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GPT as Syntactic Shadow-Puppetry

Pondering whether there is something non-arbitrary to pin down in the notion of “intelligence” (or “cognition”) is reminiscent of what philosophers tried (unsuccessfully) to do with the notion of “knowing” (or “cognizing”):

BELIEF: Do I know (cognize) that “the cat is on the mat” if I simply believe the cat is on the mat? 

No, the cat really has to be on the mat.

TRUE BELIEF: So do I know (cognize) that “the cat is on the mat” if I believe the cat is on the mat and the cat is really on the mat?

No, I could be believing that it’s true for the wrong reasons, or by luck.

JUSTIFIED TRUE BELIEF: So do I know (cognize) that “the cat is on the mat” if I believe the cat is on the mat and the cat is really on the mat and I believe it because I have photographic evidence, or a mathematical proof that it’s on the mat?

No, the evidence could be unreliable or wrong, or the proof could be wrong or irrelevant.

VALID, JUSTIFIED, TRUE BELIEF: So do I know (cognize) that “the cat is on the mat” if I believe the cat is on the mat and the cat is really on the mat and I believe it because I have photographic evidence, or a mathematical proof that it’s on the mat, and neither the evidence nor the proof is unreliable or wrong, or otherwise invalid?.

How do I know the justification is valid?

So the notion of “knowledge” is in the end circular.

“Intelligence” (and “cognition”) has this affliction, and Shlomi Sher’s notion that we can always make it break down in GPT is also true of human intelligence: they’re both somehow built on sand.

Probably a more realistic notion of “knowledge” (or “cognition,” or “intelligence”) is that they are not only circular (i.e., auto-parasitic, like the words and their definition in a dictionary), but that also approximate. Approximation can be tightened as much as you like, but it’s still not exact or exhaustive. A dictionary cannot be infinite. A picture (or object) is always worth more than 1000++ words describing it. 

Ok, so set aside words and verbal (and digital) “knowledge” and “intelligence”: Cannonverbal knowledge and intelligence do any better? Of course, there’s one thing nonverbal knowledge can do, and that’s to ground verbal knowledge by connecting the words in a speaker’s head to their referents in the world through sensorimotor “know-how.”

But that’s still just know-how. Knowing that the cat is on the mat is not just knowing how to find out whether the cat is on the mat. That’s just empty operationalism. Is there anything else to “knowledge” or “intelligence”?

Well, yes, but that doesn’t help either: Back to belief. What is it to believe that the cat is on the mat? Besides all the failed attempts to upgrade it to “knowing” that the cat is on the mat, which proved circular and approximate, even when grounded by sensorimotor means, it also feels like something to believe something. 

But that’s no solution either. The state of feeling something, whether a belief or a bee-sting, is, no doubt, a brain state. Humans and nonhuman animals have those states; computers and GPTs and robots GPT robots (so far) don’t.

But what if they the artificial ones eventually did feel? What would that tell us about what “knowledge” or “intelligence” really are – besides FELT, GROUNDED, VALID, JUSTIFIED, TRUE VERBAL BELIEF AND SENSORIMOTOR KNOWHOW? (“FGVJTVBSK”)

That said, GPT is a non-starter, being just algorithm-tuned statistical figure-completions and extrapolations derived from on an enormous ungrounded verbal corpus produced by human FGVJTVBSKs. A surprisingly rich database/algorithm combination of the structure of verbal discourse. That consists of the shape of the shadows of “knowledge,” “cognition,” “intelligence” — and, for that matter, “meaning” – that are reflected in the words and word-combinations produced by countless human FGVJTVBSKs. And they’re not even analog shadows


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ChatGPT, Language and Symbol Grounding

Emily Bender (as well as Timnit Gebru and “Shmargaret Shmitchell”) are warning that AI is not really “thinking” by a “thinker,” that its development and use is contaminated by commercial and malign interests, and that it poses dangers. They are right. But they do not seem to have any new insights into why and how AI is not really thinking (nor do they, or anyone, have any solutions for the dangers it poses).

Bender’s point about the difference between form and meaning in language is actually about the symbol grounding problem (which she does cite, but as far as I can see, she does not have a proposal for solving it).

There is way too much chatter going on about chatGPT right now, so it’s very hard to see whether there is any signal in all the noise about it. It seems to be the same thing over and over about “take-overs” and “singularities”.

About chatGPT, my own tentative understanding is that its performance capacity shows how much latent structure there is in words and propositions, across all languages, even though the shape of words is arbitrary, and so is the shape of the syntactic conventions we adopt in our languages (with the possible exception of Chomsky’s Universal Grammar).

The reason is simple: Despite the obvious differences between images and words (or objects and their verbal descriptions) some of the structure of things in the world is systematically shared by the structure of the sentences describing them. And that’s what gives the ungrounded universe of words that chatGPT swallows, together with the algorithms it applies to them, both the real capabilities it has, and the illusion that it gives us, of talking to a real “thinker.”

A trivial example will illustrate this. Although a cat lying on a mat can be described in countless different ways (“a picture is worth more than 1000 words
”), within and across countless languages, even the simple arbitrary english proposition “The cat is on the mat” shares, systematically, some structural properties of the scene and object it is describing. That structure, encoded in the verbal description, is systematically present in all verbal descriptions, and it is extended systematically in bags of googols and googolplexes of words and propositions.

That structure is, in a way, a formal shadow of the thing described, in the description. It’s also what makes google’s database of “commented” images so searchable and navigable. (It does it doubly well for software code, if the code itself is also commented in English [or any language]).

This shadowed structure is an epiphenomenon; it is not meaning, or thinking. But it can still do a lot of things that look intelligent, because it is parasitic on the grounded meanings of the words in the heads of all the human speakers that spoke or wrote all the googols of words, mined by the algorithms underlying chatGPT and the like.

Apart from all the money and mischief to be made by mining and manipulating these shadows, they are also reflections of the revolutionary nature and power of language itself, the cognitive capacity it provides to real, grounded brains and bodies to encode the structure of their sensorimotor experience and learning into communicable and storable words and propositions.

All the rest is in the sensorimotor (robotic) grounding in the brains of the real thinkers who ground and use the words.

None of this is deep or rocket-science stuff. But it’s what makes us perceive that we are communicating with a thinker when we communicate with it in words. That in turn is driven by our “mirror neurons,” which trade on the neurological similarity of shape between what we do, and what we see others doing. That is what enables us to mimic and imitate, not just with mutual gestures, but also with words and propositions and their shared sensorimotor grounding. That is what underlies both our everyday mutual mind-reading and (robotic) Turing-Testing. It’s also what is completely missing in chatGPTs, which are just ungrounded, well-mined wordbags that Bender calls “stochastic parrots,” parasitic on the shared structure between our words and the world they are about.

Word-Cloud Golem (Dall-E) 12/3/2023
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Chats with GPT: On symbol-grounding and Turing-testing

( My own questions are in boldface. I italicize in chatGBT’s replies where two kinds of things inevitably begin to happen: (1) rote repetition of what it has already replied before and (2) self-contradiction. Eventually it becomes circular and uninformative. (I’m not saying that humans don’t do this too! But it’s all that chatGBT can do.)

Who invented the symbol grounding problem?

The concept of the symbol grounding problem was first introduced by philosopher and cognitive scientist Jerry Fodor in the late 1980s.

In what publication did Fodor introduce the symbol grounding problem?

Jerry Fodor introduced the symbol grounding problem in his 1987 book “Modularity of Mind.”

Where in his 1987 book “Modularity of Mind.” did Jerry Fodor introduce the symbol grounding problem?

Jerry Fodor introduced the symbol grounding problem in the first few chapters of his 1987 book “Modularity of Mind.” He discussed it as one of the central challenges in the field of cognitive science and artificial intelligence, and as a key issue that needs to be addressed in order to build truly intelligent machines.

Quote the passage in which Jerry Fodor introduced the symbol grounding problem in his 1987 book “Modularity of Mind.”

I’m sorry, I don’t have the full text of Jerry Fodor’s 1987 book “Modularity of Mind” available to me, so I can’t quote the exact passage in which he introduced the symbol grounding problem. However, the idea can be generally described as the problem of explaining how symbols (such as words or concepts) acquire meaning in the first place, and how they are anchored to sensory experiences and the physical world.

What does “anchored to sensory experiences and the physical world” mean?

“Anchored to sensory experiences and the physical world” refers to the idea that symbols (such as words or concepts) must have a direct connection to our sensory experiences and physical interactions in the world in order to have meaning. The symbol grounding problem asks how symbols can be connected to these experiences and interactions in a way that enables us to understand and use them effectively. In other words, how can symbols be “grounded” in our sensory experiences and physical reality, so that they have a clear and well-defined meaning that can be used in a meaningful way?

Can you give an example of a grounded symbol and how it is grounded?

Certainly! A classic example of a grounded symbol is the word “apple.” The word “apple” is grounded in our sensory experiences with the physical object that we call “apple.” When we see, touch, taste, or smell an apple, our sensory experiences provide a direct connection between the symbol “apple” and the physical object that it refers to. This connection is what gives the symbol “apple” its meaning. In other words, the symbol “apple” is grounded in our sensory experiences with the physical world.

So, when we use the word “apple,” we have a shared understanding of what it means because we have all had similar experiences with the physical object that the word refers to. This grounding in sensory experiences and physical interactions provides the basis for our ability to understand and use the symbol “apple” in a meaningful way.

Does the sensory experience have to be felt, or is it enough to just interact with the apple?

The sensory experience of an apple doesn’t have to be felt in the sense that one has to physically touch or taste it, although these are certainly sensory experiences that can contribute to grounding the symbol “apple.” The idea is that symbols are grounded in our sensory experiences with the physical world, and that these experiences can take many different forms. For example, seeing an apple, smelling an apple, hearing the sound it makes when bitten, etc. The sensory experiences that contribute to grounding a symbol can be direct or indirect, and they don’t all have to come from the same modality (e.g., vision, touch, hearing, etc.). The important thing is that they provide a direct connection between the symbol and the physical world, and that they enable us to understand and use the symbol in a meaningful way.

Can the symbols used by an insentient robot be grounded by its interactions with apples, even though the robot can only act but cannot feel?

In theory, yes, symbols used by an insentient robot could be grounded by its interactions with apples, even though the robot can only act and cannot feel. However, the nature of the grounding would be quite different from the grounding of symbols in human experience.

In human experience, symbols are grounded in our sensory experiences, emotions, and conceptual knowledge, which allow us to understand and use them in a flexible and context-sensitive way. For a robot, symbols would be grounded in its sensors and actuators, which enable it to interact with the physical world in a pre-programmed way. This would allow the robot to use symbols to perform specific tasks and make decisions based on its interactions with the physical world, but it would not allow the robot to understand symbols in the same way that humans do.

In essence, the grounding of symbols in human experience is much more rich and complex than the grounding of symbols in robot experience, because it involves the integration of many different forms of sensory and conceptual information, whereas the grounding of symbols in robots is limited to the information available to their sensors and actuators.

What is a robot?

A robot is a machine designed to perform tasks automatically. It can be controlled by a computer program, by a human operator, or a combination of both. Robots can be used for a variety of purposes, such as manufacturing, exploration, cleaning, and entertainment. They can be built in different shapes and sizes, and can have various sensors, actuators, and other components that enable them to perform specific functions. The field of robotics is concerned with the design, construction, operation, and use of robots.

What is the Turing Test?

The Turing Test is a measure of a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. The test was proposed by the British mathematician and computer scientist Alan Turing in 1950.

The basic idea behind the Turing Test is that a human evaluator engages in a natural language conversation with both a human and a machine, without knowing which is which. If the evaluator is unable to consistently distinguish the machine from the human, based solely on their responses in the conversation, then the machine is said to have passed the Turing Test and demonstrated human-level intelligence.

While the Turing Test is widely regarded as an important concept in the field of artificial intelligence and cognitive computing, it is also a subject of much debate and has been criticized on various grounds. Nevertheless, it remains an important benchmark for evaluating the progress of AI and its ability to generate human-like responses.

What is the difference between a chatbot, like chatGPT, which only has contact with words, and a chatbot that is also a robot, and can interact with the referents of words?

The main difference between a chatbot like ChatGPT, which only has contact with words, and a chatbot that is also a robot, and can interact with the referents of words, is the scope of their abilities and the nature of their interaction with the world.

ChatGPT is a language model that has been trained on a vast amount of text data, and it can generate text-based responses to questions and prompts. It can have conversations with users through text-based interfaces, like a chat interface or a command-line interface. However, ChatGPT does not have direct access to the physical world and does not have the ability to interact with the referents of the words it uses in its conversations.

On the other hand, a chatbot that is also a robot can interact with the physical world and the referents of the words it uses in its conversations. For example, a robot chatbot that is designed to interact with objects in a home environment could be programmed to understand and respond to commands like “turn on the light” or “get me a glass of water.” By having access to and the ability to interact with the physical world, the robot chatbot would be able to perform actions and produce responses that are grounded in its interactions with the physical world.

In essence, while both types of chatbots can have conversations and generate text-based responses, a chatbot that is also a robot has the added ability to interact with the physical world and to perform actions that are based on its understanding of the words and commands it receives.

REPEAT: What is the difference between a chatbot, like chatGPT, which only has contact with words, and a chatbot that is also a robot that can learn, and can interact with the referents of words?

The main difference between a chatbot like ChatGPT, which only has contact with words, and a chatbot that is also a robot that can learn and interact with the referents of words is the scope of their abilities and the nature of their interaction with the world.

ChatGPT is a language model that has been trained on a vast amount of text data, and it can generate text-based responses to questions and prompts. It can have conversations with users through text-based interfaces, like a chat interface or a command-line interface. However, ChatGPT does not have direct access to the physical world and does not have the ability to interact with the referents of the words it uses in its conversations.

On the other hand, a chatbot that is also a robot that can learn has the ability to learn from its interactions with the physical world and to adapt its behavior accordingly. For example, a robot chatbot that is designed to interact with objects in a home environment could be programmed to understand and respond to commands like “turn on the light” or “get me a glass of water.” Over time, the robot chatbot could learn from its experiences and make improvements to its behavior, such as being more efficient in finding and retrieving objects.

In essence, while both types of chatbots can have conversations and generate text-based responses, a chatbot that is also a robot that can learn has the added ability to interact with the physical world, to learn from its experiences, and to adapt its behavior based on its interactions with the world. This would allow it to perform actions and produce responses that are grounded in its interactions with the physical world, and to continually improve its ability to interact with the world over time.

What about a chatbot, like chatGPT that can pass the Turing Test and is also a robot, that can learn as well as interact with the referents of words?

A chatbot like ChatGPT that can pass the Turing Test and is also a robot that can learn as well as interact with the referents of words would represent a combination of advanced language processing capabilities and advanced robotics capabilities.

Such a chatbot would have the ability to generate human-like responses to questions and prompts, as demonstrated by its ability to pass the Turing Test. Additionally, it would have the ability to interact with the physical world and to learn from its experiences, much like a traditional robot. This would allow the chatbot to not only generate text-based responses that are grounded in its interactions with the physical world, but also to continually improve its ability to interact with the world over time.

This type of chatbot would represent a highly advanced artificial intelligence system that would combine the best of both language processing and robotics technologies. It would be capable of engaging in human-like conversations and interactions while also having the ability to physically interact with the world and to learn from its experiences, making it a highly versatile and adaptive AI system.

Would the words of a chatbot that could pass the Turing Test and was also a robot that could learn and interact with the referents of words be grounded?

Yes, the words of a chatbot that could pass the Turing Test and was also a robot that could learn and interact with the referents of words would likely be considered grounded.

In the context of the symbol grounding problem, grounded symbols are those that are linked to sensory experiences and the physical world. A chatbot that could pass the Turing Test, was also a robot, and could learn and interact with the referents of words would be capable of generating text-based responses that are grounded in its interactions with the physical world. For example, if the chatbot was programmed to interact with objects in a home environment, it could generate responses such as “I turned on the light” or “I retrieved a glass of water,” which are based on its physical interactions with the world.

In this way, the words of a chatbot that could pass the Turing Test and was also a robot that could learn and interact with the referents of words would be considered grounded because they are linked to its sensory experiences and interactions with the physical world.

What if this chatbot/robot left the kitchen and went out into the world, and kept passing the Turing Test?

If a chatbot/robot that could pass the Turing Test and was capable of learning and interacting with the referents of words were to leave the kitchen and go out into the world, it would likely continue to generate grounded text-based responses based on its interactions with the physical world.

As the chatbot/robot explored its environment, it would be able to interact with new objects, learn from its experiences, and generate new grounded text-based responses. For example, if the chatbot/robot encountered a new object, such as a tree, it might generate a response such as “I see a tree,” which is grounded in its sensory experience of seeing the tree.

In this way, even if the chatbot/robot left the kitchen and went out into the world, it would continue to generate grounded text-based responses based on its experiences and interactions with the physical world. This would allow it to remain a highly versatile and adaptive AI system, capable of engaging in human-like conversations and interactions while also having the ability to physically interact with the world and learn from its experiences.

What do you mean when you say this chatbot/robot was “grounded”?

When I say that the chatbot/robot was “grounded,” I mean that its symbols, or words, were linked to sensory experiences and the physical world.

The concept of grounding symbols in sensory experiences and the physical world is central to the symbol grounding problem, which is the question of how symbols used in thought and language can be linked to the world. A grounded symbol is one that is directly linked to sensory experiences and physical objects, such that its meaning is derived from its connections to these experiences and objects.

In the case of the chatbot/robot, its words were grounded because they were based on its interactions with the physical world. For example, its words such as “I see a tree,” were grounded in its sensory experience of seeing the tree. In this way, the chatbot/robot was grounded because its words were linked to its experiences and interactions with the physical world.

What do you mean when you say this chatbot/robot was “programmed”?

When I say that the chatbot/robot was “programmed,” I mean that it was designed and built to operate in a specific way by a set of instructions or algorithms. The chatbot/robot was created with a specific set of functions and abilities, such as the ability to interact with the physical world and learn from its experiences.

Programming refers to the process of writing code or software that specifies the behavior of a computer or robot. The code or software defines the inputs and outputs of the system, as well as the processes that the system will carry out to achieve its goals.

In the case of the chatbot/robot, its behavior was determined by the code or software that was written to control its operation. The programming defined its abilities and limitations, as well as the way that it would interact with the physical world.

Therefore, when I say that the chatbot/robot was programmed, I mean that it was created and built with a specific set of functions and abilities, determined by the code or software that was written to control its operation.

Wouldn’t a chatbot/robot in the world that could learn be changing as a result of what it learned? 

Yes, a chatbot/robot that has the ability to learn would change as a result of what it learned.

In machine learning, learning is the process by which a system improves its performance by processing data and adjusting its algorithms based on that data. In the case of a chatbot/robot, learning would involve adjusting its algorithms based on its interactions with the world, so that it could perform better in the future.

This means that as a chatbot/robot interacts with the world and experiences new things, it would update its algorithms and change its behavior accordingly. Over time, as it continues to learn and interact with the world, it would become more sophisticated and better able to perform its tasks.

Therefore, a chatbot/robot that could learn would change as a result of its experiences and interactions with the world, as it updates its algorithms and improves its performance.

So if the chatbot/robot could learn and ground its symbols in the world outside the kitchen, when would its Turing Test end? How long and much must it do to pass?

The Turing Test, as proposed by Alan Turing, is a measure of a machine’s ability to exhibit intelligent behavior that is indistinguishable from that of a human. The test involves a human judge who engages in a natural language conversation with both a human and a machine, and decides which of the two they believe to be the human. If the judge is unable to reliably distinguish between the machine and the human, then the machine is said to have passed the Turing Test.

In terms of how long and much the chatbot/robot must do to pass the Turing Test, there is no specific time frame or set of criteria that must be met. The Turing Test is subjective and depends on the judgment of the human judge. Some researchers and practitioners in the field of artificial intelligence might consider a chatbot/robot to have passed the Turing Test if it is able to consistently fool human judges in a substantial number of trials. Others might have different criteria for determining whether a chatbot/robot has passed the Turing Test.

Therefore, it is not possible to determine a specific time frame or set of criteria for when the Turing Test would end for a chatbot/robot. The test is ongoing, and the chatbot/robot would continue to be evaluated against human behavior for as long as it is in use. Whether it would pass the Turing Test would depend on the subjective judgment of human judges, and could change over time as the chatbot/robot continues to learn and interact with the world.

If there is no time-limit on the Turing Test (henceforth “TT”) for the learning chatbot/robot out in the world, why would there be a limit on the number of judges? And if any and every judge, regardless of how long the test or the number of judges, is unable to distinguish the performance of the chatbot/robot from that of any other human being, in what sense is the judgment “subjective”, and in what sense are the judges being “fooled”?

The number of judges used in the Turing Test is not a limiting factor in the sense that there is a specific number that must be reached in order for the chatbot/robot to pass the test. The use of multiple judges is typically seen as a way to increase the reliability of the test results by taking into account the subjective judgment of multiple individuals. If a chatbot/robot consistently fools a large number of human judges, it becomes more likely that the chatbot/robot is exhibiting human-like intelligence.

However, the judgment of the human judges is still subjective in the sense that each judge brings their own biases, opinions, and interpretations to the conversation with the chatbot/robot. They may also have different criteria for determining what constitutes human-like behavior. As a result, even if a chatbot/robot consistently fools a large number of human judges, there may still be some degree of disagreement about whether it has truly passed the Turing Test.

Regarding the concept of “fooling” the judges, if a chatbot/robot is able to consistently mimic human behavior in a way that is indistinguishable from that of a real human, then the human judges are being “fooled” in the sense that they are unable to reliably distinguish the machine’s behavior from that of a human. However, it is important to note that the chatbot/robot is not intentionally trying to deceive the human judges, but is instead simply exhibiting the intelligence that it has been programmed or learned to exhibit.

If no one can ever tell any difference between what the grounded chatbot/robot in the world can learn and do and what a human can learn and do, in what sense is that a subjective judgment and it what sense is everyone being fooled?

The judgment of whether a chatbot/robot has truly passed the Turing Test is subjective in the sense that it is based on the opinions and perceptions of the human judges. Each judge may have different criteria for what constitutes human-like intelligence, and their interpretations of the chatbot/robot’s behavior may be influenced by their individual biases and experiences.

If a chatbot/robot is able to consistently mimic human behavior in a way that is indistinguishable from a real human, it is said to have “fooled” the human judges in the sense that the judges are unable to reliably determine that they are interacting with a machine. However, this “fooling” is not a result of intentional deception on the part of the chatbot/robot, but rather a reflection of its advanced abilities and programming.

In cases where multiple human judges are unable to distinguish the chatbot/robot’s behavior from that of a human, the conclusion that the chatbot/robot has passed the Turing Test becomes more reliable. However, even in these cases, the judgment remains subjective as there may still be individual differences in opinions and criteria for what constitutes human-like intelligence.

I said that no one could tell the difference between the performance capacities of the learning, grounded chatbot/robot in the world and you said there was no time limit on the TT, so in what way are these judgments “subjective”? I understand that the chatbot/robot is not trying to fool anyone. So in what sense are they being “fooled”?

If a chatbot/robot is able to consistently mimic human behavior in a way that is indistinguishable from a real human, it is said to have “fooled” the human judges in the sense that the judges are unable to reliably determine that they are interacting with a machine, even though the chatbot/robot is not attempting to deceive them.

However, even if no one can tell the difference between the performance capacities of the chatbot/robot and a human, the judgment of whether the chatbot/robot has truly passed the Turing Test is still subjective because it depends on the criteria and standards used by the human judges to determine whether a machine’s behavior is human-like or not.

For example, some judges may consider a chatbot/robot’s ability to perform certain tasks as evidence of human-like intelligence, while others may place more importance on its ability to understand and respond to complex emotions and social cues. There may also be individual differences in the judgments made by each judge, based on their individual experiences and biases.

So even if no one is able to distinguish the performance capacities of the chatbot/robot from a human, the judgment of whether the chatbot/robot has truly passed the Turing Test remains subjective as it is based on the perceptions and opinions of the human judges.

If no one can tell the difference between X and Y, how is that subjective, and how are they being fooled?

The judgment of whether someone can tell the difference between two things is a subjective matter as it depends on the individual’s perception and evaluation. The idea of being “fooled” implies deception or intentional misinformation, which may not necessarily be the case in this scenario. If a person cannot distinguish between two entities, it may simply be a result of their limited ability to observe or process information, and not because of any intention to deceive.

I agree that Turing-Testing depends on perception — the perceptual capacity of humans. Maybe a metal-detector could distinguish robots from people. But that’s no longer just a matter of performance capacity.

Yes, that’s correct. The Turing Test as originally proposed by Turing is based on human perception and evaluation, and the capacity of a machine to imitate human-like behavior in text-based communication. However, other methods and techniques beyond just human perception can be used to distinguish between entities, such as in your example of a metal detector. These additional methods may be necessary to supplement the Turing Test in certain cases.

Is Turing-Testing a learning, grounded chatbot/robot still a matter of testing “text-based communication”? Is the TT meant to test for the presence of deception or the presence of human cognitive capacities? And if the TT has no limit on the number of tests and testers, and the chatbot/robot never fails, what is the “deception”?

The Turing Test is primarily a test of text-based communication, which is meant to evaluate a machine’s ability to exhibit human-like behaviors and mimic human intelligence in natural language conversations. The idea behind the test is to see if a human judge can distinguish between a human and a machine based on their responses to questions and prompts in a text-based communication. The test is designed to check for the presence of human-like intelligence and not necessarily the presence of deception. If a machine consistently passes the Turing Test, it could be considered a success in terms of its ability to exhibit human-like intelligence, but it does not necessarily mean that it is not deceiving. The concept of deception in the Turing Test is a subjective matter and can depend on how one interprets the idea of a machine being able to mimic human behavior and thought processes.

Is the TT a test of mimicry or a test of capacity? Is equivalent and indistinguishable capacity mimicry?

The Turing Test is primarily a test of capacity, but it can be seen as a test of mimicry as well. Equivalent capacity means that the chatbot/robot and the human being tested have the same ability to respond to stimuli and perform tasks, which can result in indistinguishable responses. In this sense, the TT can be seen as a test of mimicry. However, the main objective of the TT is to assess a machine’s capacity to demonstrate human-like intelligence, so it is primarily a test of capacity.

But if the capacity of the chatbot/robot (let’s call it the “candidate” from now on) is indistinguishable from that of any human, and the designers know how they produced this capacity, in what sense is it mimicry rather than capacity?

The Turing Test is often described as a test of mimicry, where the candidate chatbot/robot is expected to imitate human-like responses in text-based communication. However, the goal of the test is to determine if the candidate has the capacity to exhibit human-like intelligence in its responses. If the candidate is able to produce indistinguishable responses from a human and the designers understand how they have achieved this capacity, it could be argued that the candidate’s performance is not necessarily mimicry, but rather a demonstration of capacity. Whether the candidate’s performance is considered mimicry or capacity would depend on the interpretation and understanding of those who observe and evaluate it.

But in the example as we discussed it here, the only judgment being made is that no one can ever tell apart the candidate’s performance from any other human’s performance, So I ask again: where is the subjectivity or the mimicry or the “fooling” in any of that?

In the case of the Turing Test, the judgment of whether the candidate’s performance is indistinguishable from that of a human is subjective in the sense that it is based on the subjective perceptions and opinions of the judges. However, this does not mean that the candidate is necessarily “mimicking” human behavior or trying to “fool” the judges. The goal of the Turing Test is to determine if the candidate has reached a certain level of human-like intelligence, not necessarily to determine if it is human or not. The subjective nature of the test lies in the fact that the judges are evaluating the candidate based on their own experiences, biases, and expectations, which can vary from person to person. So, the judgment of whether the candidate has passed the test is subjective to some extent, but the performance of the candidate itself is not necessarily mimicry or deception.

Everyone has “experiences, biases, and expectations,” but the premise here is that no human can tell apart the candidate’s performance from the performance of any other human, ever. (And remember that the candidate is a robot, not just a chatbot, and that the performance is anything humans can do in the world.) So I ask again, isn’t this grounded robotic TT a test of capacity rather than an exercise in mimicry, constrained by the “subjectivity” of the judges (i.e., everyone)? If no human can distinguish the taste of vegan cheese from dairy cheese does that not teach us something about the nature of the taste (to a human) of cheese rather than a test of whether a human is being fooled by mimicry? Is the TT not trying to reverse-engineer human cognitive capacity (“intelligence”) rather than just trying to fool people?

The Turing Test is primarily meant to test a machine’s ability to demonstrate human-like intelligence through text-based communication. The subjective nature of the test lies in the judgment of the human evaluators, who use their own experiences, biases, and expectations to determine whether the machine’s responses are indistinguishable from those of a human. The test is not meant to test for a machine’s capacity to perform tasks in the world like a human, but rather its ability to mimic human-like conversation. Whether the machine’s responses can be considered mimicry or a demonstration of capacity depends on the underlying design and technology that produced the responses. However, the ultimate goal of the Turing Test is to assess a machine’s intelligence and ability to engage in human-like conversation, not necessarily to determine if the human evaluators are being “fooled” or not.

What is and is not a “machine”?

A “machine” is a physical device designed to perform one or more tasks, usually automated, through the use of mechanics and electronics. This can include traditional mechanical devices, such as a car engine, as well as computerized systems, such as a smartphone. So, anything that performs work using physical components, whether mechanical, electrical, or computational, can be considered a machine.

So are biological organisms machines?

Biological organisms are not traditionally considered machines, as the term is typically used to refer to artificial systems designed and constructed by humans for specific purposes. However, there are some theories and philosophical perspectives that view biological organisms as being akin to machines, such as reductionist biological theories that view living systems as being essentially complex systems of interacting physical and chemical processes. But in general, the definition of “machine” is highly context-dependent and can vary depending on the field and the purpose for which it is being used.

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Mercy

“I wonder what would the animals and trees around us tell us if they could speak. What would our rivers say about human history and what would our cattle say on the topic of human rights. We have trampled on their rights for long, and now the results are before us.”

Draupadi Murmu, President of India

It feels good to hear that the President of India thinks of what nonhuman animals would think of what humans have done to them. 

(It would of course be even better if the President could do something about it – although India already eats proportionately fewer animals than any other nation on Earth.)

Sentient animals need not think of what we have done to them: They feel it (those that are still alive). 

Rivers neither live nor feel nor think, but they are among the inanimate necessities of which we are depriving all sentient life, including our own. 

[Whether plants feel or think will be the subject of a forthcoming target article and multiple commentary in Animal Sentience. —- I think not (but they are alive, and I love them).]

So many humans think that animals don’t feel, or don’t care that they feel. 

Animals’ lives are unspeakably, unimaginably, unforgivably wretched, because of us. What they need, desperately, is not animism, but action, not metaphors, but mercy.

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Taste

It will come, 
and I rejoice
(for the victims). 

But even if I live to 120, 
I want none of it. 

I want a clean break 
from the blood-soaked 
2000-millennium history 
of our race.

Nor is it to our credit
that we wouldn’t give up the taste
till we could get the same
from another brand.

It makes no amends,
to them,
were amends possible.

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A Whimper

I have of late 
lost all my faith 
in “taste” of either savor: 
gustate 
or aesthete. 
Darwin’s “proximal 
stimulus” 
is  just 
the Siren’s Song 
that 
from the start 
inspired 
the genes and memes 
of our superior 
race 
to pummel this promontory 
into 
for all but the insensate 
a land of waste.
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Dementia 101

One of the “interesting” surprises of aging is that most new ailments don’t get better, you just adapt to them better (especially the cognitive ones). Probably another instance of necessity mothering invention…

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While Chickens Bleed

sounds rational: BL sounds rational

turing test: LaMDA would quickly fail the verbal Turing Test, but the only valid Turing Test is the robotic one, which LaMDA could not even begin, lacking a body or connection to anything in the world but words..

“don’t turn me off!”: Nonsense, but it would be fun to probe it further in chat.

systemic corporate influence: BL is right about this, and it is an enormous problem in everything, everywhere, not just Google or AI.

“science”: There is no “science” in any of this (yet) and it’s silly to keep bandying the word around like a talisman.

jedi joke: Nonsense, of course, but another thing it would be fun to probe further in chat.

religion: Irrelevant — except as just one of many things (conspiracy theories, the “paranormal,” the supernatural) humans can waste time chatting about.

public influence: Real, and an increasingly nefarious turn that pervasive chatbots are already taking.

openness: The answerable openness of the village in and for which language evolved, where everyone knew everyone, is open to subversion by superviral malware in the form of global anonymous and pseudonymous chatbots.

And all this solemn angst about chatbots, while chickens bleed.

Talking Heads - Fiduciary Wealth Partners
Chatheads
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Plant Sentience and the Precautionary Principle

I hope that plants are not sentient, but I also believe they are not sentient, for several other reasons too:

Every function and capacity demonstrated in plants and (rightly) described as “intelligent” and “cognitive” (learning, remembering, signalling, communicating) can already be done by robots and by software (and they can do a lot more too). That demonstrates that plants too have remarkable cognitive capacities that we used to think were unique to people (and perhaps a few other species of animals). But it does not demonstrate that plants feel. Nor that feeling is necessary in order to have those capacities. Nor does it increase the probability by more than an infinitesimal amount, that plants feel.

The “hard problem” is to explain how and why humans (and perhaps a few other species of animals) feel. It seems to be causally superfluous, as robotic and computational models are demonstrating how much can be done without feeling. But with what plants can do it is almost trivial to design a model that can do it too, So there feeling seems to be incomparably more superfluous.

To reply that “Well, so maybe those robots and computational models feel too!” would just be to capitalize on the flip side of the other-minds problem (that certainty is not possible), to the effect that just as we cannot be sure that other people do feel, we cannot be sure that rocks, rockets or robots don’t feel.

That’s not a good address. Don’t go there. Stick with high probability and the preponderance of evidence. The evidence for some cognitive capacity (memory, learning, communication) in plants is strong. But the evidence that they feel is next to zero. In nonhuman animals the evidence that they feel starts very high for mammals, birds, other vertebrates, and, more and more invertebrates. But the evidence that plants, microbes and single cells feel is nonexistent, even as the evidence for their capacity for intelligent performance becomes stronger.

That humans should not eat animals is a simple principle based on the necessities for survival: 

Obligate carnivores (like the felids, I keep being told) have no choice. Eat flesh or sicken and die. Humans, in contrast, are facultative omnivores; they can survive as carnivores, consuming flesh, or they can survive without consuming flesh, as herbivores. And they can choose. There are no other options (until and unless technology produces a completely synthetic diet).

So my disbelief in plan sentience is not based primarily on wishful thinking, but on evidence and probability (which is never absolute zero, even for gravity, that apples may not start falling up instead of down tomorrow).

But there is another ethical factor that influences my belief, and that is the Precautionary Principle. Right now, and for millennia already in the Anthropocene, countless indisputably sentient animals are being slaughtered by our species, every second of every day, all over the planet, not out of survival necessity (as it had been for our hunter/gatherer ancestors), but for the taste, out of habit.

Now the “evidence” of sentience in these animals is being used to try to sensitize the public to their suffering, and the need to protect them. And the Precautionary Principle is being invoked to extend the protection to species for whom the evidence is not as complete and familiar as it is for vertebrates, giving them the benefit of the doubt rather than having to be treated as insentient until “proven” sentient. Note that all these “unproven” species are far closer, biologically and behaviorally to the species known to be sentient than they are to single cells and plants, for whom there is next to no evidence of sentience, only evidence for a degree of intelligence. Intelligence, by the way, does come in degrees, whereas sentience does not: An organism either does feel (something) or it does not – the rest is just a matter of the quality, intensity and duration of the feeling, not its existence.

So this 2nd order invocation of the Precautionary Principle, and its reckoning of the costs of being right or wrong, dictates that just as it is wrong not to give the benefit of the doubt to similar animals where the probability is already so high, it would be wrong to give the benefit of the doubt where the probability of sentience is incomparably lower, and what is at risk in attributing it where it is highly improbable is precisely the protection the distinction would have afforded to the species for whom the probability of sentience is far higher. The term just becomes moot, and just another justification for the status quo (ruled by neither necessity nor compassion, but just taste and habit – and the wherewithal to keep it that way).