Stevan Harnad

Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4.

Pain is not a single entity but is instead a collection of sensory experiences commonly associated with tissue damage. There is growing recognition that not all pains are equivalent, that pains and pathologies are not related in a simple manner, and that acute pains differ in many respects from persistent pains. Great strides have been made in improving our understanding of the neuronal mechanisms responsible for acute pain, but the studies leading to these advances have also led to the realization that a bewildering array of processes are interposed between tissue damage and sensations of pain, especially in persistent pains. Persistent pains often seem unrelated or disproportionate to identifiable pathology, and they are modulated by a multitude of factors. This complexity in such a vital function serves as a challenge both to scientists seeking fundamental understanding and to clinicians faced with the immediate need to treat patients with painful disorders

SUMMARY: Universities (the universal research-providers) as well as research funders (public and private) are beginning to make it part of their mandates to ensure not only that researchers conduct and publish peer-reviewed research (“publish or perish”), but that they also make it available online, free for all. This is called Open Access (OA), and it maximizes the uptake, impact and progress of research by making it accessible to all potential users worldwide, not just those whose universities can afford to subscribe to the journal in which it is published. Researchers can provide OA to their published journal articles by self-archiving them in their own university’s online repository. Students and junior faculty – the next generation of research providers and consumers -- are in a position to help accelerate the adoption of OA self-archiving mandates by their universities, ushering in the era of universal OA.

How many words—and which ones—are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a “Strongly Connected Subset” of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest—the graph's “minimum feedback vertex set” or MinSet—is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition.

Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem.

My purpose is to explain, first, that there is an alternative to Harnad's version of the symbol grounding problem, which is known as the problem of primitives; second, that there is an alternative to his solution (which is externalist) in the form of a dispositional conception (which is internalist); and, third, that, while the TTT, properly understood, may provide partial and fallible evidence for the presence of similar mental powers, it cannot supply conclusive proof, because more than observable symbolic manipuation and robotic behavior is involved here, as he admits (Harnad 1991). Carrying the problem further appears to require inference to the best explanation.

2. Invariant Sensorimotor Features ("Affordances"). To say this is not to declare oneself a Gibsonian, whatever that means. It is merely to point out that what a sensorimotor system can do is determined by what can be extracted from its motor interactions with its sensory input. If you lack sonar sensors, then your sensorimotor system cannot do what a bat's can do, at least not without the help of instruments. Light stimulation affords color vision for those of us with the right sensory apparatus, but not for those of us who are color-blind. The geometric fact that, when we move, the "shadows" cast on our retina by nearby objects move faster than the shadows of further objects means that, for those of us with normal vision, our visual input affords depth perception. From more complicated facts of projective and solid geometry it follows that a 3-dimensional shape, such as, say, a boomerang, can be recognized as being the same shape Ð and the same size Ð even though the size and shape of its shadow on our retinas changes as we move in relation to it or it moves in relation to us. Its shape is said to be invariant under these sensorimotor transformations, and our visual systems can detect and extract that invariance, and translate it into a visual constancy. So we keep seeing a boomerang of the same shape and size even though the shape and size of its retinal shadows keep changing

In our century a Frege/Brentano wedge has gradually been driven into the mind/body problem so deeply that it appears to have split it into two: The problem of "qualia" and the problem of "intentionality." Both problems use similar intuition pumps: For qualia, we imagine a robot that is indistinguishable from us in every objective respect, but it lacks subjective experiences; it is mindless. For intentionality, we again imagine a robot that is indistinguishable from us in every objective respect but its "thoughts" lack "aboutness"; they are meaningless. I will try to show that there is a way to re-unify the mind/body problem by grounding the "language of thought" (symbols) in our perceptual categorization capacity. The model is bottom-up and hybrid symbolic/nonsymbolic

When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one

We are accustomed to thinking that a primrose is "concrete" and a prime number is "abstract," that "roundness" is more abstract than "round," and that "property" is more abstract than "roundness." In reality, the relation between "abstract" and "concrete" is more like the (non)relation between "abstract" and "concave," "concrete" being a sensory term [about what something feels like] and "abstract" being a functional term (about what the sensorimotor system is doing with its input in order to produce its output): Feelings and things are correlated, but otherwise incommensurable. Everything that any sensorimotor system such as ourselves manages to categorize successfully is based on abstracting sensorimotor "affordances" (invariant features). The rest is merely a question of what inputs we can and do categorize, and what we must abstract from the particulars of each sensorimotor interaction in order to be able to categorize them correctly. To categorize, in other words, is to abstract. And not to categorize is merely to experience. Borges's Funes the Memorious, with his infinite, infallible rote memory, is a fictional hint at what it would be like not to be able to categorize, not to be able to selectively forget and ignore most of our input by abstracting only its reliably recurrent invariants. But a sensorimotor system like Funes would not really be viable, for if something along those lines did exist, it could not categorize recurrent objects, events or states, hence it could have no language, private or public, and could at most only feel, not function adaptively (hence survive). Luria's "S" in "The Mind of a Mnemonist" is a real-life approximation whose difficulties in conceptualizing were directly proportional to his difficulties in selectively forgetting and ignoring. Watanabe's "Ugly Duckling Theorem" shows how, if we did not selectively weight some properties more heavily than others, everything would be equally (and infinitely and indifferently) similar to everything else. Miller's "Magical Number Seven Plus or Minus Two" shows that there are (and must be) limitations on our capacity to process and remember information, both in our capacity to discriminate relatively (detect sameness/difference, degree-of-similarity) and in our capacity to discriminate absolutely (identify, categorize, name), The phenomenon of categorical perception shows how selective feature-detection puts a Whorfian "warp" on our feelings of similarity in the service of categorization, compressing within-category similarities and expanding between-category differences by abstracting and selectively filtering inputs through their invariant features, thereby allowing us to sort and name things reliably. Language does allow us to acquire categories indirectly through symbolic description....

William Gardner's proposal to establish a searchable, retrievable electronic archive is fine, as far as it goes. The potential role of electronic networks in scientific publication, however, goes far beyond providing searchable electronic archives for electronic journals. The whole process of scholarly communication is currently undergoing a revolution comparable to the one occasioned by the invention of printing. On the brink of intellectual perestroika is that vast PREPUBLICATION phase of scientific inquiry in which ideas and findings are discussed informally with colleagues, presented more formally in seminars, conferences and symposia, and distributed still more widely in the form of preprints and tech reports that have undergone various degrees of peer review. It has now become possible to do all of this in a remarkable new way that is not only incomparably more thorough and systematic in its distribution, potentially global in scale, and almost instantaneous in speed, but so unprecedentedly interactive that it will substantially restructure the pursuit of knowledge

The causal structure of cognition can be simulated but not implemented computationally, just as the causal structure of a furnace can be simulated but not implemented computationally. Heating is a dynamical property, not a computational one. A computational simulation of a furnace cannot heat a real house (only a simulated house). It lacks the essential causal property of a furnace. This is obvious with computational furnaces. The only thing that allows us even to imagine that it is otherwise in the case of computational cognition is the fact that cognizing, unlike heating, is invisible (to eveyrone except the cognizer). Chalmers’s “Dancing Qualia” Argument is hence invalid: Even if there could be a computational model of cognition that was behaviorally indistinguishable from a real, feeling cognizer, it would still be true that if, like heat, feeling is a dynamical property of the brain, a flip-flop from the presence to the absence of feeling would be undetectable anywhere along Chalmers’s hypothetical component-swapping continuum from a human cognizer to a computational cognizer -- undetectable to everyone except the cognizer. But that would only be because the cognizer was locked into being incapable of doing anything to settle the matter simply because of Chalmers’s premise of input/output indistinguishability. That is not a demonstration that cognition is computation; it is just the demonstation that you get out of a premise what you put into it. But even if the causal topography of feeling, hence of cognizing, is dynamic rather than just computational, the problem of explaining the causal role played by feeling itself – how and why we feel – in the generation of our behavioral capacity – how and why we can do what we can do – will remain a “hard” (and perhaps insoluble) problem