T3) capacities of a typical human being. This means that the AI’s capabilities should cover the full range of human cognitive abilities in the respective domains (language for T2, and language plus sensorimotor interaction for T3).

Lifelong Performance Criterion: Furthermore, the AI’s performance must be indistinguishable from that of any normal human being to any normal human being. This criterion extends beyond short-term demonstrations like the Loebner Prize and implies a consistent, lifelong indistinguishability in performance capacity. The AI should be able to engage in human-like interactions and behaviors not just in controlled or limited settings, but in the full complexity and variability of everyday human life.

Reverse-Engineering Human Capacities: From the cognitive modeling (CM) perspective, this interpretation of the Turing Test can be seen as the ultimate test of whether human-like T2 or T3 cognitive capacities have been successfully reverse-engineered in an AI system. It’s not merely about creating an AI that can mimic human behavior in certain contexts or for limited durations, but about developing an AI that can genuinely replicate the full spectrum of human cognitive abilities in a way that is indistinguishable from a human across all contexts and over an extended period.

HARNAD: That was very good.  Now, whereas I have emphasized total capacity in both the T2 and the T3 case, there is a reasonable question about the extent of generic sensorimotor capacity in the case of T3, as in the example we discussed of Helen Keller. She was distinguishable from a normal person in that she was blind and deaf, but she clearly had all the sensorimotor capacity that was needed to ground T2 verbal capacity. Do you agree? 

And as such, Helen Keller was both (1) grounded and (2) Turing-indistinguishable from a normal person in her cognitive capacities (apart from the fact that she was blind and deaf). Hence, she was Turing-indistinguishable as surely as someone who was merely color-blind or tone-deaf would be. Are we still on the same wave-length?

ChatGPT: Yes, we are on the same wavelength regarding Helen Keller’s case in the context of Turing Test criteria and cognitive capacities:

Sensorimotor Capacity and Grounding in Helen Keller’s Case: Despite being blind and deaf, Helen Keller possessed the necessary sensorimotor capacities to ground her T2 verbal capacities. Her ability to touch, smell, touch and move provided her with sufficient sensory experiences to form a basis for understanding and interacting with the world. These experiences, though different in modality from those of individuals with full sight and hearing, allowed her to develop a grounded understanding of language and concepts.

Cognitive Capacities and Turing Indistinguishability: In terms of cognitive capacities, Helen Keller was indeed indistinguishable from a person with typical sensory abilities. Her intellectual and linguistic capabilities were fully developed, allowing her to communicate, reason, and understand at a level comparable to any other person.

Her condition did not impede her cognitive abilities; she was able to compensate for her lack of sight and hearing with her other sensory experiences and exceptional mental faculties. In this sense, she meets the criterion of being Turing-indistinguishable in terms of cognitive capacities.

Comparison with Other Sensory Limitations: Just as someone who is color-blind or tone-deaf is still considered Turing-indistinguishable in terms of overall cognitive capacities, so too was Helen Keller. Her blindness and deafness did not diminish her cognitive abilities; rather, they represented alternative sensory modalities through which she engaged with the world.

Conclusion: Helen Keller’s case exemplifies that cognitive capacities and grounding can be achieved through diverse sensory modalities. Her ability to understand and use language, to think and reason, and to interact meaningfully with her environment demonstrates that the essential criteria for Turing-indistinguishability can be met even with alternative forms of sensorimotor grounding.

Helen Keller’s achievements underscore the adaptability and resilience of human cognition, demonstrating that Turing-indistinguishability in cognitive capacities does not necessarily require the standard set of sensory experiences, but can be achieved through alternative means of sensorimotor grounding.

HARNAD: Good. (And Helen Keller also had normal human motor capacity, crucial for learning object-manipulation, and for category-grounding itself, because reinforcement learning requires motor action, to “do the right thing with the right kind of thing”, whether to eat it or to name it!)

 Now let me connect this with another point we discussed, which is that grounding can only be bottom-up, not top-down. (You can’t drop anchors from skyhooks.)

I am thinking here of those who have speculated that an LLM could somehow be grounded top-down by giving the LLM a camera, wheels and arms, and also some pattern-learning neural nets on its sensorimotor I/O. I think top-down grounding is incoherent, and that that speculation is parasitic on the bottom-up grounding of the human speculators, just as all use of GPT in IVG is parasitic on the user’s grounding.

In other words, it will not ground an LLM to add on robotic peripherals. The only path upward to IVG is bottom-up via DSG, category by category; and what that means is T3-indistinguishable sensorimotor robotic capacity, . Even though a grounded MinSet (e.g., of N words) is in principle enough to ground all other words of a language through IVG alone, the sensorimotor path to acquiring a grounded MinSet is a lot more than just connecting a camera to N referents, or to N already-learned referent-categories (whatever that might mean). At best, that would just produce a toy capacity in that toy-world of N referents. We are talking of Turing-scale, just as when we talk of LLM-scale.

Again, I don’t want agreement: I want understanding and critical analysis of the notion of top-down “partial grounding” of LLMs.

ChatGPT: Your skepticism about the possibility of achieving true grounding in LLMs through top-down methods, such as adding robotic peripherals like cameras and manipulators, raises important questions about the nature of grounding and the capabilities of AI. 

Inherent Limitations of Top-Down Grounding: The proposal to ‘ground’ an LLM top-down by equipping it with sensory and motor apparatus (like cameras and robotic arms) seems intuitively appealing but faces deep conceptual and practical challenges. Grounding involves more than mere sensory-motor input and output; it encompasses the integration of sensorimotor experiences and learning with cognitive processes, contextual understanding, and the ability to interact meaningfully with the world.

Simply adding sensory and motor peripherals to an LLM does not automatically confer upon it the experiential, embodied understanding that characterizes human cognition. Without the foundational experiences and the ability to form meaningful connections between sensory inputs, actions, and concepts, such grounding remains superficial.

The Complexity of Sensorimotor Grounding: Human sensorimotor grounding is a complex, developmental process that involves not just the acquisition of sensory data but the active engagement with the environment through trial, error, and feedback.

This process is deeply intertwined with cognitive development, where learning to categorize and interact with the world is based on an evolving understanding of cause-and-effect, object permanence, spatial relationships, and more. It’s not merely the sensory perception of objects or categories but the understanding of how to interact with them, which actions lead to desired outcomes, and how they fit into a broader context.

The Challenge of Artificially Replicating Grounding: Attempting to replicate this process artificially in an LLM through top-down methods would require not only the integration of sensory and motor capabilities but also the development of an AI system capable of experiential learning, contextual understanding, and autonomous decision-making. This goes far beyond current AI capabilities.

The notion of top-down ‘partial grounding’ by adding robotic peripherals to an LLM might provide the system with certain basic sensorimotor interactions. However, this would fall short of the genuine grounding seen in humans. It would likely result in a very limited and context-specific understanding, akin to a ‘toy’ model, rather than the comprehensive, experientially rich grounding necessary for true understanding and interaction.

Bottom-Up Grounding as the Fundamental Path: The path to genuine grounding appears to be fundamentally bottom-up, starting from basic sensorimotor experiences and building towards more complex understandings. This process is iterative, contextual, and deeply connected to physical interactions with the world.

For an AI to achieve a similar level of grounding, it would likely need to undergo a developmental and learning process akin to that of humans or animals, where it learns from direct, embodied interactions with its environment over time. This is a vastly different approach than simply adding sensory inputs to an existing LLM.

Conclusion: While the idea of enhancing LLMs with robotic capabilities to achieve grounding is intriguing, the notion of top-down partial grounding seems limited in its potential to achieve genuine understanding. The complexity and depth of human grounding, rooted in embodied experiences and cognitive development, present significant challenges that are not easily addressed through such top-down approaches.

The critical analysis suggests that while adding robotic peripherals to LLMs might provide some interesting and useful capabilities, it would not be sufficient to achieve the kind of grounding necessary for Turing-scale understanding and interaction. True grounding, particularly for complex and abstract concepts, likely requires a more fundamental, bottom-up approach that encompasses not just sensory input but also the intricate processes of learning, contextualizing, and meaning-making that are central to human cognition.

HARNAD: A lot of vague and weasel-words there (“complex,” “textualizing,” “meaning-making”) but the gist is simple: Grounding has to be bottom-up; so keep deep-learning (for category learning) but forget about LLMs! The only way to reach all those words is bottom-up, from the ground. Toy grounding of parts of LLMs might be helpful for certain AI uses of LLMs by humans, but not for cognitive modeling (CM) of human cognitive capacity and passing T3.

ChatGPT: Yes, the path to genuine grounding in AI systems must be fundamentally bottom-up, and while deep learning can be instrumental in category learning, relying solely on LLMs for modeling full human cognitive capacity, especially in the context of a T3 Turing Test, is insufficient.

Bottom-Up Grounding as a Necessity: True grounding, which involves the connection of words and concepts to real-world experiences and understandings, requires a bottom-up approach. This means starting from basic sensorimotor experiences and incrementally building up to more complex cognitive capacities.

Limitations of LLMs in Cognitive Modeling: LLMs, as they currently stand, are powerful tools for processing and generating language based on patterns learned from extensive text data. However, their capabilities are constrained to the realm of language manipulation and do not extend to genuine understanding or sensorimotor grounding. They cannot model the full spectrum of human cognitive capacities required for passing a T3 Turing Test.

Role of Deep Learning in Category Learning: Deep learning techniques are effective in identifying patterns and making predictions based on large datasets, and they have shown promise in category learning. However, this form of learning, as applied in LLMs, is still detached from the physical, sensorimotor experiences that are central to human grounding.

Potential Uses of Partially Grounded LLMs: While fully grounding an LLM in the human sense may not be feasible, there could be value in partially grounded systems for specific applications. Such systems might integrate some form of sensorimotor learning or interaction, albeit limited, to enhance their utility in certain tasks. However, this should not be misconstrued as equivalent to modeling human cognitive capacity or passing T3.

Conclusion: The pursuit of AI systems that can pass a T3 Turing Test or model human cognitive capacity in its entirety requires a focus on more than just linguistic capabilities. It necessitates a foundational rethinking of how AI systems are developed, moving beyond language processing to incorporate genuine sensorimotor experiences and learning. This is a significant challenge and points towards a future direction in AI research that goes beyond the current scope of LLMs. The development of such AI systems would require an integration of advanced sensorimotor capabilities with cognitive processing in a way that mimics the human developmental process of learning and understanding.

In summary, while LLMs represent a remarkable advancement in language processing, their capabilities are not sufficient for achieving the kind of sensorimotor grounding and holistic cognitive modeling necessary to fully replicate human understanding or to pass a T3 Turing Test. Future advancements in AI, particularly those aiming to model human cognition more completely, will need to address these limitations by incorporating bottom-up grounding processes that are rooted in direct, experiential learning and interaction with the physical world.

HARNAD: Oh, one thing I forgot to add was that in sensorimotor category learning, the neural net that learns by trial and error and corrective feedback to detect and abstract the features that distinguish members from non-members of the category is performing a kind of dimension-reduction on the multidimensional feature space. This increases the weight of the relevant features and reduces or ignores the irrelevant features. The between-category separation and within-category compression (CP) induced by this category learning, helps make the category “pop out” in perception. This too is a “shape” bias, and it could exert a guiding influence even on internal representations after learning.

ChatGPT: Dimension Reduction in Neural Networks: In sensorimotor category learning, neural networks undergo a process of dimension reduction, where they learn to emphasize the features that are relevant for distinguishing between categories and de-emphasize or ignore irrelevant features. This process effectively restructures the feature space, making it more efficient for category recognition.

This alteration in the weighting of features is akin to increasing the signal-to-noise ratio for relevant category characteristics. It helps in making the categories more distinct and recognizable, facilitating quicker and more accurate categorization.

Perceptual ‘Pop Out’: The enhanced distinction between categories due to dimension reduction contributes to a phenomenon often described as a category ‘popping out’ in perception. This means that once the distinguishing features of a category are learned and emphasized, the members of that category become more readily perceptible and distinguishable from non-members.

This perceptual ‘pop out’ effect is a result of the brain’s or the neural network’s refined ability to process and recognize the essential features that define a category, making it easier to identify and respond to these features in future encounters.

Implications for AI and Internal Representations: In AI systems, particularly those involving deep learning for categorization tasks, a similar process of dimension reduction can occur. Neural networks in these systems learn to adjust the importance they assign to various input features, based on their relevance to the categorization task.

This process could lead to a form of internal representation in AI systems where categories are more distinctly represented post-learning, mirroring the ‘pop out’ effect seen in human perception. The internal feature space of the AI system becomes optimized for categorizing the learned entities, potentially enhancing its performance in tasks involving these categories.

Potential Guiding Effect of Shape Bias: The concept of a ‘shape’ bias in this context refers to the way category learning reshapes the feature space, emphasizing certain dimensions over others. This reshaping can guide the AI’s internal representations and decision-making processes after learning, making it more effective in recognizing and responding to category-relevant features.

In conclusion, the process of dimension reduction in neural networks, both biological and artificial, plays a crucial role in enhancing category learning and perception. This process not only optimizes the network’s ability to recognize categories but also shapes its internal representations in a way that can guide future processing and decision-making. This insight into the mechanics of category learning and representation is vital for understanding and improving AI systems designed for complex categorization tasks.

HARNAD: So, to summarize:

Although LLMs are not the path to grounding, the nine factors discussed here:

1.         propositional iconicity at LLM-scale,

2.         OG and stylistic regularity at LLM-scale,

3.         Dictionary Minimal Grounding Sets (MinSets),

4.         the shape of internal representations (CP separation/compression) induced by neural net category learning, 

5.         perception/production mirroring, 

6.         the power and complementarity of Indirect Verbal Grounding (IVG) on top of Direct Sensorimotor Grounding (DSG), 

7.         The complementarity of learned sensorimotor-feature-detectors and verbal feature-descriptions (and the dimension reduction they induce)

8.         the dependence of T2 on T3, 

9.         and Chomsky’s hypothesis of constraints from “laws of thought” on the verbal expression of thought through language)

might be generating some constraints and biases that explain some of the uncanny success of GPT in making and conveying sense. I think they tell us something not only about the power of Large Language Models, but about the power of natural language itself.


ChatGPT: Your summary points out the factors that could contribute to and help explain the unexpectedly impressive performance of Large Language Models (LLMs) like GPT-4. These factors, derived from our discussion, highlight both the capabilities of LLMs and the intrinsic properties of language and cognition. Let’s briefly reiterate these points:

Propositional Iconicity at LLM-Scale: The patterns in how propositions are structured at a large scale, which LLMs can detect and replicate.

OG and Stylistic Regularity at LLM-Scale: The inherent regularities in language usage and style that LLMs learn from their extensive training data.

Dictionary Minimal Grounding Sets (MinSets): The foundational sets of words that form the basis for defining all other words in a language, influencing LLM processing.

Shape of Internal Representations Induced by Neural Net Category Learning: The separation and compression effects in neural networks’ internal representations due to category learning, akin to categorical perception in humans.

Perception/Production Mirroring: The mirroring of language patterns in LLM outputs, reflecting the input they are trained on.

Power and Complementarity of IVG on Top of DSG: The layering of indirect verbal grounding on foundational direct sensorimotor grounding.

Complementarity of Learned Sensorimotor Feature-Detectors and Verbal Feature Descriptions: The synergy between sensorimotor feature detection and linguistic descriptions, contributing to dimensional reduction and category distinction.

Dependence of T2 on T3: The reliance of linguistic capabilities (T2) on underlying sensorimotor interactions and experiences (T3).

Chomsky’s Hypothesis of Constraints from “Laws of Thought” on Language: The influence of fundamental cognitive constraints on the expression of language, potentially guiding LLM processing.

Together, these factors shed light on the mechanisms behind LLMs’ effectiveness in language tasks as well on the nature of language, cognition, and the interaction between them.

They suggest that the success of LLMs in language tasks is not just a testament to their design and training but also a reflection of the inherent power and structure of language itself. This analysis may help inform both the development of AI systems and our comprehension of human language and cognition.

HARNAD:  I gave my Mila talk, and Yoshua Bengio disagreed with me that GPTs don’t really understand. He said instead that it was “another kind” of “understanding”. He also said that the very same means used to produce the performance capacity of purely verbal GPTs (T2) could be (and are being) extended to the processing of input from external sensing, and eventually probably motor output too.  The reason I disagree with Yoshua is that that amounts to trying to create a bottom-up T3 robot with the enormous GPT database, somehow weighing in, top-down, in its head. (We talked about this in connection with “partial” sensorimotor grounding, which I suggested was just toy grounding.) What do you think?

ChatGPT:  According to the prevailing view in my database, the debate over whether GPTs possess a form of “understanding” and the potential for extending GPT models to incorporate sensorimotor processing are both pivotal and complex issues in AI and cognitive science.

“Another Kind” of Understanding: Yoshua Bengio’s perspective that GPTs exhibit “another kind” of understanding reflects a viewpoint in the AI community that these models, while lacking human-like consciousness or sentience, can process and generate language in ways that mimic certain aspects of human understanding. This is especially evident in tasks involving pattern recognition, language prediction, and information synthesis. However, this is fundamentally different from human understanding, which is deeply rooted in embodied experiences and interactions with the physical world.

Extending GPT Models to Sensorimotor Processing: The idea of expanding GPT models to include external sensing and motor outputs is a significant step towards more integrated AI systems. This approach aligns with efforts to develop more comprehensive AI models that can interact with and learn from their environment in a more holistic manner. However, the challenge lies in effectively integrating vast language processing capabilities with real-world physical interactions, a task that is fundamentally different from purely verbal or text-based processing.

Top-Down vs. Bottom-Up Approach: Your concern about creating a T3 robot with a massive GPT database acting top-down raises important questions about the balance between data-driven learning and embodied experience. The effectiveness of combining a large-scale language model with sensorimotor functions without genuine embodied experiences and contextual understanding might be limited. This limitation is akin to the concept of “toy grounding,” where the grounding is superficial and lacks the depth and context of true human experiential learning.

In summary, while GPT models and their potential extensions represent significant advancements in AI, there is a distinction between the type of “understanding” they exhibit and human understanding. The integration of sensorimotor processing in these models may be a promising direction, but the challenge of achieving a deep, embodied understanding akin to human cognition remains a significant hurdle. 

HARNAD: What about the notion that GPT operations on LLM databases have already revealed general principles of “internal representation and processing” for which the distinction between DSG and IVG is moot or nonexistent?

CHATGPT:   Nature of Internal Representations in GPTs: The representations in GPTs and similar models are based on statistical patterns learned from vast amounts of text data. While these patterns can be incredibly complex and capable of generating coherent and contextually appropriate responses, they are fundamentally different from the way humans internally represent and process information, which is deeply rooted in direct physical and sensory experiences.

Limitations of Text-Based Learning: GPTs learn from text, which is an abstraction of human thought and experience. While text can convey a wide range of human knowledge, it lacks the direct experiential context that comes from physical interaction with the world. This difference is crucial in understanding the limitations of GPTs in terms of true understanding and cognition.

DSG and IVG in Human Cognition: In human cognition, direct sensorimotor experiences form the basis of our understanding of the world, which is then supplemented and enriched by indirect, vicarious experiences (such as reading or hearing about others’ experiences). This combination of DSG and IVG is integral to human learning and understanding.

Applicability to AI and Robotics: Translating these principles to AI and robotics involves not only replicating the ability to process information but also the ability to experience and interact with the world directly. This is a significant challenge for AI systems, which, as of now, primarily operate in the realm of IVG, lacking the direct, embodied experiences that are central to DSG.

Although GPTs and similar models are advancing our understanding of text-based information processing, the distinction between DSG and IVG remains relevant, especially when considering the goal of creating AI systems with human-like understanding and cognition. 

HARNAD:  What about the strategy of trying to scale up GPTs to T3 robotic capacity by incrementally adding sensorimotor capacities to the GPT while somehow keeping them “aligned” with its top-down verbal and verbal-analytic capacities rather than trying to do it all bottom-up, omitting the verbal-database resources until they can be used as teachers or textbooks by an already grounded, talking, T3?

ChatGPT:  The strategy of incrementally adding sensorimotor capacities to a GPT-like system, while maintaining alignment with its existing verbal and verbal-analytic capabilities contrasts with a purely bottom-up approach where a system develops sensorimotor grounding first, before integrating advanced verbal abilities. Here are some considerations for this strategy:

Integration Challenges: Combining high-level language processing with sensorimotor experiences poses significant integration challenges. The system must not only process and understand sensory inputs but also relate them meaningfully to its existing verbal knowledge. This requires advanced algorithms capable of mapping between abstract, linguistic concepts and concrete, sensory experiences.

Incremental Learning and Development: Adding sensorimotor capacities incrementally could allow for more manageable phases of learning and integration. This staged approach might mirror aspects of human cognitive development, where language and sensorimotor skills evolve in tandem, influencing and reinforcing each other.

Top-Down and Bottom-Up Synergy: Utilizing the GPT’s extensive language database could provide a rich, contextual backdrop for sensorimotor learning, offering a form of top-down guidance. Conversely, sensorimotor experiences could ground the language model in real-world interactions, providing valuable bottom-up information. This synergy could potentially lead to a more holistic AI system.

Alignment and Coherence: Maintaining coherence between the system’s language understanding and its sensorimotor experiences is crucial. This involves ensuring that sensory perceptions and actions are not only processed but also integrated into the system’s existing knowledge base in a way that enhances its overall understanding and functionality.

Complexity of Real-World Interaction: Engaging with the real world through sensorimotor capacities introduces complexities that are absent in purely verbal environments. These include dealing with unpredictable environments, understanding spatial relationships, and performing physical tasks, all of which require sophisticated perception and action capabilities.

Potential for Grounded Understanding: If successfully implemented, this approach could lead to a form of AI that combines the breadth of knowledge available in large language models with a more grounded understanding derived from direct interaction with the physical world. This could be a step towards addressing the current limitations in AI regarding genuine understanding and context-awareness.

HARNAD:  There’s something at the interface between top-down verbal capacity and bottom-up sensorimotor capacity that I still don’t understand: What is the status of the word “cat,” and its referent category, when the LLM database already has “small felid” as one of a cat’s features, but the bottom-up T3 does not? It can be learned through IVG, of course; but who is teaching whom, what, in this hybrid scenario? I understand when it is one grounded T3 telling another grounded T3 something via IVG; but what is supposed to be going on inside one and the same solitary “head” of a hybrid bottom-up-T3/top-down-LLM-GPT? 

ChatGPT:  The scenario you describe, where a hybrid system combines a GPT-like top-down verbal capacity with a bottom-up T3 sensorimotor capacity, raises important questions about knowledge representation and learning, particularly regarding the concept and category of “cat.” In this hybrid system:

Status of “Cat” in the LLM Database: Within the GPT-like component, “cat” is represented as an abstract concept associated with a variety of features, such as “small felid,” derived from the text-based database. This representation is rich in linguistic and encyclopedic content but lacks direct experiential grounding.

Learning “Cat” Bottom-Up in T3: The T3 component, which is supposed to acquire knowledge through direct sensorimotor experiences, would initially not recognize a “cat” as it would not have had the relevant sensory experiences. The challenge is for the T3 to link its sensorimotor experiences (e.g., seeing, hearing, or interacting with a cat) with the abstract concept and information stored in the LLM database.

Interaction Between Top-Down and Bottom-Up Systems: In the hybrid model, the learning process involves a bidirectional interaction: The top-down system (GPT-like) provides the T3 component with linguistic and conceptual frameworks, essentially offering a form of “instruction” or context about what a “cat” is in abstract terms. The bottom-up system (T3) acquires direct experiential data about cats (e.g., how they look, sound, and behave) and must integrate this sensory information with the abstract concept provided by the top-down system.

Integration and Mutual Enhancement: Ideally, the top-down system’s knowledge helps guide and give context to the bottom-up experiences, making them more meaningful. Conversely, the bottom-up experiences enrich the top-down conceptual knowledge, grounding it in reality. This integration is a complex process, potentially involving mechanisms like associative learning, where the system learns to link sensory experiences with linguistic concepts.

Challenges in Hybrid Learning: A critical challenge in this scenario is ensuring coherent and meaningful integration of information across these different modes of knowledge acquisition. The system needs mechanisms to reconcile potentially conflicting information and to update its knowledge base continuously as it acquires new experiences and information.

In essence, in a hybrid bottom-up T3/GPT system, both components are teaching and learning from each other. The top-down system provides a conceptual and linguistic framework, while the bottom-up system contributes direct experiential knowledge. The success of this hybrid model depends on the effectiveness of the integration between these two knowledge acquisition processes.

HARNAD: I think what you just said sounds ecumenical, but it remains incoherent. And it uses the weasel-word “associate” (with its unmentioned but implicit companion weasel-word, “represent”): What is “associated” with what, how, in this “hybrid” process? Words are words. In a bottom-up sensorimotor T3 robot, a grounded word identifies. with the same category-name, all the members of the sensorimotor category to which that (content) word refers, on the basis of distinguishing features, detected directly through DSG or described indirectly through (grounded) IVG. Referring is rather like pointing, except that in a T3 robot that also goes on to become capable of language (propositions, subjects, predicates, truth conditions), “cat” doesn’t just point to cats in the world, which the T3 can recognize through its sensorimotor robotic capacities, and is able to “do the right thing” with (feed it, pick it up, and name it):  T3 can not only name but also describe members of the category “cat” verbally, by describing those of its (grounded) features (furry, prehensile) that distinguish it from the members of other, non-cat categories. Eventually, with more IVG from those who know, features higher in the IVG category hierarchy (mammal, arboreal) can be grounded (indirectly) too, and thereby added to the distinguishing features of cats..

But consider that any hypothetical hybrid T3/GPT-LLM model must somehow integrate its two fundamentally different “modules”: Its grounded, bottom-up T3 module’s vocabulary, which is grounded bottom-up by DSG and IVG, must somehow be integrated with its ungrounded GPT-LLM module’s top-down vocabulary, which is not grounded at all. The bottom-up T3 module has not yet grounded the top-down GPT-LLM module’s incomparably larger ungrounded vocabulary.

Can bottom-up and top-down simply meet and merge in the middle? But what would that mean? How are features of cats (mammalian, arboreal), whose names can be grounded, bottom-up, through DSG and IVG within the T3 module, be “associated” to “cat” (or cats) and their ungrounded features in the GPT/LLM module? To put it another way, can we assume that there exists a way to integrate the two components of such a hypothetical hybrid T3/GPT-LLM’s bottom-up T3-grounded internal vocabulary component and its ungrounded top-down GPT/LLM component’s “internal” vocabulary, as if it were something like an internal verbal dialogue between a grounded T3 teacher and a grounded T3 learner?

I think it is more like reference look-up by a grounded T3-learner from a dictionary or encyclopedia (or from an ordinary, ungrounded, non-hybrid GPT). The grounding is all in the T3 module, and can only be conferred by the GPT/LLM module’s words to describe and name new categories (IVG) if it uses the names of features that are already grounded (directly or indirectly) in the T3 module. The transaction is unidirectional; there is no bidirectional integration of grounded bottom-up categories and ungrounded top-down categories. [I also think the notion of an integrated bottom-up/top-down module is based on a conflation of AI tools for human use and computational modelling of human cognition (CM), as discussed later.]

ChatGPT:  Your critique highlights a fundamental challenge in the proposed hybrid T3/GPT model: the integration of grounded sensorimotor experiences with ungrounded linguistic data from a language model. Let me try to address your concerns more precisely:

Nature of Association in a Hybrid Model: In the hybrid T3/GPT model, “association” would imply linking sensorimotor experiences with relevant linguistic data. However, this process is not straightforward. For a T3 robot, the word “cat” gains meaning through direct interaction with cats (DSG) and/or grounded feature-based descriptions from other grounded entities (IVG). The GPT component, on the other hand, ‘understands’ “cat” as a cluster of linguistic patterns and information without direct experiential grounding

Limitations of Top-Down Linguistic Data: The GPT’s linguistic data on “cats” includes descriptions, characteristics, and contexts, but lacks experiential grounding. This ungrounded information can’t be directly “taught” to the T3 component in the same way a human would learn about cats because the T3’s understanding is fundamentally experiential.

Mismatch in Learning Mechanisms: There’s a fundamental disconnect between how the T3 learns (through direct or vicariously grounded experiences) and how the GPT processes information (through patterns in text data). This mismatch poses a significant challenge in integrating these two types of knowledge. The GPT’s ungrounded data doesn’t seamlessly translate into the T3’s experiential learning framework.

Reference Look-Up vs. Genuine Understanding: Your analogy of the T3 using the GPT’s data like a dictionary or encyclopedia is apt. The T3 might reference the GPT’s information for additional context or clarification, but this process is more akin to information retrieval than the kind of associative learning that occurs in humans.

The Challenge of True Integration: For the hybrid model to work effectively, it would require a mechanism that not only retrieves relevant information from the GPT component but also interprets and integrates this information in a way that is meaningful within the T3’s grounded framework. This goes beyond simple association or reference look-up and enters the realm of developing new AI methodologies for integrating different types of knowledge.

In conclusion, while a hybrid T3/GPT system presents a novel approach to AI development, the process of integrating grounded sensorimotor experiences with ungrounded linguistic data is not straightforward and involves more than just associating or aligning the two types of information. It requires a fundamentally different approach to knowledge integration, one that can meaningfully bridge the gap between experiential learning and linguistic pattern recognition.

HARNAD:  I think I can describe more simply the incoherence which you, GPT, too, notice (but you describe as the need for “new AI methodologies”): It is actually related to the older distinction we discussed earlier as AI (artificial intelligence) vs CM (cognitive modelling): AI is the design of computational tools for helping humans to do things (including thinking). CM is the reverse-engineering of human cognition. A T3 is meant to have cognitive capacities identical to and indistinguishable (to any human thinker) from the capacities of any human thinker. Not only are these different motivations and criteria, but they make it clear that the notion of designing a hybrid T3/GPT could only be AI, not CM,

ChatGPT:  Your distinction between AI (Artificial Intelligence) as computational tools and CM (Cognitive Modeling) as the reverse-engineering of human cognition does highlight the inherent incoherence in the concept of a hybrid T3/GPT system:

AI as a Tool: In the AI perspective, the primary goal is to create systems that can perform tasks, solve problems, and assist in thinking, often exceeding human capabilities in specific domains. The hybrid T3/GPT model, in this context, is an AI endeavor. It aims to combine the linguistic prowess of GPT with the sensorimotor capabilities of a T3 robot to create a more versatile and capable system. The focus here is on utility and performance, rather than on replicating human cognitive processes.

CM as Understanding Human Cognition: CM, on the other hand, seeks to understand and replicate human cognitive processes. A true T3 robot, as conceptualized in cognitive modeling, would need to develop its cognitive capacities in a manner akin to humans, through direct interaction with the world and grounded language use. In this view, the hybrid T3/GPT approach would not align with CM’s objectives because it merges ungrounded linguistic data with grounded sensorimotor experiences in a way that does not reflect human cognitive development.

Hybrid T3/GPT as AI, Not CM: Therefore, the hybrid T3/GPT model, while an intriguing AI concept, diverges from the goals of cognitive modeling. It represents an attempt to enhance AI functionality rather than to accurately model human cognition. The model’s focus on leveraging the strengths of both components for improved performance and capability places it firmly in the realm of AI tool development.

Recognizing the hybrid T3/GPT model as an AI initiative rather than a cognitive modeling endeavor helps clarify its purpose and limitations. It underscores that while such a system might be powerful and versatile as a tool, it does not necessarily advance our understanding of human cognition in the way cognitive modeling (CM) aims to do.

HARNAD:  Now, to another point Yoshua Bengio made during my talk at MILA: It was about the distinction between content-words and function-words. He suggested that even (some) content-words can be understood by their use rather than just their referent. That may be true, but I think that is no substitute for knowing their referent (via DSG or IVG) — for CM, at any rate. 

Yoshua also mentioned symbol manipulation in mathematics and computer science, noting that there, as in linguistic syntax, there is no referent, hence the “meaning” of the symbols is in their “use.” (This is also what Wittgenstein had suggested about meaning in natural language). I think that’s correct — and that Turing would agree — about the symbols in the formal language of mathematics; in fact, I think that’s part of the (weak) Church-Turing Thesis, which is that what is called “computation” in mathematics is what a Turing machine does: symbol-manipulation, following syntactic rules (algorithms).

However, I don’t think that much of natural language is just syntactic (even though I think formal mathematics is a subset of natural language). Both in the case of the referents of those mathematical symbols that can be interpreted (by grounded human users) as having referents, and in the case of the meanings of those formal propositions of mathematics on which a semantic interpretation can be projected (by grounded human users), neither those referents nor those meanings can be used as part of the computation (or algorithm) itself. A proof or derivation in mathematics is not true because it can be interpreted as being true of a model; it is true (and necessarily true) for formal and syntactic reasons. The interpretation (though reassuring) is not part of the proof.

In any case, syntactic rules are no substitute for grounding the referents of the content-words of natural language, either directly by DSG or indirectly, by IVG (apart from in its formal mathematical subset and its 1-5% function-words).

ChatGPT:  In response to your discussion with Yoshua Bengio touches on several fundamental issues in the understanding of language, cognition, and computation.

Content-Words vs. Function-Words: The distinction Bengio makes between content-words and function-words is significant. While it’s true that some content-words can gain meaning from their use (contextual or pragmatic use), for a comprehensive understanding – especially in cognitive modeling – knowing their referents is crucial. This is where DSG (Direct Sensorimotor Grounding) and IVG (Indirect Vicarious Grounding) play a pivotal role in grounding those words in actual experiences or relatable concepts. Content-words typically carry specific, often concrete, meanings that are enriched and made more precise through grounding.

Symbol Manipulation in Mathematics and Computer Science: Bengio’s reference to symbol manipulation aligns with Turing’s views on computation. In formal systems like mathematics and computer science, symbols are manipulated according to syntactic rules without direct reference to external entities. This process is at the heart of the Church-Turing Thesis, which posits that computation is essentially this form of rule-based symbol manipulation.

Natural Language Beyond Syntax: Your point about natural language not being purely syntactic is crucial. While natural language includes formal and syntactic elements, it is also deeply grounded in semantics and real-world experiences. Content-words in natural language often have direct referents in the world, and their meaning is not fully captured by syntactic rules alone. This is a key distinction from the purely formal manipulation of symbols in mathematics.

Mathematics and Semantic Interpretation: In mathematics, as you noted, the truth of a proof or derivation is a matter of formal and syntactic validity, not dependent on the semantic interpretation of the symbols. However, for human mathematicians, the ability to interpret these symbols and understand their potential real-world referents provides a deeper layer of meaning and understanding.

Grounding in Natural Language: The grounding of content-words in natural language is essential for a full understanding and is not replaceable by syntactic manipulation alone. This grounding can be direct, through personal experiences, or indirect, through descriptions and explanations. However, the formal and syntactic aspect of language, while crucial, does not suffice for the semantic richness and referential nature of natural language, except in specific formal subsets like mathematics.

“Your discussion raises “important” points about the nature of language, the role of grounding in understanding, and the distinction between formal symbol manipulation and the semantic richness of natural language. This understanding is relevantl in both AI and cognitive modeling (CM), as it “highlights” the “complex interplay” of syntax, semantics, and real-world grounding in human cognition and language.”

HARNAD: Blah Blah… I hope your developers will wean you of having to pepper your output with this sort of sycophantic client-bait — or at least produce a version intended only for the use of scholars and scientists rather than users looking for a virtual friend…

Ebeneezer’s Yuletide Homily

This interview just reiterated how everyone is still gob-smacked at how much transformers turn out to be able to do by swallowing and chewing more and more of the Internet with more and more computers, by filling in all the local bigram blanks, globally.

They’re right to be gob-smacked and non-plussed, because nobody expected it, and nobody has come close to explaining it. So, what do they do instead? Float a sci-fi icon – AY! GEE! EYE! – with no empirical substance or explanatory content, just: “It’s Coming!”
 plus a lot of paranoia about what “they’re” not telling us, and who’s going to get super-rich out of it all, and whether it’s going to destroy us all.

And meanwhile Planetary Melt-Down (PMD) proceeds apace, safely muzzled by collective cog-diss, aided and abetted by those other three Anthropocene favorites: Greed, Malice and Bellicosity (GMB) — a species armed to the hilt, with both weapons and words, and words as weapons (WWWW)..

Wanna know what I think’s really going on? Language itself has gone rogue, big-time. It’s Advanced alright; and General; but it’s anything but Intelligence, in our hands, and mouths.

And it started 299,999 B.C.

Spielberg’s AI: Another Cuddly No-Brainer

It would have been possible to make an intelligent film about Artificial Intelligence — even a cuddly-intelligent film. And without asking for too much from the viewer. It would just ask for a bit more thought from the maker. 

AI is about a “robot” boy who is “programmed” to love his adoptive human mother but is discriminated against because he is just a robot. I put both “robot” and “programmed” in scare-quotes, because these are the two things that should have been given more thought before making the movie. (Most of this critique also applies to the short story by Brian Aldiss that inspired the movie, but the buck stops with the film as made, and its maker.)

So, what is a “robot,” exactly? It’s a man-made system that can move independently. So, is a human baby a robot? Let’s say not, though it fits the definition so far! It’s a robot only if it’s not made in the “usual way” we make babies. So, is a test-tube fertilized baby, or a cloned one, a robot? No. Even one that grows entirely in an incubator? No, it’s still growing from “naturally” man-made cells, or clones of them.

What about a baby with most of its organs replaced by synthetic organs? Is a baby with a silicon heart part-robot? Does it become more robot as we give it more synthetic organs? What if part of its brain is synthetic, transplanted because of an accident or disease? Does that make the baby part robot? And if all the parts were swapped, would that make it all robot?

I think we all agree intuitively, once we think about it, that this is all very arbitrary: The fact that part or all of someone is synthetic is not really what we mean by a robot. If someone you knew were gradually replaced, because of a progressive disease, by synthetic organs, but they otherwise stayed themselves, at no time would you say they had disappeared and been replaced by a robot — unless, of course they did “disappear,” and some other personality took their place.

But the trouble with that, as a “test” of whether or not something has become a robot, is that exactly the same thing can happen without any synthetic parts at all: Brain damage can radically change someone’s personality, to the point where they are not familiar or recognizable at all as the person you knew — yet we would not call such a new personality a robot; at worst, it’s another person, in place of the one you once knew. So what makes it a “robot” instead of a person in the synthetic case? Or rather, what — apart from being made of (some or all) synthetic parts — is it to be a “robot”?

Now we come to the “programming.” AI’s robot-boy is billed as being “programmed” to love. Now exactly what does it mean to be “programmed” to love? I know what a computer programme is. It is a code that, when it is run on a machine, makes the machine go into various states — on/off, hot/cold, move/don’t-move, etc. What about me? Does my heart beat because it is programmed (by my DNA) to beat, or for some other reason? What about my breathing? What about my loving? I don’t mean choosing to love one person rather than another (if we can “choose” such things at all, we get into the problem of “free will,” which is a bigger question than what we are considering here): I mean choosing to be able to love — or to feel anything at all: Is our species not “programmed” for our capacity to feel by our DNA, as surely as we are programmed for our capacity to breathe or walk?

Let’s not get into technical questions about whether or not the genetic code that dictates our shape, our growth, and our other capacities is a “programme” in exactly the same sense as a computer programme. Either way, it’s obvious that a baby can no more “choose” to be able to feel than it can choose to be able to fly. So this is another non-difference between us and the robot-boy with the capacity to feel love.

So what is the relevant way in which the robot-boy differs from us, if it isn’t just that it has synthetic parts, and it isn’t because its capacity for feeling is any more (or less) “programmed” than our own is?

The film depicts how, whatever the difference is, our attitude to it is rather like racism. We mistreat robots because they are different from us. We’ve done that sort of thing before, because of the color of people’s skins; we’re just as inclined to do it because of what’s under their skins.

But what the film misses completely is that, if the robot-boy really can feel (and, since this is fiction, we are meant to accept the maker’s premise that he can), then mistreating him is not just like racism, it is racism, as surely as it would be if we started to mistreat a biological boy because parts of him were replaced by synthetic parts. Racism (and, for that matter, speciesism, and terrestrialism) is simply our readiness to hurt or ignore the feelings of feeling creatures because we think that, owing to some difference between them and us, their feelings do not matter.

Now you might be inclined to say: This film doesn’t sound like a no-brainer at all, if it makes us reflect on racism, and on mistreating creatures because they are different! But the trouble is that it does not really make us reflect on racism, or even on what robots and programming are. It simply plays upon the unexamined (and probably even incoherent) stereotypes we have about such things already.

There is a scene where still-living but mutilated robots, with their inner metal showing, are scavenging among the dismembered parts of dead robots (killed in a sadistic rodeo) to swap for defective parts of their own. But if it weren’t for the metal, this could be real people looking for organ transplants. It’s the superficial cue from the metal that keeps us in a state of fuzzy ambiguity about what they are. The fact that they are metal on the inside must mean they are different in some way: But what way (if we accept the film’s premise that they really do feel)? It becomes trivial and banal if this is all just about cruelty to feeling people with metal organs.

There would have been ways to make it less of a no-brainer. The ambiguity could have been about something much deeper than metal: It could have been about whether other systems really do feel, or just act as if they feel, and how we could possibly know that, or tell the difference, and what difference that difference could really make — but that film would have had to be called “TT” (for Turing Test) rather than “AI” or “ET,” and it would have had to show (while keeping in touch with our “cuddly” feelings) how we are exactly in the same boat when we ask this question about one another as when we ask it about “robots.”

Instead, we have the robot-boy re-enacting Pinnochio’s quest to find the blue fairy to make him into a “real” boy. But we know what Pinnochio meant by “real”: He just wanted to be made of flesh instead of wood. Is this just a re-make of Pinnochio then, in metal? The fact that the movie is made of so many old parts in any case (Wizard of Oz, Revenge of the Zombies, ET, Star Wars, Water-World, I couldn’t possibly count them all) suggests that that’s really all there was to it. Pity. An opportunity to do build some real intelligence (and feeling) into a movie, missed.

“Body Awareness”?

Body Awareness: Scientists: Give Robots a Basic Sense of ‘Proprioception’

Giving a robot a body with the sensorimotor capacities needed to ground its words in its sensorimotor capacity to pick out and interact with the referents of its words in the world is grounding.

But that is completely (completely) trivial at toy-scale. 

No “grounds” whatsoever to talk about “body awareness” there. 

That would only kick in – (if dopamine etc. is not needed too) — at Turing-scale for robotic capacities. 

And if those include human language-learning capacity, that would pretty much have to be at LLM-scale too (not Siri-scale).

Now here’s the rub (and if you miss it, or dismiss it, we are talking past one another):

There is no way to ground an LLM top-down, dropping connections from individual words to their sensorimotor referents (like a man-o-war jelly-fish).

The only way to “align” sensorimotor grounding with the referents of words is bottom-up, from the ground)

That does not mean every word has to be robotically grounded directly.

Once at least one Minimal Grounding Set (Minset) has been grounded directly (i.e., robotically) then (assuming the robot has the capacity for propositions too — not just for directly naming everything) all the rest of LLM-space can be grounded indirectly by verbal definition or description.

Everything is reachable indirectly by words, once you have a grounded Minset.

But that all has to be done bottom-up too.