James Jensen's List: Foundations of AI

• Artificial intelligence (AI) would be the possession of intelligence, or the exercise of thought, by machines such as computers.

  • A nice definition — I should probably quote it.
• Philosophically, the main AI question is “Can there be such?” or, as Alan Turing put it, “Can a machine think?”

  • Wonderful statement of the problem.
• What makes this a philosophical and not just a scientific and technical question is the scientific recalcitrance of the concept of intelligence or thought and its moral, religious, and legal significance.

  • In other words, making AI a purely scientific problem would require (a) establishing a scientific paradigm for consciousness and (b) ignoring or diffusing the moral/religious/legal issues involved. Neither seems likely to occur soon.
• In European and other traditions, moral and legal standing depend not just on what is outwardly done but also on inward states of mind. Only rational individuals have standing as moral agents and status as moral patients subject to certain harms, such as being betrayed. Only sentient individuals are subject to certain other harms, such as pain and suffering.
• if they are really thinking, are they not, then, owed similar rights to rational human beings?
• “we wish to exclude from the machines” in question “men born in the usual manner” (Alan Turing), or even in unusual manners such as in vitro fertilization or ectogenesis. And if nonhuman animals think, we wish to exclude them from the machines, too.

  • A conception of machines that includes humans and animals would shed no light on the problem of AI. We want to isolate the paradigmatic examples of intelligence from the machines in question to ask *those* machines can also be examples of intelligence.
• the AI thesis should be understood to hold that thought, or intelligence, can be produced by artificial means; made, not grown
• Since the present interest in thinking machines has been aroused by a particular kind of machine, an electronic computer or digital computer, present controversies regarding claims of artificial intelligence center on these.
• the scientific discipline and engineering enterprise of AI has been characterized as “the attempt to discover and implement the computational means” to make machines “behave in ways that would be called intelligent if a human were so behaving” (John McCarthy), or to make them do things that “would require intelligence if done by men” (Marvin Minsky). These standard formulations duck the question of whether deeds which indicate intelligence when done by humans truly indicate it when done by machines: that’s the philosophical question.

  • The philosophical problems seems to me somewhat like a special case of the general epistemological appearance-reality problem: are appearances (here the outward deeds) a reliable indicator of reality (the possession of intelligence)?
• weak AI grants the fact (or prospect) of intelligent-acting machines; strong AI says these actions can be real intelligence. Strong AI says some artificial computation is thought

  • I'm tempted to say that, in this area, weak AI maintains the appearance-reality distinction while strong AI collapses it, but I don't think that's right.
• Computationalism says that all thought is computation.

  • On the surface of it, computationalism seems to be going backwards: from building computers to do what minds do to infering that minds are like computers. The fact that thought can perform computation does not imply that thoughts *are* computation.
• some artificial computation being thought is consistent with some thought not being computation, contra computationalism. All thought being computation is consistent with some computation (and perhaps all artificial computation) not being thought.

  • So computationalism would seem irrelevant to the question at hand.
• Intelligence might be styled the capacity to think extensively and well.

  • "Behavioral complexity" as I've discussed it so far in my paper.
• Thinking well centrally involves apt conception, true representation, and correct reasoning.

  • "True representation." *cringe*
• The extent or breadth of a thing’s thinking concerns the variety of content it can conceive, and the variety of thought processes it deploys.
• the more extensively a thing thinks, the higher the “level” (as is said) of its thinking

  • I would think that higher levels would involves meta-thinking, but maybe that's part of this.

• we need to distinguish two different AI questions:

   

   

   
  1. Can machines think at all?
   
  2. Can machine intelligence approach or surpass the human level?
• Still, to the extent that what is called “thinking” in us is paradigmatic for what thought is, the question of human level intelligence may arise anew at the foundations. Do insects think at all? And if insects … what of “bacteria level intelligence” (Brooks 1991a)? Even “water flowing downhill,” it seems, “tries to get to the bottom of the hill by ingeniously seeking the line of least resistance” (Searle 1989). Don’t we have to draw the line somewhere? Perhaps seeming intelligence – to really be intelligence – has to come up to some threshold level.

  • Davidson's ideas imply that insects do not think, because thought, language, and intentions are a package deal. Water cannot even have intentions unless it has language capable of expressing those intentions, and without language it cannot think, since thinking consists of exchanging propositions (expressed in language) for other propositions.
    
    However, water, insects, and machines can be intelligent in the sense of being behaviorally complex: they exhibit varyingly complex reactions to their environment. In the case of water, this is seeking the ground; in the case of insects, seeking foods and other highly-complex entities.
    
    So it would seem that many intelligent activities do not require thought.
• While we don’t know what thought or intelligence is, essentially, and while we’re very far from agreed on what things do and don’t have it, almost everyone agrees that humans think, and agrees with Descartes that our intelligence is amply manifest in our speech.
• The doings of many machines – some much simpler than computers – inspire us to describe them in mental terms commonly reserved for animals. Some missiles, for instance, seek heat, or so we say. We call them “heat seeking missiles” and nobody takes it amiss.
• Our working characterizations of computers are rife with low-level mental attributions: we say they detect key presses, try to initialize their printers, search for available devices, and so forth. Even those who would deny the proposition “machines think” when it is explicitly put to them, are moved unavoidably in their practical dealings to characterize the doings of computers in mental terms, and they would be hard put to do otherwise.
• “at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted”
• Theorem proving and mathematical exploration being their home turf, computers have displayed not only human-level but, in certain respects, superhuman abilities here.
• Today such techniques enjoy widespread application in expert systems like MYCIN, in logic tutorial software, and in computer languages such as PROLOG. There are even original mathematical discoveries owing to computers. Notably, K. Appel, W. Haken, and J. Koch (1977a, 1977b), and computer, proved that every planar map is four colorable – an important mathematical conjecture that had resisted unassisted human proof for over a hundred years. Certain computer generated parts of this proof are too complex to be directly verified (without computer assistance) by human mathematicians.
• Game playing engaged the interest of AI researchers almost from the start.
• Though some in AI disparaged Deep Blue’s reliance on “brute force” application of computer power rather than improved search guiding heuristics, we may still add chess to checkers (where the reigning “human-machine machine champion” since 1994 has been CHINOOK, the machine), and backgammon, as games that computers now play at or above the highest human levels. Computers also play fair to middling poker, bridge, and Go – though not at the highest human level. Additionally, intelligent agents or “softbots” are elements or participants in a variety of electronic games.
• An ironic revelation of robotics research is that abilities such as object recognition and obstacle avoidance that humans share with “lower” animals often prove more difficult to implement than distinctively human “high level” mathematical and inferential abilities that come more naturally (so to speak) to computers.
• Knowledge representation embodies concepts and information in computationally accessible and inferentially tractable forms.
• Adept at pattern recognition, such networks seem notably capable of forming concepts on their own based on feedback from experience and exhibit several other humanoid cognitive characteristics besides.

  • If I've read Harnad right, this would still entail that the Chinese Room's main point is correct: cognition is not *just* computation.
• Languages are symbol systems and (serial architecture) computers are symbol crunching machines, each with its own proprietary instruction set (machine code) into which it translates or compiles instructions couched in high level programming languages like LISP and C.
• One of the principle challenges posed by natural languages is the proper assignment of meaning. High-level computer languages express imperatives which the machine “understands” procedurally by translation into its native (and similarly imperative) machine code: their constructions are basically instructions. Natural languages, on the other hand, have – perhaps principally – declarative functions: their constructions include descriptions whose understanding seems fundamentally to require rightly relating them to their referents in the world.
• Low level intelligent action is pervasive, from thermostats (to cite a low tech. example) to voice recognition (for example, in cars, cell-phones, and other appliances responsive to spoken verbal commands) to fuzzy controllers and “neuro fuzzy” rice cookers. Everywhere these days there are “smart” devices.
• Computationalism, as already noted, says that all thought is computation, not that all computation is thought. Computationalists, accordingly, may still deny that the machinations of current generation electronic computers comprise real thought or that these devices possess any genuine intelligence; and many do deny it based on their perception of various behavioral deficits these machines suffer from. However, few computationalists would go so far as to deny the possibility of genuine intelligence ever being artificially achieved.

• Dualism – holding that thought is essentially subjective experience – would underwrite the following argument:

   

   

   
  1. Thought is some kind of conscious experience. (Dualism)
   
  2. Machines can’t have conscious experiences.   therefore,
   
  3. Machines can’t think.

• Mind-brain identity theory – holding that thoughts essentially are biological brain processes – yields yet another argument:

   

   

   
  1. Thoughts are specific biological brain processes. (Mind-Brain Identity)
   
  2. Artificial computers can’t have biological brain processes. (By our initial definition of the “artificial” in AI, above).    therefore,
   
  3. Artificial computers can’t think.
• On the other hand, such bald mind-brain identity as the anti-AI argument premises seems too speciesist to be believed. Besides AI, it calls into doubt the possibility of extraterrestrial, perhaps all nonmammalian, or even all nonhuman, intelligence. As plausibly modified to allow species specific mind-matter identities, on the other hand, it would not preclude computers from being considered distinct species themselves.

  • I disagree. The dissimilarities between two types of biological brains would not be nearly as great as the differences between a biological brain and a computer. And if we *did* find a biological brain that was so greatly dissimilar, we'd at worst have the same problem we have with machines. The take-away lesson is that the only sure paradigmatic example of a thinking device that we have is the human brain, which is biological: we are therefore more justified in imputing thought to a biological brain of a different sort thinking than to a non-biological brain, all else being equal.
• Whereas we have strong inclinations to say computers see, seek, and infer things we have scant inclinations to say they ache or itch or experience ennui. Nevertheless, to be sustained, this objection requires reason to believe that thought is inseparable from feeling. Perhaps computers are just dispassionate thinkers. Indeed, far from being regarded as indispensable to rational thought, passion traditionally has been thought antithetical to it.
• Ordinarily, when one understands a language (or possesses certain other intentional mental states) this is apparent both to the understander (or possessor) and to others: subjective “first-person” appearances and objective “third-person” appearances coincide.
• he Chinese room experiment fails because acceptance of its putative result – that the person in the room doesn’t understand – already presupposes the dualist hypothesis over computationalism or mind-brain identity theory.
• There’s nothing that it’s like, subjectively, to be a computer. The “light” of consciousness is not on, inwardly, for them. There’s “no one home.” This is due to their lack of felt qualia. To equip computers with sensors to detect environmental conditions, for instance, would not thereby endow them with the private sensations (of heat, cold, hue, pitch, and so forth) that accompany sense-perception in us: such private sensations are what consciousness is made of.
• according to this objection it must be inconceivable that Data 2.0 really is sentient. But certainly we can conceive that he is – indeed, more easily than not, it seems.
• At least it may be concluded that since current computers (objective evidence suggests) do lack feelings – until Data 2.0 does come along (if ever) – we are entitled, given computers’ lack of feelings, to deny that the low-level and piecemeal high-level intelligent behavior of computers bespeak genuine subjectivity or intelligence.
• This objection conflates subjectivity with sentience. Intentional mental states such as belief and choice seem subjective independently of whatever qualia may or may not attend them
• Weak AI doesn’t necessarily entail strong AI, but prima facie it does.

  • Not sure I agree with this.
• At the basic level, and fragmentarily at the human level, computers do things that we credit as thinking when humanly done; and so should we credit them when done by nonhumans, absent credible theoretic reasons against.

  • Seems like an equivocation over "thinking" here. When we credit animals with "thinking" we don't necessarily mean that they think in the same way we do—trading propositions for other propositions.

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• I'm always surprised to find people who "doubt" strong AI.... [H]uman beings are themselves examples of strong AI. I find it amusing to hear people arguing that they cannot, in fact, exist...
• Of course, I think it will be much cheaper to make adult human-level AI entities via a twenty-year production process using unskilled labor--at least half of all possible two human teams can do so!--then employing skilled computer scientists. But what do I know?

• a way of dealing with the question whether machines can think

  • Note, not of *answering* the question, but of *dealing with* it. Reminds me of Rorty.
• the question whether machines can think is itself “too meaningless” to deserve discussion

  • Sounds like logical positivism.
• whether a digital computer can do well in a certain kind of game

  • Sounds Wittgensteinian. I kind of like it.
• If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men. The first is that they could never use words, or put together signs, as we do in order to declare our thoughts to others. For we can certainly conceive of a machine so constructed that it utters words, and even utters words that correspond to bodily actions causing a change in its organs. … But it is not conceivable that such a machine should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as the dullest of men can do. Secondly, even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, but only from the disposition of their organs. For whereas reason is a universal instrument, which can be used in all kinds of situations, these organs need some particular action; hence it is for all practical purposes impossible for a machine to have enough different organs to make it act in all the contingencies of life in the way in which our reason makes us act.

  • Descartes fails to anticipate electronic computers that can be programmed to respond to stimuli, including language.
• it seems to follow that Descartes would have agreed that the Turing Test would be a good test of his confident assumption that there cannot be thinking machines

  • So apparently, a machine that could pass this test would not be a "mere machine."
• Ned Block's “Blockhead” thought experiment is often said to be a (putative) knockdown objection to The Turing Test. (Block (1981) contains a direct discussion of The Turing Test in this context.) Here, what a proponent of this view has in mind is the idea that it is logically possible for an entity to pass the kinds of tests that Descartes and (at least allegedly) Turing have in mind—to use words (and, perhaps, to act) in just the kind of way that human beings do—and yet to be entirely lacking in intelligence, not possessed of a mind, etc.
• There are at least two kinds of questions that can be raised about Turing's Imitation Game. First, there are empirical questions, e.g., Is it true that we now—or will soon—have made computers that can play the imitation game so well that an average interrogator has no more than a 70 percent chance of making the right identification after five minutes of questioning? Second, there are conceptual questions, e.g., Is it true that, if an average interrogator had no more than a 70 percent chance of making the right identification after five minutes of questioning, we should conclude that the machine exhibits some level of thought, or intelligence, or mentality?
• Some people have suggested that the Turing Test is chauvinistic: it only recognizes intelligence in things that are able to sustain a conversation with us. Why couldn't it be the case that there are intelligent things that are unable to carry on a conversation, or, at any rate, unable to carry on a conversation with creatures like us? (See, for example, French (1990).)

  • The obvious Wittgenstein/Davidson reply is that were this the case, we couldn't even recognize them as language-users and so could never be justified in thinking them to have intelligence in *our* sense.
• What Turing claims is only that, if something can carry out a conversation with us, then we have good grounds to suppose that that thing has intelligence of the kind that we possess; he does not claim that only something that can carry out a conversation with us can possess the kind of intelligence that we have.

  • In other words, it is sufficient, if perhaps not necessary, for a system to pass the Turing Test in order for us to be justified in declaring it intelligent in the sense that we are.

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• “according to strong AI,” according to Searle, “the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states”
• Searle also insists the systems reply would have the absurd consequence that “mind is everywhere.” For instance, “there is a level of description at which my stomach does information processing” there being “nothing to prevent [describers] from treating the input and output of my digestive organs as information if they so desire.”
• Searle also charges that the robot reply “tacitly concedes that cognition is not solely a matter of formal symbol manipulation” after all, as “strong AI” supposes, since it “adds a set of causal relation[s] to the outside world” (1980a, p. 420).
• it’s “not. . . how I know that other people have cognitive states, but rather what it is that I am attributing when I attribute cognitive states to them. The thrust of the argument is that it couldn’t be just computational processes and their output because the computational processes and their output can exist without the cognitive state”
• The Many Mansions Reply suggests that even if Searle is right in his suggestion that programming cannot suffice to cause computers to have intentionality and cognitive states, other means besides programming might be devised such that computers may be imbued with whatever does suffice for intentionality by these other means.
• This too, Searle says, misses the point: it “trivializes the project of Strong AI by redefining it as whatever artificially produces and explains cognition” abandoning “the original claim made on behalf of artificial intelligence” that “mental processes are computational processes over formally defined elements.” If AI is not identified with that “precise, well defined thesis,” Searle says, “my objections no longer apply because there is no longer a testable hypothesis for them to apply to”

•  

  (C3) Any artifact that produced mental phenomena, any artificial brain, would have to be able to duplicate the specific causal powers of brains, and it could not do that just by running a formal program.
   (C4) The way that human brains actually produce mental phenomena cannot be solely by virtue of running a computer program.
• One tack, taken by Daniel Dennett (1980), among others, decries the dualistic tendencies discernible, for instance, in Searle’s methodological maxim “always insist on the first-person point of view” (Searle 1980b, p. 451). Another tack notices that the symbols Searle-in-the-room processes are not meaningless ciphers, they’re Chinese inscriptions. So they are meaningful; and so is Searle’s processing of them in the room; whether he knows it or not.
• Though Searle unapologetically identifies intrinsic intentionality with conscious intentionality, still he resists Dennett’s and others’ imputations of dualism. Given that what it is we’re attributing in attributing mental states is conscious intentionality, Searle maintains, insistence on the “first-person point of view” is warranted; because “the ontology of the mind is a first-person ontology”: “the mind consists of qualia [subjective conscious experiences] . . . right down to the ground” (1992, p. 20)
• This commonsense identification of thought with consciousness, Searle maintains, is readily reconcilable with thoroughgoing physicalism when we conceive of consciousness as both caused by and realized in underlying brain processes. Identification of thought with consciousness along these lines, Searle insists, is not dualism; it might more aptly be styled monist interactionism (1980b, p. 455-456) or (as he now prefers) “biological naturalism” (1992, p. 1).
• Imagine, if you will, a Chinese gymnasium, with many monolingual English speakers working in parallel, producing output indistinguishable from that of native Chinese speakers: each follows their own (more limited) set of instructions in English. Still, Searle insists, obviously, none of these individuals understands; and neither does the whole company of them collectively. It’s intuitively utterly obvious, Searle maintains, that no one and nothing in the revised “Chinese gym” experiment understands a word of Chinese either individually or collectively. Both individually and collectively, nothing is being done in the Chinese gym except meaningless syntactic manipulations from which intentionality and consequently meaningful thought could not conceivably arise.
• Since “it is not conceivable,” Descartes says, that a machine “should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as even the dullest of men can do” (1637, Part V), whatever has such ability evidently thinks.
• Searle-in-the-room behaves as if he understands Chinese; yet doesn’t understand: so, contrary to Behaviorism, acting (as-if) intelligent does not suffice for being so; something else is required. But, contrary to Functionalism this something else is not – or at least, not just – a matter of by what underlying procedures (or programming) the intelligent-seeming behavior is brought about: Searle-in-the-room, according to the thought-experiment, may be implementing whatever program you please, yet still be lacking the mental state (e.g., understanding Chinese) that his behavior would seem to evidence.
• Debate over the Chinese room thought experiment – while generating considerable heat – has proven inconclusive. To the Chinese room’s champions – as to Searle himself – the experiment and allied argument have often seemed so obviously cogent and decisively victorious that doubts professed by naysayers have seemed discreditable and disingenuous attempts to salvage “strong AI” at all costs. To the argument’s detractors, on the other hand, the Chinese room has seemed more like “religious diatribe against AI, masquerading as a serious scientific argument” (Hofstadter 1980, p. 433) than a serious objection.
• If Searle’s positive views are basically dualistic – as many believe – then the usual objections to dualism apply, other-minds troubles among them; so, the “other-minds” reply can hardly be said to “miss the point”. Indeed, since the question of whether computers (can) think just is an other-minds question, if other minds questions “miss the point” it’s hard to see how the Chinese room speaks to the issue of whether computers really (can) think at all.
• To show that thought is not just computation (what the Chinese room — if it shows anything — shows) is not to show that computers’ intelligent seeming performances are not real thought (as the “strong” “weak” dichotomy suggests) .

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• The paper pointed out that a "Chinese-Understanding" computer program would not really understand Chinese because someone who did not understand Chinese (e.g., Searle himself) could execute the same program while still not understanding Chinese; hence the computer executing the program would not be understanding Chinese either.
• Whatever cognition actually turns out to be -- whether just computation, or something more, or something else -- cognitive science can only ever be a form of "reverse engineering" (Harnad 1994a) and reverse-engineering has only two kinds of empirical data to go by: structure and function (the latter including all performance capacities).
• if one rejects the premise that a computer could ever pass T2, one plays into Searle's hands, as one does if one holds that T2 is not a strong enough test, or that implementational details do matter.
• Because of (2), any and every implementation of that T2-passing program must have the mental states in question, if they are truly just computational states. In particular, each of them must understand. Fair enough. But now Searle brings out his intuition pump, adding that we are to imagine this computer as passing T2 in Chinese; and we are asked to believe (because it is true) that Searle himself does not understand Chinese. It remains only to note that if Searle himself were executing the computer program, he would still not be understanding Chinese. Hence (by (2)) neither would the computer, executing the very same program. Q.E.D. Computationalism is false.
• just as it is no refutation (but rather an affirmation) of the CRA to deny that T2 is a strong enough test, or to deny that a computer could ever pass it, it is merely special pleading to try to save computationalism by stipulating ad hoc (in the face of the CRA) that implementational details do matter after all, and that the computer's is the "right" kind of implementation, whereas Searle's is the "wrong" kind. This just amounts to conceding that tenet (2) is false after all
• variants on the Systems Reply, to the effect that it was unreasonable to suppose that Searle should be understanding under these conditions; he would be only a part of the implementing system, whereas it would be the system as a whole that would be doing the understanding
• he suggested to the Systematists that if they were really ready to believe that whereas he alone would not be understanding under those conditions, the "room" as a whole, consisting of him and the symbol-strewn walls, would be understanding, then they should just assume that he had memorized all the symbols on the walls; then Searle himself would be all there was to the system
• A more interesting gambit was to concede that no conscious understanding would be going on under these conditions, but that unconscious understanding would be, in virtue of the computations.
• the force of his CRA depends completely on understanding's being a conscious mental state, one whose presence or absence one can consciously (and hence truthfully) ascertain and attest to (Searle's Periscope)
• But Searle also needs no defense against this revised notion of understanding, for it only makes sense to speak of unconscious mental states (if it makes sense at all) in an otherwise conscious entity
• Unconscious states in nonconscious entities (like toasters) are no kind of mental state at all.
• We're ready to believe that we "know" a phone number when, unable to recall it consciously, we find we can nevertheless dial it when we let our fingers do the walking. But finding oneself able to exchange inscrutable letters for a lifetime with a pen-pal in this way would be rather more like sleep-walking, or speaking in tongues (even the neurological syndrome of "automatic writing" is nothing like this; Luria 1972). It's definitely not what we mean by "understanding a language," which surely means conscious understanding.
• Normally, if someone claims that an entity -- any entity -- is in a mental state (has a mind), there is no way I can confirm or disconfirm it. This is the "other minds" problem. We "solve" it with one another and with animal species that are sufficiently like us through what has come to be called "mind-reading" (Heyes 1998) in the literature since it was first introduced in BBS two years before Searle's article (Premack & Woodruff 1978). But of course mind-reading is not really telepathy at all, but Turing-Testing -- biologically prepared inferences and empathy based on similarities to our own appearance, performance, and experiences.
• although we can never become any other physical entity than ourselves, if there are indeed mental states that occur purely in virtue of being in the right computational state, then if we can get into the same computational state as the entity in question, we can check whether or not it's got the mental states imputed to it. This is Searle's Periscope, and a system can only protect itself from it by either not purporting to be in a mental state purely in virtue of being in a computational state -- or by giving up on the mental nature of the computational state, conceding that it is just another unconscious (or rather nonconscious) state -- nothing to do with the mind.
• Searle thought that the CRA had invalidated the Turing Test as an indicator of mental states. But we always knew that the TT was fallible; like the CRA, it is not a proof. Moreover, it is only T2 (not T3 or T4 REFS) that is vulnerable to the CRA, and even that only for the special case of an implementation-independent, purely computational candidate. The CRA would not work against a non-computational T2-passing system; nor would it work against a hybrid, computational/noncomputational one (REFS), for the simple reason that in neither case could Searle be the entire system; Searle's Periscope would fail. Not that Systematists should take heart from this, for if cognition is hybrid, computationalism is still false.
• the CRA shows that cognition cannot be all just computational

  • Computationalism seems mostly irrelevant to the question of strong AI, but this quote might be useful.
• In showing that the purely computational road would not lead to London, did he leave us as uncertain as before about where the right road to London might be? I think not, for his critique has helped open up the vistas that are now called "embodied cognition" and "situated robotics," and they have certainly impelled me toward the hybrid road of grounding symbol systems in the sensorimotor (T3) world with neural nets.
• There are still plenty of degrees of freedom in both hybrid and noncomputational approaches to reverse-engineering cognition without constraining us to reverse-engineering the brain (T4). So cognitive neuroscience cannot take heart from the CRA either. It is only one very narrow approach that has been discredited: pure computationalism.

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• A creature that we cannot interpret as capable of meaningful speech will thus also be a creature that we cannot interpret as capable of possessing contentful attitudes. Such considerations lead Davidson to deny that non-linguistic animals are capable of thought — where thought involves the possession of propositional attitudes such as beliefs or desires (see especially ‘Thought and Talk’). This does not mean that such animals have no mental life at all, nor does it mean that we cannot usefully use mental concepts in explaining and predicting the behaviour of such creatures. What it does mean, however, is that the extent to which we can think of such creatures as having attitudes and a mental life like our own is measured by the extent to which we can assign determinate propositional content to the attitudes we would ascribe to those creatures.

  • This would also seem to imply that a computer incapable of language and trading beliefs for other beliefs would not count as thinking.

  • What he's saying is that animals aren't trading beliefs for other beliefs, because believing would require them to have a language to express those beliefs in and a concept of truth and falsity.
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  • • This doesn't seem to me as radical as it superficially appears.

• “The basic problem [with mind/brain identity] is that not even the most complete account of brain function reaches the actual conscious experience with which it is associated” (The New Frontier of Religion and Science [NFRS], 85).

  • I think Dennett would disagree with this. His argument — someone's argument, anyway — is that an account of brain function is *not* complete without reference to consciousness — that consciousness is a materialistic phenomenon that is integral to cognition.
• If epiphenomenalism is true, then consciousness serves no biological role, and “its emergence would be inexplicable” (NFRS, 103).

  • Not only would it not serve a role now, it would never have served a role. It would not even be vestigial, like our now-excessive desire for fats and sugars, but a liability from the start.
• argues that developments in artificial intelligence, which are often used to support materialism, actually provide an argument against materialism. For if it is possible to program computers to perform complex functions akin to human behavior without being conscious, then again “consciousness becomes functionless and inexplicable” (NFRS, 101).

  • Not sure I understand this point, but that may not be Hick's fault.
• Assuming that it is more likely that consciousness would emerge if it offered an evolutionary advantage of some kind, he judges epiphenomenalism to be nearly as implausible as mind/brain identity.

  • I agree, at least if mind/brain identity means reductionistic identity, but I wonder if Hick has considered Davidson's anomalous monism?

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• "Can machines think?"  This should begin with definitions of the meaning of the terms "machine"  and "think."
• If the meaning of the words "machine" and "think" are  to be found by examining how they are commonly used it is difficult to  escape the conclusion that the meaning and the answer to the question,  "Can machines think?" is to be sought in a statistical survey  such as a Gallup poll.
• We now ask the question, "What will happen when a machine takes  the part of A in this game?" Will the interrogator decide wrongly  as often when the game is played like this as he does when the game is  played between a man and a woman? These questions replace our original,  "Can machines think?"
• The game may perhaps be criticised on the ground that the odds are weighted  too heavily against the machine. If the man were to try and pretend to  be the machine he would clearly make a very poor showing. He would be given  away at once by slowness and inaccuracy in arithmetic. May not machines  carry out something which ought to be described as thinking but which is  very different from what a man does? This objection is a very strong one,  but at least we can say that if, nevertheless, a machine can be constructed  to play the imitation game satisfactorily, we need not be troubled by this  objection.

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