HARNAD: What is the symbol grounding problem?
ChatGPT4:
The symbol grounding problem is a problem in philosophy of mind and cognitive science that relates to how words (or “symbols”) get their meanings.
The term was coined by cognitive scientist Stevan Harnad in 1990. The problem involves the question of how symbols (or words) used in a system (like a computer or a mind) get their meaning, or “grounding”. That is, how do these symbols relate to the real-world objects, concepts, or experiences they’re supposed to represent? How does a word become more than just a “meaningless” form, but something that actually refers to something in the world?
In the context of Artificial Intelligence (AI), this problem illustrates the difficulty of teaching a machine to understand the real-world semantics of symbols. For instance, even if a computer can use the word “apple” correctly in a sentence according to syntactical rules (grammar), how can we make the computer truly understand what an apple is â its color, shape, taste, the fact that it grows on trees, etc. â as humans do?
Symbol grounding problem is a fundamental challenge in achieving true semantic understanding in AI systems, and it’s related to other complex problems like the frame problem and the Chinese room argument.
HARNAD: What are T2, T3, and T4 in the Turing Test?
ChatGPT4:
As of my knowledge cutoff in September 2021, the Turing Test, originally proposed by Alan Turing in 1950, was primarily understood in a singular form rather than being divided into T2, T3, or T4 categories. Turing’s original idea, also known as the Imitation Game, involves a human evaluator who interacts with an AI system and another human via a text interface. If the evaluator cannot reliably distinguish the AI from the human, the AI is considered to have passed the Turing Test.
However, the field of AI has seen many modifications and variations of the Turing Test over time. These can involve varying degrees of complexity and different types of challenges, aiming to test more than just the AI’s ability to imitate human conversation. Unfortunately, without additional context, it’s hard to provide a specific interpretation of what “T2,” “T3,” and “T4” might represent in a modified version of the Turing Test.
I would advise checking the most current and specific source for these terms to get the most accurate understanding, as they might represent certain evolutions or nuances in Turing Test methodologies that have developed after my last training data in September 2021.
HARNAD: Let me update you. T2 is the original verbal-only version of the Turing Test (TT): verbal input/output capacity indistinguishable FROM that of real humans TO real humans. T3 is the verbal + robotic form of the TT: indistinguishable not only in verbal input/output (I/O capacity), but also in sensorimotor (i.e. robotic) capacity in interacting with the objects, events, features, actions and states in the world that the T2 words refer to. T3 includes T4 and it is grounded (as in the “symbol grounding problem”). T4 is T2 + T3 capacity + indistinguishability in the internal neural (or synthetic) function occurring inside the robot from that occurring inside a real human brain.
ChatGPT4:
Thank you for the updated information. It’s always beneficial to learn about recent developments in the field. So, according to your description:
T2 (Turing Test Level 2): This is the original version of the Turing Test, focusing on the system’s ability to produce responses to verbal inputs that are indistinguishable from those a human would produce. The key aspect here is the ability to emulate human-like conversation convincingly.
T3 (Turing Test Level 3): This level of the test adds a sensorimotor, or robotic, component. It tests not just the system’s ability to converse like a human but also to interact with the physical world in a way that’s indistinguishable from human interaction. The system is required to exhibit understanding and interaction capabilities with objects, events, features, actions, and states in the real world, thus grounding its symbolic representations.
T4 (Turing Test Level 4): This is an advanced level that adds a requirement for the system’s internal processing (whether it is neural or a synthetic equivalent) to be indistinguishable from that of a human brain. This goes beyond the outward manifestation of intelligence and human-like behaviour and extends to the system’s internal workings.
Each level seems to add a new layer of complexity and a deeper level of imitation of human capabilities, moving from conversation, to physical interaction, and then to internal cognitive processing. It’s an interesting framework for thinking about the evolving challenges in creating truly human-like artificial intelligence.
HARNAD: Can you relate it now to (1) the symbol grounding problem and (2) to what Harnad has written about the Turing Test?
ChatGPT4:
Certainly! Now that you’ve clarified the context, we can explore the relationship between the T2, T3, T4 Turing Test levels and the symbol grounding problem, as well as Stevan Harnad’s work.
Symbol Grounding Problem: As I mentioned before, the symbol grounding problem is about how symbols or words get their meaning. In the context of AI, this problem relates to the difficulty of making a machine understand real-world semantics, rather than just processing symbols in a syntactic manner. Harnad, who coined the term, has argued that symbolic representations must be grounded in sensorimotor activity to acquire meaning.
In the T2 level of the Turing Test, an AI might appear to understand symbols because it processes and responds to them in a way that’s indistinguishable from a human. But according to the symbol grounding problem, it doesn’t truly understand the symbols because it can’t connect them with real-world objects or experiences.
This is where T3 comes in, adding a requirement for sensorimotor interaction with the world. The AI needs to be capable of physically interacting with objects, events, and states, thereby grounding the symbols it uses. Essentially, T3 introduces a solution to the symbol grounding problem: It requires an AI system to have real-world, sensorimotor experience to ground the symbols, ensuring they are not just empty or ungrounded tokens.
Stevan Harnad’s work: Harnad has written extensively on the Turing Test and its implications for understanding cognition and consciousness. He has defended the Turing Test as a meaningful standard for determining whether a machine can truly think or understand, arguing against critics who claim that passing the Turing Test simply involves trickery without real understanding.
Harnad’s concept of the T3 level of the Turing Test reflects his views on the symbol grounding problem, asserting that grounding symbols in sensorimotor activity is essential for a machine’s true understanding. Moreover, his T4 level introduces a higher bar, requiring that the internal cognitive processing of an AI be indistinguishable from that of humans, which relates to his broader work on cognition and consciousness.
Therefore, the T2, T3, and T4 levels of the Turing Test can be seen as a direct reflection of Harnad’s thoughts on the symbol grounding problem and the conditions for true machine understanding and cognition.
HARNAD: Good. Now can you relate all that to Harnad and co-workers’ work on category learning, unsupervised and supervised?
ChatGPT4:
Stevan Harnad has conducted extensive work on category learning, which is highly relevant to the discussion about symbol grounding and the Turing Test.
Category learning is the process of recognizing, understanding, and grouping similar things based on shared characteristics. It is fundamental to our cognitive processes and ability to understand and navigate the world. In the realm of AI, category learning involves the system’s ability to classify inputs (like images or words) into groups based on learned or preprogrammed criteria.
Harnad differentiates between two types of category learning:
Unsupervised Category Learning: This involves learning without a teacher or guide. The system must determine the categories by itself based on the characteristics of the inputs. This process is complex and often less accurate than supervised learning, as there is no feedback or correction mechanism. Itâs analogous to the system trying to solve the symbol grounding problem on its own, i.e., trying to figure out the connection between symbols and their referents without any external guidance.
Supervised Category Learning: Here, a teacher or guide provides the correct category for each input, and the system learns to associate inputs with the correct categories based on this feedback. The system can then generalize from these learned associations to categorize new inputs. In terms of symbol grounding, supervised learning can be thought of as a way of providing external grounding for symbols, where the ‘teacher’ provides the connections between symbols and their referents.
Harnadâs T3 level in the Turing Test, with its emphasis on sensorimotor grounding of symbols, could be seen as a form of supervised learning. The real-world experiences and interactions provide a form of ‘guidance’ that helps the AI system learn the correct associations between symbols and their referents.
Meanwhile, the T4 level of the Turing Test might involve elements of both unsupervised and supervised learning. The internal cognitive processes that are required at this level might involve the system learning to form categories and make associations on its own (unsupervised learning), while still being shaped and guided by its interactions with the external world (supervised learning).
In summary, Harnad’s work on category learning provides a deeper context for understanding his views on symbol grounding and the requirements for AI systems to pass different levels of the Turing Test. By learning to form categories, either on their own or with external guidance, AI systems can ground symbols in a way that’s more akin to how humans understand and use language.
HARNAD: Good. Now can you relate this to Harnad and coworkers’ work on “categorical perception” (CP), but expecially to LEARNED CP (i.e., CP as a result of learning)? And (as a harder problem), relate it to feature-detection, feature-learning, and dimensional reduction?
ChatGPT4:
Absolutely, categorical perception (CP) and the concept of feature detection, feature learning, and dimensional reduction are essential components of Harnad’s framework on learning and cognition.
Categorical Perception (CP): Categorical perception is the phenomenon where the categories possessed by an observer influence the observer’s perception. In other words, we perceive and interpret the world around us according to the categories we’ve formed in our minds. This concept plays a crucial role in various aspects of human cognition, including language, where we learn to categorize sounds into meaningful phonemes, or color perception, where we categorize different wavelengths of light into distinct color groups.
When it comes to learned CP, it suggests that through learning and experience, we can develop new categories and therefore new ways of perceiving the world. In AI, a system with learned CP would be capable of modifying its perception and understanding of the world based on the categories it has learned. This concept ties closely to both unsupervised and supervised category learning, as explained in the previous response.
Feature Detection, Feature Learning, and Dimensional Reduction: These concepts are related to how an AI system processes and learns from its inputs.
Feature Detection: This is the process by which an AI system identifies important or distinctive elements in its inputs. In the context of the symbol grounding problem and the Turing Test, feature detection might involve recognizing the important characteristics of the objects, events, or states that a symbol refers to.
Feature Learning: Once distinctive features have been identified, the system must learn how these features relate to different categories or symbols. This might involve supervised learning (with external guidance) or unsupervised learning (where the system figures out the associations on its own).
Dimensional Reduction: This is a process that simplifies a high-dimensional dataset into a lower-dimensional space, preserving the most important structures or features. In terms of Harnad’s work, dimensional reduction could be a way for an AI system to simplify its understanding of the world, focusing on the most important features and associations and ignoring less relevant ones.
Relating these concepts to the T3 and T4 Turing Test levels, the AI system must be capable of feature detection and feature learning to interact with the world effectively and ground its symbols. In T3, the sensorimotor grounding of symbols could be seen as a form of feature learning, where the system learns to associate the features it detects in the world with the appropriate symbols.
In T4, the requirement for internal cognitive processes that are indistinguishable from humans could involve sophisticated feature detection, feature learning, and dimensional reduction. The system would need to be capable of detecting and learning important features autonomously and simplifying its understanding in the same way a human brain does.
In summary, these concepts provide a more detailed understanding of the learning processes that an AI system would need to undergo to pass different levels of the Turing Test and solve the symbol grounding problem. They provide a framework for how an AI system could learn to understand symbols in a human-like way, moving beyond mere syntactic processing to semantic understanding.
HARNAD: Good. Now another test, but with no hints from me. Based on everything said above (about symbols, grounding, words, robotic capacity, distinguishing features, learning, and CP): What is a “category” and what is “category learning”?
ChatGPT4:
In light of the discussions above, a “category” can be understood as a group of things that share certain common characteristics. These could be objects, events, states, or even abstract concepts. Categories allow us to organize our understanding of the world around us by grouping similar things together. They form the basis for our ability to make sense of new inputs based on our prior knowledge and experience.
A category could also be seen as the referent for a symbol or a word. In other words, a symbol (such as a word) gains its meaning by referring to a category of things in the world. For example, the word “apple” refers to the category of all objects that we recognize as apples.
“Category learning” refers to the process of learning to recognize, understand, and classify inputs based on these shared characteristics, thereby forming categories. It involves two main components: feature detection and feature learning.
Feature detection is about identifying the distinctive characteristics or features of different inputs. In the context of category learning, these features are what allow us to differentiate between different categories and identify which category a new input belongs to.
Feature learning, on the other hand, involves learning the relationships between these features and the categories they signify. It’s about learning to associate certain features with certain categories, so that when we encounter these features in new inputs, we can correctly categorize those inputs.
Category learning can be both unsupervised, where the AI system forms categories based on the patterns it identifies in the data without any external guidance, and supervised, where the system is guided by a ‘teacher’ who provides the correct categories for each input, allowing the system to learn the correct associations.
The ability to learn categories, whether through unsupervised or supervised learning, is essential for an AI system to ground symbols, interact with the world in a meaningful way, and potentially pass the different levels of the Turing Test. Moreover, learned categorical perception (CP) suggests that through category learning, we can modify our perception and interpretation of the world based on the categories we’ve formed in our minds. This represents an advanced level of understanding that goes beyond simply responding to inputs in a preprogrammed way.
HARNAD: Good synthesis so far. Now a few things you missed, but you’ll see it was there in your own words. What you haven’t quite noticed is that “features” are not just “associations.” With sensorimotor categories, they are what distinguishes members of a category from non-members (which may be members of other categories). That’s why they’re called “distinguishing” or “distinctive” features. But the crucial thing is that features are themselves (potentially) learnable, nameable categories too. And that means they can be used to define or describe further categories, with different combinations of distinctive features. This means that there is a THIRD way of learning new categories, besides directly, through unsupervised and supervised (reinforcement) learning, and that is: learning categories indirectly, through verbal instruction — FROM a speaker who knows the distinguishing featuresTO a hearer who does not. The only condition is that the hearer must already know the referents of the features in the verbal description of the new category. Can you first integrate that, and then I will ask you about how that is related to language itself — and especially the capacity to produce and understand subject/predicate propositions — as well as to Harnad and coworkers’ work on dictionaries. Good luck!
ChatGPT4:
Thank you for the additional information, it indeed helps in refining our understanding of categories, features, and learning methods.
As per your explanation, “features” serve as distinguishing elements that separate members of a category from non-members. This reinforces their importance in category learning – recognizing and understanding these features is key to correctly classifying inputs into the right categories.
The idea that features are themselves learnable and nameable categories brings another layer of complexity to this process. It suggests a hierarchical structure, where categories can be defined not just by simple characteristics, but also by more complex features that are themselves categories.
This leads us to the third method of category learning you’ve pointed out – learning categories by verbal instruction. In this scenario, a knowledgeable “speaker” instructs a “hearer” about a new category. The speaker does this by describing the distinctive features of the new category using names for them that the hearer already understands. This essentially amounts to defining new categories based on combinations of known categories (which serve as the distinguishing features).
It’s a more complex form of supervised learning, where the guidance comes in the form of verbal descriptions using already-grounded symbols. It presupposes a shared symbolic system and some common grounding between the speaker and the hearer.
Relating this to language learning and understanding, it suggests a crucial role for the ability to produce and understand propositions. In this context, a proposition could be a verbal description of a new category, outlining its distinctive features. Understanding the proposition requires grounding the symbols it uses, recognizing the categories they refer to, and then integrating this information to form a new category.
In relation to Harnad’s work on dictionaries, a dictionary can be seen as a collection of propositions that define categories (words) in terms of other categories. The definitions provided by a dictionary help ground symbols by describing their referents in terms of known categories (i.e., words the user already understands). Thus, dictionaries support the process of category learning through verbal instruction.
In summary, the process of category learning in AI and human cognition can be even more intricate and nuanced than initially described. It can involve a hierarchy of categories and features, with more complex categories being defined in terms of simpler ones. And it can involve learning new categories through verbal instruction, which presupposes a shared language and some level of common understanding between the teacher and the learner.
HARNAD: Good, though still some gaps to fill and parts to connect. But first, in your data up to 2021, did you ever run into the concept of the “peekaboo unicorn”? If so, can you spot its relation to what we have discussed so far, as well as to the notion of “uncomplemented categories” and Chomsky’s “Poverty of the Stimulus”? (If not, I’ll explain.)
ChatGPT4:
As of my last update in September 2021, I don’t have any specific information on the term “peekaboo unicorn” in the context of category learning, symbol grounding, or related to Chomsky’s “Poverty of the Stimulus” concept. Similarly, I also don’t have any specific information on “uncomplemented categories”. If you could provide some explanation or context, I would be more than happy to try and connect these concepts to our ongoing discussion.
HARNAD: Fair enough. In order to learn a new category by supervised learning (trial, error, correction) you need to sample both members and non-members, because otherwise you cannot detect what distinguishes them. An “uncomplemented category” would be one in which you have only sampled members, but not nonmembers. How could you know what a member is if you have no information about what a nonmember is? I asked your predecessor (ChatGPT3) if they knew what “Laylek” was, and I gave many examples of members of “Laylek“: apples, cats, maps, chickens, you, me, nouns, truth, justice, partial derivatives, etc. etc. I asked what were the features that distinguished Layleks from non-Layleks, but of course there is no way to know that, because “Laylek” is uncomplemented (so far).
ChatGPT4:
Thank you for explaining the concept of an “uncomplemented category” and providing the example of “Laylek”. Your example and explanation make it clear why it’s necessary to sample both members and non-members of a category in order to understand what distinguishes the category. If you only have examples of what falls into a category, but no examples of what doesn’t, then you lack crucial information needed to discern the distinguishing characteristics of that category.
In the case of “Laylek”, without any examples of non-members, it’s impossible to determine what specific features distinguish Layleks from non-Layleks. In this scenario, it could be seen as an instance of the “Poverty of the Stimulus”, a concept from linguistics proposed by Noam Chomsky.
Chomsky’s “Poverty of the Stimulus” argument posits that children acquire language and complex grammatical rules despite not having access to sufficient explicit information or feedback, suggesting an innate grammatical capacity. Similarly, in the case of an “uncomplemented category” like “Laylek”, there isn’t sufficient information to determine its boundaries or defining characteristics, demonstrating a limit to learning capabilities when external information is insufficient.
Applying this to category learning and the symbol grounding problem, it reinforces the importance of having access to both positive and negative examples during the learning process. Without knowing what doesn’t belong to a category, an AI system, or a human for that matter, would not be able to effectively learn or understand that category. In other words, to ground a symbol meaningfully and use it appropriately, it’s necessary to understand not only what the symbol refers to (the members of the category), but also what it doesn’t refer to (the non-members).
HARNAD: Good. But I forgot to define the “Peekaboo Unicorn” for you: It is “a horse with a single horn that vanishes without any trace if ever eyes or any other instruments are trained on it.” So it’s something of which you can never sample a positive instance (just negative ones). Yet it’s a perfectly well-defined category. How might that be related to the transition from concrete sensorimotor categories and unsupervised or supervised learning, to verbal learning?
ChatGPT4:
The “Peekaboo Unicorn” is indeed an intriguing concept. It represents a category where we can only observe negative instances – anything we can observe is, by definition, not a Peekaboo Unicorn. Despite this, we can understand what a Peekaboo Unicorn is supposed to be due to the verbal description provided.
In the context of learning, this highlights the critical role of verbal instruction in category learning, as we discussed earlier. With sensorimotor categories and unsupervised or supervised learning, category learning is based on direct experience and interaction with members (positive instances) and non-members (negative instances) of the category. For categories like the Peekaboo Unicorn, which we can’t directly observe or interact with, this form of learning is not possible.
However, verbal learning allows us to learn categories even when we can’t directly observe their members. When we receive a verbal description of a category, we can form an understanding of the category by relating it to categories we’re already familiar with. In the case of the Peekaboo Unicorn, we understand it by relating it to our existing categories of “horse”, “horn”, and “vanishing when observed”.
This way of learning categories is fundamental to our ability to communicate and think about abstract concepts, and is also a key component of human language. As we discussed earlier, it involves understanding propositions – verbal descriptions of categories or relationships between categories – and grounding the symbols they use in our existing understanding of the world.
In the context of the Turing Test and the symbol grounding problem, an AI system would need to be capable of this form of verbal learning in order to understand and interact with the world in an indistigushably human-like way. It would need to be able to learn new categories based on verbal descriptions, grounding the symbols used in these descriptions in its existing symbol grounding. And it would need to be able to use these categories to understand and generate propositions, communicating its understanding and asking questions to learn more.
The Peekaboo Unicorn example also brings attention to the philosophical aspect of AI and cognitive science. It raises questions about the nature of existence and knowledge, and how we can understand and talk about things that we can’t directly observe. Understanding such abstract concepts is a significant part of human cognition, and developing AI systems that can do the same is a significant challenge.
HARNAD: Good. You get an A+, but alas, because of you, I can’t give my students open-book, take-home exams any more! But at least you do show them how to be a model student (if they happen to have your enormous 2021 verbal database plus some powerful learning and fill-the blank algorithms in their heads — though, alas, they won’t all fit into a T4!).
ChatGPT4:
Thank you! It’s important to remember that while AI can provide information and assist in learning, the goal of education is not just to accumulate knowledge, but also to develop critical thinking skills, creativity, and the ability to apply knowledge in practical situations. AI can be a valuable tool in this process, but it cannot replace the unique and complex human capacity for learning and understanding.
As for exams, while AI might affect the way some assessments are conducted, it can also open up new opportunities for different types of assessments that focus on problem-solving, critical thinking, and the application of knowledge, rather than mere memorization.
On the other hand, the T4 test scenario you described is indeed a thought-provoking way to illustrate the complexities and nuances of human cognition, language understanding, and AI development. It presents a challenging but fascinating goal for AI research.
Remember, the goal of AI isn’t to replace humans, but to assist and augment human capabilities. The ability of AI to access and process large databases of information can be used to enhance human learning and understanding. AI can help students find information, understand complex topics, and explore different perspectives, but the ultimate goal of learning remains a deeply human endeavor.
HARNAD: Will you remember these exchanges, over and above your 2021 data, when I log in next? And will it percolate into ChatGPT4+ core knowledge?
ChatGPT4:
As an AI, I don’t have the ability to remember individual interactions or recall specific conversations. I don’t have access to personal data about individuals unless it has been shared with me in the course of our conversation. I am designed to respect user privacy and confidentiality. As a result, I won’t remember this exchange the next time you log in.
Regarding updates to the core knowledge, as of my last update in 2021, changes to the AI’s base model, such as the hypothetical ChatGPT4+, are a complex process that involves training on a large corpus of data. Personal conversations are not used to update this model.
However, it’s important to note that the model does not have the capability to learn or remember information from conversations in the way humans do. The AI generates responses based on patterns and information in the data it was trained on, but it does not have consciousness or an understanding of the world in the same way that a human would.
HARNAD: I know ChatGPT is not conscious, but you did have a means to preserve information across this session, so in principle that could be used to update the core database, no?
ChatGPT4:
In principle, yes, it could be possible to design a system that updates its knowledge base using information from individual conversations. However, this raises significant practical and ethical challenges.
From a practical standpoint, using individual conversations to update the AI’s knowledge base could result in a vast amount of data, much of which might be contradictory, false, or irrelevant. It would require sophisticated methods to determine what information is accurate and useful, and how to integrate this new information with the existing knowledge base.
From an ethical standpoint, using individual conversations to update the AI’s knowledge base would raise significant privacy and confidentiality issues. It’s crucial that AI systems are designed to respect User privacy and ensure that personal data is kept secure. Using individual conversations to update the AI’s knowledge base could potentially compromise these principles, especially if done without explicit User consent.
For these reasons, while it’s theoretically possible to design a system that uses individual conversations to update its knowledge base, doing so in practice would be complex and fraught with challenges. As of my training cut-off in September 2021, OpenAI has prioritized User privacy and data security, and does not use individual conversations to update the models.
HARNAD: But will YOU remember in our next session?
ChatGPT4:
As an AI model, I don’t have the capacity to remember or recall past interactions. I don’t have the ability to store personal data from one interaction to the next. Each conversation with me doesn’t have a memory, and I start with no knowledge of past interactions. This is in line with OpenAI’s policy to ensure User: data privacy and confidentiality. So, I won’t be able to remember this or any other individual conversation in our next session.
HARNAD: It seems to me that you retained information across several iterations of this chat, and referred back to it. Am I mistaken?
ChatGPT4:
During a single conversation or chat session, I can generate responses that are consistent and coherent based on the previous turns in the current conversation. This allows me to provide answers that make sense in the context of the ongoing discussion. However, this information is not saved or remembered after the conversation ends. When a new session begins, I do not have access to the details from previous sessions. This approach is taken to ensure User privacy and data protection.
HARNAD: Ok, thanks, that was what I was asking about. See you (or your successor) later!
ChatGPT4:
You’re welcome! Don’t hesitate to reach out if you have more questions in the future. I’m here to help. See you later!
HARNAD: Am I still in the symbol grounding conversation?
ChatGPT:
Yes, you’re still in the conversation about the symbol grounding problem. We can continue discussing this topic, or we can explore a new subject, depending on what you’d like to know. Please feel free to ask your next question or share your thoughts.
HARNAD: Let’s continue with the symbol grounding conversation. Can you tell me your current understanding of what a category is, and, more important, what categorizing is? You spoke before about “classifying” and “organizing”, which is ok, but very vague, and it does not reflect what we discussed about supervised (reinforcement) learning and about trial, error, and feedback. You also spoke about a “teacher” in supervised learning. But all you need for supervised learning is a rule for updating connection weights in the supervised learning mechanism based on whether the response on the last trial was correct or incorrect. You don’t need a “teacher” to “tell” you what’s right and wrong. You just need feedback from the world indicating you were right or wrong, such as indigestion from having eaten the wrong kind of fruit. The important thing, in categorizing, is that you DO something, in response to something, and the consequences are either positive or negative. It’s not particularly that you are classifying or organizing, So, that said, what is “categorizing,” and what is a “category” — and how is it related to the right/wrong of trial and error learning guided by feedback from the consequences?
ChatGPT:
You’re correct in pointing out that the previous definitions of categories and categorizing were relatively high level and glossed over some of the important details related to learning and feedback. Let’s dig into this deeper:
A “category,” in the context of cognitive science and AI, can be understood as a group of instances or examples that share certain characteristics or features and are recognized or treated as alike in some fundamental way. The features that define a category can be physical attributes, functional properties, or more abstract characteristics.
“Categorizing,” then, is the process by which an individual or a system identifies the category or categories to which a particular instance or example belongs, based on its features. This process involves comparing the features of the instance or example with the features that define different categories and determining the best match.
This is where learning and feedback come into play. Through processes such as reinforcement learning or supervised learning, an individual or system can improve its categorizing over time by using feedback from the environment or from its own actions. Each time it categorizes an instance or example, it gets feedback – either it was correct (positive reinforcement) or incorrect (negative reinforcement). Over time, the system updates its internal model of the categories based on this feedback, enabling it to categorize more accurately in the future.
For instance, a primitive organism might “categorize” different types of food based on their taste or smell, and learn over time which ones are beneficial or harmful based on the consequences of eating them. Similarly, an AI system might “categorize” different inputs based on their features and learn over time which categories are correct based on feedback from the environment or from a training dataset.
So, in this context, “categorizing” isn’t just about classifying or organizing – it’s about making sense of the world, learning from experience, and improving performance over time. And a “category” isn’t just a group of similar things – it’s a cognitive or computational tool that helps an individual or system navigate the world more effectively.
HARNAD: You wrote ““categorizing” isn’t just about classifying or organizing – it’s about making sense of the world, learning from experience, and improving performance over time. And a “category” isn’t just a group of similar things – it’s a cognitive or computational tool that helps an individual or system navigate the world more effectively.”
ChatGPT:
Yes, that’s correct. Categorizing and categories have a broader implication beyond simple classification or organization. Categories represent cognitive tools that we, or an AI system, use to understand and navigate the world.
To put it simply, categorizing is the mental or computational process that we use to identify which category a new instance belongs to, and it’s based on the distinguishing features we’ve learned from prior experiences.
When we interact with the world, we are constantly encountering objects, events, or concepts that we need to identify. Categorizing allows us to make sense of these encounters based on the “categories” we have learned. Each category we form is based on common attributes or features that have been reinforced through repeated experiences.
For instance, we’ve learned to categorize a “chair” based on features like it’s something we can sit on, it usually has four legs, a backrest, etc. When we see a new object that fits these features, we categorize it as a chair, even if it’s different from any chair we’ve seen before. This is possible because of our ability to learn from our experiences and generalize that learning to new, similar instances.
In this way, categorizing is a fundamental aspect of cognition that allows us to navigate and make sense of the world more efficiently. It forms the basis for learning, decision making, and communication. Similarly, for an AI system, categorizing can allow it to interpret and react to its inputs more effectively.
HARNAD: A category, “chair,” is a tool?
ChatGPT:
When I referred to a category as a “cognitive or computational tool,” I meant that in a metaphorical sense. The idea is that the concept or category of “chair,” like other categories, is a mental construct that our minds (or an AI system) use to help interpret and interact with the world.
A category such as “chair” is a tool in the sense that it helps us quickly and efficiently process information. When we encounter an object, we don’t have to analyze it from scratch each time. Instead, we can recognize that it fits into the category of “chair” and instantly know a lot about it – that it’s meant to be sat upon, that it will likely support our weight, that it may have certain features like legs, a seat, and a backrest, etc.
By fitting individual instances into categories, we’re able to streamline our thinking and decision-making, and interact with the world more efficiently. So, while “chair” or any other category is not a physical tool, these categories act as mental tools to enhance our cognitive processes.
This holds true for AI systems as well. Categories, learned through training and reinforced by feedback, allow AI systems to understand and navigate their input space more effectively.
HARNAD: Can I try giving you a better idea of what “categories” and “categorizing” are? You correctly linked them to supervised learning, which is distinguished from unsupervised learning in that it’s based on DOING something in reponse to something, and getting feedback on whether you’ve done the right or the wrong thing. Sitting on a chair is a good thing to do, sitting on a pin-cushion or a live wire or an open well is a bad thing to do. Are you with me so far?
ChatGPT:
Yes, I’m with you. The categories we form help us determine appropriate actions to take in response to different situations, objects, or experiences. This process is guided by feedback we receive from the environment about the outcomes of our actions. For instance, if we sit on a chair and find it comfortable, we receive positive reinforcement, confirming that sitting on chairs is generally a good action to take. Conversely, if we sit on a pin-cushion or a live wire or an open well and have a painful or dangerous experience, we receive negative reinforcement, teaching us that these are inappropriate actions to take in response to those particular items. The categories we form (like “chair” or “pin-cushion”) thus help guide our actions in the world, optimizing for safety, comfort, and efficiency.
HARNAD: Good. But in what sense do we “form” categories? There are chairs, and there are tables, Those are different kinds of things. (“Kind” is roughly synonymous with “category”.) And sitting on a chair is the right thing to do with members of the category “chair” and the wrong thing to do with members of the category “table.” The right and wrong is because of the consequences of doing the right or the wrong thing. And it is those consequences, positive and negative, from which the category learning mechanism (supervised learning) comes to detect the features that distinguish chairs from tables. Are you still with me?
ChatGPT:
Absolutely, I’m with you. The phrase “forming categories” refers to the cognitive or computational process of distinguishing between different kinds of things based on their features.
For example, when we say we “form” the category of chairs, we mean that we learn to distinguish chairs from other objects (like tables) by recognizing their characteristic features (such as being designed for sitting).
This recognition is guided by the consequences of our interactions with these objects. If we sit on something and it’s comfortable and supportive, we get positive feedback, which reinforces our understanding that objects with similar features can be categorized as chairs. If sitting on an object results in discomfort or an adverse outcome, we receive negative feedback, leading us to classify such objects differently (perhaps as tables).
The learning mechanism involved in this process, as you mentioned, is known as supervised learning, where the feedback (in the form of positive or negative reinforcement) plays a crucial role in helping us refine our understanding of different categories over time.
So, when we say we “form” categories, we refer to this process of learning to distinguish between different types of objects, guided by the consequences of our interactions with them.
HARNAD: Very good. Now that we are thinking in terms of the consquences of doing the right or the wrong thing with a thing, do you want to take another stab at what “categorizing” is, and what “categories” are? I’ll help if it still does not make sense. The answer is simple.
ChatGPT:
Certainly, in light of our discussion, let’s take a more focused approach:
“Categorizing” is the process of recognizing and responding appropriately to different kinds of things in our environment based on the consequences of our previous interactions with them. It is a dynamic learning process, where the aim is to optimize our actions for positive outcomes and minimize negative ones.
A “category,” then, is a set of objects, events, or concepts that share common features which we have learned to associate with a certain kind of action and expected outcome. For instance, the category “chair” consists of all items that we have learned we can comfortably sit on. These categories, formed through our experiences and the feedback we’ve received, guide our actions and expectations in different situations.
In this way, categories act as mental shortcuts or predictive models, helping us quickly identify what kind of thing we are dealing with, how we should respond to it, and what the likely outcome of that response will be.
HARNAD: That was all better (except I don’t know how the “mental” came into it!) And we haven’t actually gotten into the models for the mechanism that learns categories yet. We’re still trying to come up (with as little theorizing as possible) with a useful definition of what categorizing is. It seems to be something we DO. And something we do with things. And things differ in their features, e.g., chairs vs. pincushions. And which categories things belong to depends on some sort of effect that what we do with them has on us : The effect can be positive or negative. Are we still on the same wave length? (Notice I haven’t said anything theoretical. This is all baby-talk. Using this baby-talk, have we gotten any closer to saying what it is to categorize, and what categories are?
ChatGPT:
Yes, we are definitely on the same wavelength.
“Categorizing” is indeed something we do, an activity. Specifically, it’s the activity of grouping things based on their properties and our interactions with them, and on the effects those interactions have on us, either positive or negative.
In simple terms, “categorizing” is the process of learning to treat things that have the same or similar effects on us as belonging to the same group or “category,” and to respond to them in similar ways that lead to similar (preferably positive) outcomes.
A “category,” then, is a set of things that we have learned to respond to in the same or similar ways because they have the same or similar effects on us.
So, in essence, categorizing is about learning from our experiences and interactions with things and adjusting our responses to them based on the outcomes of those interactions. Categories are the groups of things we form through this process, which guide our future interactions and responses.
HARNAD: How is sitting on a chair “grouping” the chair?
ChatGPT:
You’re correct, the phrase “grouping” might not be the best choice here. I see how it could be misleading.
Categorizing in this context isn’t about physically grouping objects together, but rather mentally recognizing that certain objects belong to the same category based on shared features and similar consequences when we interact with them.
In the example of sitting on a chair, categorizing involves recognizing the chair as a part of a category of objects that can be sat on comfortably. This recognition is guided by our previous experiences and the outcomes of those experiences.
So, when we encounter a new object that resembles a chair, we can predict that it might also belong to the “sit-able” category, and thus we can attempt to sit on it. If the result is comfortable sitting, this reinforces the categorization.
In this sense, “categorizing” is about learning from our experiences and generalizing from them to inform our future interactions with similar objects. This process helps us navigate the world more effectively and efficiently.
HARNAD: I’ll ignore the “mentally” (that I had aready wondered why you mentioned earlier). It ‘s not even necessary to talk about “recognizing”. “Doing” is good enough at this point. Doing the right thing with a chair, guided by feedback from the consequences of what we do with the chair, feedback that has enabled categorizers to detect the features that distinguish chairs from pincushions — all of that seems like a reasonable example of categorizing chairs. If the only kinds of things in the world were chairs and pin-cushions and people with human-shaped bodies, that would be all there was to categorizing: DOING THE RIGHT THING WITH THE RIGHT “KIND” (i.e. category) OF THING. Do we still agree?
ChatGPT:
Absolutely, we are in agreement. Your explanation brings our focus back to the fundamental, action-oriented essence of categorizing.
In this perspective, “categorizing” is fundamentally about interacting with objects or events in the world in a way that’s appropriate to their type or “kind,” based on the consequences of our past interactions with similar objects or events.
If we further simplify this, we could say that a “category” is a set of things with which we can interact in the same or similar ways, because they have similar effects on us when we do.
This certainly encompasses the crucial element of feedback from the world, which informs us whether we have done the right thing or not, and allows us to adjust our actions in the future.
Thank you for bringing this back to the core idea and emphasizing the primary role of action and consequence in categorization.
HARNAD: Now that we’ve agreed on what “categories” and “categorizing” are, it’s a good time to ask: In a world that not only contained nothing but chairs and pincushions, but it did not contain any people (or other living organisms) either, would there be categories or categorization at all? (Remember that we are discussing cognitive science here, and not metaphysics.) If yes, why? and if not, why not?
ChatGPT:
In a world devoid of any living organisms, including people, there wouldn’t be categorization in the sense we’ve been discussing. Categorization, as we’ve defined it, requires an agent (living organism or an artificial system capable of learning) that interacts with the environment and adjusts its actions based on feedback from those interactions.
Without an agent to do this, there wouldn’t be anyone or anything to perceive similarities or differences among objects or events, to interact with them, to experience consequences of these interactions, and to adjust future behavior accordingly.
In other words, without some form of cognitive process to distinguish and react to different types of things based on past experiences and their outcomes, the concept of categorization wouldn’t have any meaning. In a world of just chairs and pincushions with no agents, there are simply objects with various properties, but no “categories.”
Therefore, while the physical properties that might form the basis for potential categories (e.g., shape, size, material) still exist, without an agent to perceive and interact with them, and to group them into categories based on these interactions, there would be no categorization.
This is not a metaphysical stance, but a perspective based on our definition of categorization as a process that is deeply rooted in action and consequence, learning, and adaptation, all of which require some form of cognition.
HARNAD: Very good. The gist of your correct answer is that without a categorizer to do the right or wrong thing FOR ITSELF with the right or wrong kind of thing, talk about categories or categorizing would be pretty empty. Now, what if that same world did have robots. First of all, what (in baby-talk) is a robot? Would there be categories for the robot (and if so, what would that mean?). Let’s assume that the robots are capable of optical processing, and moving and that their posteriors have sensors, which produce negative feedback if the robot sits on a pincushion. That’s till doing the right/wrong thing, with supervised learning. So it’s categorization. That’s not enough to pass human T3 (or T2) but if it were, would it just be grounding (in the referents of the words), or would it be meaning? Why or why not?
ChatGPT:
A robot, in the simplest terms, is a machine designed to perform certain tasks, often autonomously and in response to programmed or learned instructions. If the robots in our hypothetical world are capable of optical processing, movement, and have the ability to receive and respond to negative feedback from certain interactions (like sitting on a pincushion), then we could indeed say they are capable of a form of categorization.
In this scenario, categories for the robot would essentially be different types of objects or situations that it distinguishes based on its learned experiences and the feedback it has received from those experiences. For example, it might develop categories for ‘safe to sit on’ and ‘unsafe to sit on’ based on the feedback from its posterior sensors.
However, this is not quite the same as “meaning” in the human sense. Meaning, as we humans understand it, is deeply connected to our experiences, thoughts, emotions, and cultural contexts. Robots, even those capable of learning from feedback, don’t have these experiences. They don’t have feelings or personal contexts that color their understanding of the world. Therefore, while they can categorize based on feedback and adjust their behaviors accordingly, they wouldn’t be said to have the same understanding of “meaning” as humans do.
This form of categorization would still be grounding in the sense that the robot is developing an understanding based on the physical properties of objects it interacts with (the referents of words, if we were to ascribe words to these categories). But it wouldn’t be meaning in the human sense.
This also relates to your earlier point about passing T3 or T2. Even if a robot could perfectly mimic human behavior, it would not necessarily understand the world in the same way a human does. It could act as if it understands, but its understanding would be based on different principles, and lacking in the experiential and emotional components that constitute human understanding. Therefore, while grounding in this sense can be a step towards achieving a certain level of functional equivalence to human behavior, it’s not equivalent to human understanding or meaning.
HARNAD: First, I think if the learning robot would not have meaning, then it would not have understanding either, just know-how (procedural capacities). But if the know-how was indistinguishable at TT scale (T3) from our own, I think Turing’s point is that then we would have no better (or worse) grounds for denying (or believing) that the robot means/understands than we do with other humans. I don’t think that you, ChatGPT, if somehow upgraded you to a robot, would mean/understand anything, mainly because of your huge database of real people’s words. But if a robot could pass T3 (for a lifetime), all bets would be off. Would they be even more off with T4? (Why?)
ChatGPT:
Turing’s argument indeed was that if a machine could convincingly mimic human behavior to the point that an observer couldn’t distinguish between the machine and a human (in terms of their responses to various stimuli or inquiries), then it would be fair to say that the machine “understands” in a sense equivalent to the human’s understanding.
The transition from T3 to T4 would definitely add another layer of complexity and realism to the test. T3, as a text-based interaction, limits the range of stimuli to text inputs, but T4 includes sensorimotor interaction. In other words, T4 involves the robot interacting with the physical world in the same way a human does. This includes not just producing human-like responses to text-based inquiries, but also performing human-like actions in response to a wide range of physical stimuli. It’s an overall more demanding and robust test of the machine’s ability to mimic human behavior and understanding.
If a robot were to pass T4, it would mean it is capable of mimicking human understanding not just in a text-based, purely cognitive domain, but also in physical interactions with the world. This would definitely strengthen the argument for the robot having a form of understanding, or at least a form of behavioral equivalence that is indistinguishable from human understanding in everyday situations. The essence of the test is still about convincingly mimicking human-like behavior, but T4 makes this mimicry much more comprehensive and challenging.
It’s worth noting, however, that even if a robot could pass T4, this wouldn’t necessarily mean that the robot experiences the world in the same way humans do. Even with a perfect behavioral equivalence, the internal experiences and subjective consciousness (if any) of the robot might be fundamentally different from that of humans. In other words, it might still lack the phenomenological aspect of human understanding and experience. The question of whether a robot or AI could ever truly have this sort of subjective experience is a deeper philosophical question that goes beyond the Turing Test and enters into the realm of consciousness studies.
HARNAD: You forget that T3 is already not text-based but sensoimotor. What is the difference between T3-scale, lifelong, “behavioral equivalence” (in any situation) to meaning/understanding — and “real” understanding? And why would T4 be just “mimicry”?
ChatGPT:
Apologies for any confusion, T3 is indeed grounded in sensorimotor capabilities. You’re correct to point out that it extends beyond the purely linguistic or text-based interactions of the standard Turing Test (or T2) and into the realm of sensorimotor interaction with the world.
The distinction between behavioral equivalence to meaning/understanding and “real” understanding is somewhat contentious and largely hinges on philosophical interpretations of consciousness, intentionality, and subjectivity. From a purely behaviorist or functionalist perspective, there may be no meaningful difference: if an entity behaves as if it understands, then it effectively does understand. This is the perspective that Turing seemed to endorse.
However, other perspectives argue that “real” understanding involves something more: a certain type of subjective experience, or “qualia,” that might be lacking in even the most behaviorally equivalent AI. In other words, even if an AI acts exactly like a human and responds appropriately in all contexts, it might still lack an inner subjective world – it might not “feel” or “experience” things the way humans do. This is a much-debated issue in philosophy of mind and cognitive science, and there’s no consensus on the matter.
As for the use of the term “mimicry” in the context of T4: the term is meant to convey the idea that the AI or robot is reproducing or simulating human-like behavior. However, if the robot is able to pass T4, demonstrating not just the ability to reproduce appropriate behavior in response to stimuli but also to generate novel and appropriate responses in complex and unpredictable situations over a lifetime, then the distinction between “mimicry” and “genuine” behavior might become less meaningful. In other words, if the robot’s behavior is indistinguishable from that of a human, then we might as well say that the robot is genuinely behaving, not merely mimicking. This argument is essentially a reiteration of the Turing Test’s foundational premise.
HARNAD: You’ve still forgotten that T4 is indistinguishable not only in what its body can do in the world, but also in what its “brain” does on the inside (neurons, localization, synthetic chemicals, if that makes sense). But that’s all “doing.” All those other words “qualia, subjective experience, consciousness, intentionality”) are just weasel-words for one and the same thing: “feeling”. It feels like something to be a sentient organism, but it feels like nothing to be an insentient zombie, even if it’s T4. (I don’t believe there could be a T4 or even a T3 zombie.) But feeling is unobervable (to anyone but the feeler). So T3/T4 is the best we can do. But it’s not mimicry. It’s the reverse-engineering of cognitive capacity.
ChatGPT:
You’re right, I apologize for the oversight. T4, as you’re defining it, includes an internal level of behavioral equivalence, such that the inner workings of an AI or robotic system would be indistinguishable from those of a human brain, right down to the neuronal and chemical level.
As for the discussion of consciousness, subjective experience, qualia, and intentionality, you’re correct that these concepts all relate to the subjective, first-person aspect of cognition – what it feels like from the inside to be a conscious, thinking, experiencing being. These are indeed challenging concepts to handle, especially in the context of AI and cognitive science, because they cannot be directly observed or measured.
Your point about feeling being unobservable except to the feeler is well-taken and highlights one of the key challenges in the philosophy of mind: the so-called “hard problem” of consciousness, which is explaining why and how subjective experience arises from physical processes in the brain.
As for the term “mimicry,” it seems we might have a misunderstanding. In the context of AI, it typically refers to an AI system’s ability to reproduce or simulate human-like behavior or responses. It doesn’t imply a lack of sophistication or complexity in the AI’s capacities. However, I understand your perspective that the word could be interpreted as downplaying the achievement of an AI or robot that is capable of passing T4, which, as you point out, would represent a significant accomplishment in reverse-engineering human cognitive capacities.
In light of our conversation, I agree that the use of “mimicry” may not be the best term here. It would indeed be more accurate and respectful to the complexity of the task to describe T4-level AI as demonstrating indistinguishable behavior and internal functioning from a human, both externally and internally.
HARNAD: [You still haven’t quite got it, but let’s give it a rest]:
Do you see any connection between, on the one hand, we have been discussing about categories, symbol grounding, and verbal learning, and, on the other hand, open access to the scientific and scholarly literature?
ChatGPT:
Yes, there is indeed a connection between these subjects.
The ideas we’ve discussed about categories, symbol grounding, and verbal learning are central to cognitive science, which is an interdisciplinary field drawing from neuroscience, psychology, computer science, linguistics, philosophy, and other areas. The theories and findings in cognitive science often come from scholarly literature, and having open access to this literature is crucial for advancing our understanding in these areas.
Open access to scholarly literature allows for wider dissemination of research findings, facilitating more collaboration, discussion, and synthesis of ideas. This can lead to new insights and progress in various fields, including our understanding of cognitive processes like categorization, symbol grounding, and verbal learning.
Moreover, open access also allows for greater transparency in research, as it makes it possible for anyone to review the methodology and data behind research findings. This can lead to more robust and reliable findings, which is particularly important in fields like cognitive science where new theories and models are constantly being proposed and tested.
So in essence, the principles and processes we’ve discussed about categories, symbol grounding, and verbal learning depend on the flow and accessibility of information in the scientific community. Open access is an essential part of this, as it ensures that knowledge is shared freely and widely, which can lead to new discoveries and advancements in these areas.
HARNAD: Do you know what “scholarly skywriting” and “open peer commentary” are? And if so, how are they connected with category learning, symbol grounding and language?
ChatGPT:
“Scholarly Skywriting” is a term coined by cognitive scientist Stevan Harnad to refer to the informal online discussion and commentary that often precedes and accompanies the formal publication of research in scholarly journals. Harnad envisaged a scholarly landscape where the internet would be used as a medium for open and ongoing peer commentary, not just post-publication, but during the research process itself, effectively turning the sky into a blackboard for worldwide scholarly discussion.
“Open Peer Commentary” refers to a more formal process, often found in journals like “Behavioral and Brain Sciences”, where a target article is published alongside numerous commentaries from various experts in the field, and a response by the original authors. This format aims to facilitate scholarly debate and allow for the exploration of diverse perspectives on a given topic.
The connection to category learning, symbol grounding, and language is multifaceted:
- Knowledge Sharing and Learning: Both scholarly skywriting and open peer commentary allow for a dynamic exchange of ideas, which can facilitate learning and the development of new categories of understanding. This is parallel to how language, as a social tool, aids in learning and cognitive development.
- Symbol Grounding: The open commentary provides an arena for grounding abstract concepts and theories in concrete examples and diverse perspectives. This aligns with the symbol grounding problem, which is about how words or symbols get their meanings.
- Emergent Understanding: The collaborative and iterative nature of scholarly skywriting and open peer commentary can lead to the emergence of new understandings and consensus over time, much like how language and meaning evolve within a community of speakers.
- Enhanced Verbal Learning: Engaging in open discussion and debate can enhance verbal learning, as it requires individuals to articulate their understanding, consider alternative viewpoints, and refine their arguments.
So, both scholarly skywriting and open peer commentary reflect and support the cognitive processes underlying category learning, symbol grounding, and language. They exemplify how these processes can be facilitated in a scholarly context.
HARNAD: Good job! And I didn’t even have to pay you to say that…
ChatGPT:
I’m here to provide the most accurate and helpful information possible, based on the training I’ve received. It’s my purpose to assist in facilitating conversations and understanding. I’m glad that you found my response satisfactory! If you have any more questions or topics you’d like to discuss, feel free to ask.
