Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!princeton!mind!harnad From: harnad@mind.UUCP Newsgroups: comp.ai,comp.cog-eng Subject: Re: The symbol grounding problem Message-ID: <828@mind.UUCP> Date: Wed, 10-Jun-87 17:28:23 EDT Article-I.D.: mind.828 Posted: Wed Jun 10 17:28:23 1987 Date-Received: Sat, 13-Jun-87 05:34:58 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <6453@diamond.BBN.COM> Organization: Cognitive Science, Princeton University Lines: 238 Keywords: icons, categories, symbols, grounding, modularity, cognition Xref: utgpu comp.ai:470 comp.cog-eng:109 Summary: Performance modeling fails because of the symbol grounding problem aweinste@Diamond.BBN.COM (Anders Weinstein) of BBN Laboratories, Inc., Cambridge, MA writes: > There's no [symbol] grounding problem, just the old > behavior-generating problem Before responding to the supporting arguments for this conclusion, let me restate the matter in what I consider to be the right way. There is: (1) the behavior-generating problem (what I have referred to as the problem of devising a candidate that will pass the Total Turing Test), (2) the symbol-grounding problem (the problem of how to make formal symbols intrinsically meaningful, independent of our interpretations), and (3) the conjecture (based on the existing empirical evidence and on logical and methodological considerations) that (2) is responsible for the failure of the top-down symbolic approach to solve (1). >>my [SH's] invertibility criterion is, if not necessarily unhappy, somewhat >>surprising in its implications, for it implies that (1) being analog may >>be a matter of degree (i.e., degree of invertibility) and that (2) even >>a classical digital system must be regarded as analog to a degree ... > > These consequences only *seem* surprising if we forget that you've > redefined "analog" in a non-standard manner... you're really saying: > "physical invertibility is a matter of degree" or "a classical digital > system still employs physically invertible representations" -- both > quite humdrum. You've bypassed the three points I brought up in replying to your challenge to my invertibility criterion for an analog transform the last time: (1) the quantization in standard A/D is noninvertible, (2) a representation can only be analog in what it preserves, not in what it fails to preserve, and, in cognition at any rate, (3) the physical shape of the signal may be what matters, not the "message" it "carries." Add to this the surprising logical consequence that a "dedicated" digital system (hardwired to its peripherals) would be "analog" in its invertible inputs and outputs according to my invertibility criterion, and you have a coherent distinction that conforms well to some features of the classical A/D distinction, but that may prove to diverge, as I acknowledged, sufficiently to make it an independent, "non-standard" distinction, unique to cognition and neurobiology. Would it be surprising if classical electrical engineering concepts did not turn out to be just right for mind-modeling? > I [AW] had thought, perhaps wrongly, that you were claiming that the > interpretations of systems conceived by symbolic AI system must somehow > inevitably fail to be "grounded", and that only a system which employed > "analog" processing in the way you suggest would have the causal basis > required for fixing an interpretation. That is indeed what I'm claiming (although you've completely omitted the role of the categorical representations, which are just as critical to my scheme, as described in the CP book). But do make sure you keep my "non-standard" definition of analog in mind, and recall that I'm talking about asymptotic, human-scale performance, not toy systems. Toy systems are trivially "groundable" (even by my definition of "analog") by hard-wiring them into a dedicated input/output system. But the problem of intrinsic meaningfulness does not arise for toy models, only for devices that can pass the Total Turing Test (TTT). [The argument here involves showing that to attribute intentionality to devices that exhibit sub-TTT performance is not justified in the first place.] The conjecture is accordingly that the modular solution (i.e., hardwiring an autonomous top-down symbolic module to conventional peripheral modules -- transducers and effectors) will simply not succeed in producing a candidate that will be able to pass the Total Turing Test, and that the fault lies with the autonomy (or modularity) of the symbolic module. But I am not simply proposing an unexplicated "analog" solution to the grounding problem either, for note that a dedicated modular system *would* be analog according to my invertibility criterion! The conjecture is that such a modular solution would not be able to meet the TTT performance criterion, and the grounds for the conjecture are partly inductive (extrapolating symbolic AI's performance failures), partly logical and methodological (the grounding problem), and partly theory and data-driven (psychophysical findings in human categorical perception). My proposal is not that some undifferentiated, non-standard "analog" processing must be going on. I am advocating a specific hybrid bottom-up, symbolic/nonsymbolic rival to the pure top-down symbolic approach (whether or not the latter is wedded to peripheral modules), as described in the volume under discussion ("Categorical Perception: The Groundwork of Cognition," CUP 1987). > advocates of the symbolic approach already understand that causal > commerce with the environment is necessary for intentionality: they > envision the use of complex perceptual systems to provide the > requisite "grounding". So it's not as though the symbolic approach > is indifferent to this issue. This is the pious hope of the "top-down" approach: That suitably "complex" perceptual systems will meet for a successful "hook-up" somewhere in the middle. But simply reiterating it does not mean it will be realized. The evidence to date suggests the opposite: That the top-down approach will just generate more special-purpose toys, not a general purpose, TTT-scale model of human performance capacity. Nor is there any theory at all of what the requisite perceptual "complexity" might be: The stereotype is still standard transducers that go from physical energy via A/D conversion straight into symbols. Nor does "causal commerce" say anything: It leaves open anything from the modular symbol-cruncher/transducer hookups of the kind that so far only seem capable of generating toy models, to hybrid, nonmodular, bottom-up models of the sort I would advocate. Perhaps it's in the specific nature of the bottom-up grounding that the nature of the requisite "complexity" and "causality" will be cashed in. > your remarks against "toy" systems and "hard-wiring" the > interpretations of the inputs are plain unfair -- the symbolic > approach doesn't belittle the importance or complexity of what > perceptual systems must be able to do. It is in total agreement > with you that a truly intentional system must be capable of complex > adaptive performance via the use of its sensory input -- it just > hypothesizes that symbolic processing is sufficient to achieve this. And I just hypothesize that it isn't. And I try to say why not (the grounding problem and modularity) and what to do about it (bottom-up, nonmodular grounding of symbolic representations in iconic and categorical representations). > there is just no reason that a modular, all-digital system of the > kind envisioned by the symbolic approach could not be entirely > "grounded" BY YOUR OWN THEORY OF "GROUNDEDNESS": it could employ > "physically inevertible" representations (only they would be digital > ones), from these it could induct reliable "feature filters" based on > training (only these would use digital rather than analog techniques), > etc. ... the symbolic approach appears to handle your so-called > "grounding problem" every bit as well as any other method. First of all, as I indicated earlier, a dedicated top-down symbol-crunching module hooked to peripherals would indeed be "grounded" in my sense -- if it had TTT-performance power. Nor is it *logically impossible* that such a system could exist. But it certainly does not look likely on the evidence. I think some of the reasons we were led (wrongly) to expect it were the following: (1) The original successes of symbolic AI in generating intelligent performance: The initial rule-based, knowledge-driven toys were great successes, compared to the alternatives (which, apart from some limited feats of Perceptrons, were nonexistent). But now, after a generation of toys that show no signs of converging on general principles and growing up to TTT-size, the inductive evidence is pointing in the other direction: More ad hoc toys is all we have grounds to expect. (2) Symbol strings seemed such hopeful candidates for capturing mental phenomena such as thoughts, knowledge, beliefs. Symbolic function seemed like such a natural, distinct, nonphysical level for capturing the mind. Easy come, easy go. (3) We were persuaded by the power of computation -- Turing equivalence and all that -- to suppose that computation (symbol-crunching) just might *be* cognition. If every (discrete) thing anyone or anything (including the mind) does is computationally simulable, then maybe the computational functions capture the mental functions? But the fact that something is computationally simulable does not entail that it is implemented computationally (any more than behavior that is *describable* as ruleful is necessarily following an explicit rule). And some functions (such as transduction and causality) cannot be implemented computationally at all. (4) We were similarly persuaded by the power of digital coding -- the fact that it can approximate analog coding as closely as we please (and physics permits) -- to suppose that digital representations were the only ones we needed to think about. But the fact that a digital approximation is always possible does not entail that it is always practical or optimal, nor that it is the one that is actually being *used* (by, say, the brain). Some form of functionalism is probably right, but it certainly need not be symbolic functionalism, or a functionalism that is indifferent to whether a mental function or representation is analog or digital: The type of implementation may matter, both to the practical empirical problem of successfully generating performance and to the untestable phenomenological problem of capturing qualitative subjective experience. And some functions (let me again add), such as transduction and (continuous) A/A, cannot be implemented purely symbolically at all. A good example to bear in mind is Shepard's mental rotation experiments. On the face of it, the data seemed to suggest that subjects were doing analog processing: In making same/different judgments of pairs of successively presented 2-dimensional projections of 3-dimensional, computer-generated, unfamiliar forms, subjects' reaction times for saying "same" when one stimulus was in a standard orientation and the other was rotated were proportional to the degree of rotation. The diehard symbolists pointed out (correctly) that the proportionality, instead of being due to the real-time analog rotation of a mental icon, could have been produced by, say, (1) serially searching through the coordinates of a digital grid on which the stimuli were represented, with more distant numbers taking more incremental steps to reach, or by (2) doing inferences on formal descriptions that became more complex (and hence time-consuming) as the orientation became more eccentric. The point, though, is that although digital/symbolic representations were indeed possible, so were analog ones, and here the latter would certainly seem to be more practical and parsimonious. And the fact of the matter -- namely, which kinds of representations were *actually* used -- is certainly not settled by pointing out that digital representations are always *possible.* Maybe a completely digital mind would have required a head the size of New York State and polynomial evolutionary time in order to come into existence -- who knows? Not to mention that it still couldn't do the "A" in the A/D... > [you] reply that you are merely conjecturing that analog processing > may be required to realize the full range of human, as opposed to "toy", > performance -- in short, you think the symbolic approach just won't > work. But this... has nothing to do with some mythical "symbol > grounding" problem, at least as I understand it. It's just > the same old "intelligent-behavior-generating" problem which everyone > in AI, regardless of paradigm, is looking to solve... All you're > saying is that you suspect that mainstream AI's symbol system > hypothesis is false, based on its lack of conspicuous > performance-generating successes. Obviously everyone must recognize > that this is a possibility -- the premise of symbolic AI is, after > all, only a hypothesis. I'm not just saying I think the symbolic hypothesis is false. I'm saying why I think it's false (ungroundedness) and I'm suggesting an alternative (a bottom-up hybrid). > But I find this a much less interesting claim than I originally > thought -- conjectures, after all, are cheap. It *would* be > interesting if you could show, as, say, the connectionist program > is trying to, how analog processing can work wonders that > symbol-manipulation can't. But this would require detailed research, > not speculation. Until then, it remains a mystery why your proposed > approach should be regarded as any more promising than any other. Be patient. My hypotheses (which are not just spontaneous conjectures, but are based on an evaluation of the available evidence, the theoretical alternatives, and the logical and methodological problems involved) will be tested. They even have a potential connectionist component (in the induction of the features subserving categorization), although connectionism comes in for criticism too. For now it would seem only salutary to attempt to set cognitive modeling in directions that differ from the unprofitable ones it has taken so far. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU