crowston@athena.mit.edu (Kevin Crowston) (12/18/89)
I also read Searle's and the Churchland's articles in Scientific American and I'm not sure I understand Searle's argument. Perhaps someone who does can try to explain once more. Searle seems to be saying that the Turing Test is meaningless as a test of understanding because the Chinese Room can pass it, even though the person in the Chinese Room doesn't understand Chinese. But it seems to me that this argument equates the mind and the brain and thus mislocates the thing doing the understanding. I agree that the man in the room doesn't understand Chinese; but I would argue similarly that a computer, as a collection of wires and silicon, or a brain, as a blob of protoplasm, don't understand anything either. I all three cases, the thing doing the understanding is the program not the hardware. Searle acknowledges this argument (it's counterargument c in his article), but answers it by imagining a situation in which the man in the room memorizes the rules, the inbox, etc. He argues that it can't be the rules that do the understanding, since all there is in the room is the man (who we agree doesn't understand Chinese). The part I don't understand is, what difference does it make how the rules are stored? I don't see why it makes a difference if the man memorizes the rules or reads them off a piece of paper. In the latter case, admittedly, you can point to the rule book; but that doesn't mean the rule book doesn't exist in the former case. It seems to me that Searle's second example is really the same example, in which case the argument (that it's the rules that do the understanding, not the man in the room) remains unanswered. I expect the Scientific American Articles will set off another wave of articles; I look forward to the debate. Kevin Crowston
jfw@bilbo.mc.duke.edu (John Whitehead) (12/19/89)
In article <1989Dec18.014229.18058@athena.mit.edu> crowston@athena.mit.edu (Kevin Crowston) writes: >I also read Searle's and the Churchland's articles in Scientific American >and I'm not sure I understand Searle's argument. Perhaps someone who >does can try to explain once more. For a good analysis of this -- and many other similar thought-challenging papers -- check out _The Mind's I_, edited by Douglas Hofstadter and Daniel Dennett. I haven't seen the Sci Am article, but I imagine it is similiar (if not identical) to the one in this book. - John Whitehead
sarge@metapsy.UUCP (Sarge Gerbode) (12/19/89)
In article <1989Dec18.014229.18058@athena.mit.edu> crowston@athena.mit.edu (Kevin Crowston) writes: >Searle seems to be saying that the Turing Test is meaningless as a test of >understanding because the Chinese Room can pass it, even though the >person in the Chinese Room doesn't understand Chinese. But it seems to >me that this argument equates the mind and the brain and thus mislocates >the thing doing the understanding. I agree that the man in the room doesn't >understand Chinese; but I would argue similarly that a computer, as a >collection of wires and silicon, or a brain, as a blob of protoplasm, >don't understand anything either. In all three cases, the thing doing the >understanding is the program not the hardware. On reflection, I don't think you can dispose of the issue that easily by differentiating between the program and the hardware. The program is a schema that describes the electronic state the hardware should be in when the code file is loaded. In a very real sense, then, the shape of the physical machine has been altered by loading the code file, just as much as if you had flipped switches within the machine (as we used to do with the old panel switches). So after the code is loaded, there is actually a different physical machine there, just as much as if one had gone out and bought a different machine. Just because it isn't "hard" (i.e., you can't kick it, and it's easy to change), doesn't mean it isn't a physical entity. -- Sarge Gerbode -- UUCP: pyramid!thirdi!metapsy!sarge Institute for Research in Metapsychology 431 Burgess Drive; Menlo Park, CA 94025
crowston@athena.mit.edu (Kevin Crowston) (12/19/89)
In article <968@metapsy.UUCP> sarge@metapsy.UUCP (Sarge Gerbode) writes: >On reflection, I don't think you can dispose of the issue that easily >by differentiating between the program and the hardware. The program >is a schema that describes the electronic state the hardware should >be in when the code file is loaded. In a very real sense, then, the >shape of the physical machine has been altered by loading the code >file, just as much as if you had flipped switches within the machine >(as we used to do with the old panel switches). So after the code is >loaded, there is actually a different physical machine there, just as >much as if one had gone out and bought a different machine. But even so, the program still exists in both cases, right? Actually, I think you've made a key point here. Loading the software essentially gives you a different machine. But I think this actually supports my argument. Imagine the effect on the "understanding" done by the Chinese room of replacing the person with someone else (assuming that someone can also follow the rules). Now imagine changing the rulebook. In the first case, the Chinese room will be unaffected; in the second, it might change. I would argue that this is further evidence that it's the program not the hardware that matters. Since it could be anyone in the Chinese Room, it shouldn't matter what that person happens to think. > >Just because it isn't "hard" (i.e., you can't kick it, and it's easy >to change), doesn't mean it isn't a physical entity. Actually, this was my point. Software exists, even though you can't point to it. Kevin Crowston
dave@cogsci.indiana.edu (David Chalmers) (12/19/89)
"Programs" do not think. Cognition is not "symbol-manipulation." The "hardware/software" distinction is unimportant for thinking about minds. However: Systems with an appropriate causal structure think. Programs are a way of formally specifying causal structures. Physical systems which implement a given program *have* that causal structure, physically. (Not formally, physically. Symbols were simply an intermediate device.) Physical systems which implement the appropriate program think. -- Dave Chalmers (dave@cogsci.indiana.edu) Concepts and Cognition, Indiana University. "It is not the least charm of a theory that it is refutable" -- Fred
mbb@cbnewsh.ATT.COM (martin.b.brilliant) (12/20/89)
From article <31821@iuvax.cs.indiana.edu>, by dave@cogsci.indiana.edu (David Chalmers)... Slightly edited to make the bones barer: 1. Systems with an appropriate causal structure think. 2. Programs are a way of formally specifying causal structures. 3. Physical systems implement programs. 4. Physical systems which implement the appropriate program think. I take it that (1) is an acceptable definition. Does anybody think it begs the question? The weakest link here may be (2), the supposition that programs can implement any causal structure whatever, even those that do what we call thinking. The software/hardware duality question is semantically resolved by (3). The conclusion is (4), which seems to assert "strong AI." M. B. Brilliant Marty AT&T-BL HO 3D-520 (201) 949-1858 Holmdel, NJ 07733 att!hounx!marty1 or marty1@hounx.ATT.COM After retirement on 12/30/89 use att!althea!marty or marty@althea.UUCP Disclaimer: Opinions stated herein are mine unless and until my employer explicitly claims them; then I lose all rights to them.
kp@uts.amdahl.com (Ken Presting) (12/20/89)
In article <968@metapsy.UUCP> sarge@metapsy.UUCP (Sarge Gerbode) writes: >On reflection, I don't think you can dispose of the issue that easily >by differentiating between the program and the hardware. The program >is a schema that describes the electronic state the hardware should >be in when the code file is loaded. In a very real sense, then, the >shape of the physical machine has been altered by loading the code >file, just as much as if you had flipped switches within the machine >(as we used to do with the old panel switches). So after the code is >loaded, there is actually a different physical machine there, just as >much as if one had gone out and bought a different machine. This is e very good point, and often overlooked. The physical instantiation of data immensely complicates the concept of "symbol system". When machines were built from gears and axles, it was trivial to distinguish symbols from mechanisms. Symbols are part of a language, are written or spoken, and (most importantly) have no mechanical functions. But communication and computing devices blur the distinction. In these machines, an instance of a symbol (a charge, a current pulse, a switch) has a mechanical role in the operation of the device. The first problem that arises is how to distinguish symbol manipulation systems from other machines. What makes a symbol "explicit"? The clearest case of explicit symbols is printed text in a human language, but we need to resolve hard cases. One hard case is microcode or firmware. The hardest case is probably neural nets. Conclusion: No definition of "symbol manipulation system" which uses the term "explicit" will be of much help (until "explicit" is defined).
lee@uhccux.uhcc.hawaii.edu (Greg Lee) (12/20/89)
From article <6724@cbnewsh.ATT.COM>, by mbb@cbnewsh.ATT.COM (martin.b.brilliant): >From article <31821@iuvax.cs.indiana.edu>, by dave@cogsci.indiana.edu >(David Chalmers)... > >Slightly edited to make the bones barer: > >1. Systems with an appropriate causal structure think. >2. Programs are a way of formally specifying causal structures. >3. Physical systems implement programs. >4. Physical systems which implement the appropriate program think. > >I take it that (1) is an acceptable definition. Does anybody think it >begs the question? ... This and similar discussions have seemed to revolve around an equivocation between theories about how a thing works and how the thing does work. 1-4 invite this equivocation in several ways, consequently they do not serve to clarify. `causal' and `structure' have to do with theory-making -- we attribute cause/effect and structure to something in our efforts to understand it. So 1. in effect says that we can now understand, or will come to understand, how people think by making a theory involving cause and structure. If the former, it's false; if the latter, it does beg the question. If `program' in 3. is read as `theory' and `physical system' read as thing about which the theory is made, which is the best I can make of it, 3. is a generalization of 1. -- we can make (good) theories about things. As applied to human thought, and interpreted as a prediction, it likewise begs the question. Greg, lee@uhccux.uhcc.hawaii.edu
kp@uts.amdahl.com (Ken Presting) (12/20/89)
In article <6724@cbnewsh.ATT.COM> mbb@cbnewsh.ATT.COM (martin.b.brilliant) writes: >From article <31821@iuvax.cs.indiana.edu>, by dave@cogsci.indiana.edu >(David Chalmers)... > > 1. Systems with an appropriate causal structure think. > 2. Programs are a way of formally specifying causal structures. > 3. Physical systems implement programs. > 4. Physical systems which implement the appropriate program think. > >I take it that (1) is an acceptable definition. Does anybody think it >begs the question? I don't think so. Presumably, humans think because of the way we're built, and the mechanical/chemical/electrical structure determines the causal structure of our brains. >The weakest link here may be (2), the supposition that programs can >implement any causal structure whatever, even those that do what we >call thinking. Agreed. The multi-body problem of astrophysics is a clear case of a causal system which cannot be precisely represented by an algorithm. But the argument could succeed with a weaker version of 2, IF we could figure out which causal structures are relevant to thought >The software/hardware duality question is semantically resolved by (3). This is problematic. Harnad's "symbol grounding problem" (and some of Searle's objections, I think) point out the difficulty of claiming that some object "thinks" strictly on the basis of its internal operation, or even on the basis of it's outputs. Harnad would want to know how the symbols found in the output are grounded, while Searle might claim that the machine *simulated* thinking, but did not itself *think*. I agree that the correct resolution of the software/hardware duality can only be resolved by the concept of implementation used in (3). I'm just repeating a familiar (but important) theme.
miken@wheaties.ai.mit.edu (Michael N. Nitabach) (12/20/89)
In article <5767@uhccux.uhcc.hawaii.edu>, lee@uhccux.uhcc.hawaii.edu (Greg Lee) says: >`causal' and >`structure' have to do with theory-making -- we attribute >cause/effect and structure to something in our efforts to >understand it. This view of the fundamental nature of causation derives from a particular metaphysical tradition, beginning with the British Empiricists, e.g. Locke and Hume. This is the view that causation is not an aspect of the world which our mentality can recognize, but rather a schema which our mind imposes on events with appropriate spatiotemporal relations. A conceptually opposite--Realist--stance would be that causation exists as an actual attribute of certain pairs of physical events. Greg's argument in that posting rests on a particular metaphysical assumption, and not on a simple matter of definition or brute fact. Mike Nitabach
ele@cbnewsm.ATT.COM (eugene.l.edmon) (12/20/89)
In article <31821@iuvax.cs.indiana.edu> dave@cogsci.indiana.edu (David Chalmers) writes: >Systems with an appropriate causal structure think. Could you elaborate on this a bit? -- gene edmon ele@cbnewsm.ATT.COM
sm5y+@andrew.cmu.edu (Samuel Antonio Minter) (12/20/89)
1988:11:19:05:13 SFT
Couldn't you use the Chinese room analogy to prove that Humans don't
truly understand either. In this case the matter/energy in the human body
take the role of the man in the room and all his stacks of cards, while
the basic laws of physics take the role of the instuction book. After all
just as the instruction book tells the man what to do thus simulating a room
which understands Chinese, the laws telling how various atoms, electrons,
energy fields, etc. interact with each other "instruct" the matter and energy
of the human body how to simulate intelligent behavior. Maybe even
understanding Chinese! Is there an error in this argument that I'm missing.
If there isn't then it is a more powerful counter argument than the
agument "of course the man dosn't understand, but the whole room does."
1988:11:19:05:19 SFT
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~mmcg@bruce.OZ (Mike Mc Gaughey) (12/20/89)
sm5y+@andrew.cmu.edu (Samuel Antonio Minter) [20 Dec 89 05:20:12 GMT]: > 1988:11:19:05:13 SFT > > Couldn't you use the Chinese room analogy to prove that Humans don't > truly understand either. In this case the matter/energy in the human body > take the role of the man in the room and all his stacks of cards, while > the basic laws of physics take the role of the instuction book. After all No - this only proves that the laws of physics don't think (just as the man the room didn't understand). The total system behavior (i.e of a brain) is that of an entity which _does_ understand the concepts represented by the symbols being manipulated. Mike. -- Mike McGaughey ACSNET: mmcg@bruce.cs.monash.oz "You have so many computers, why don't you use them in the search for love?" - Lech Walesa :-)
lee@uhccux.uhcc.hawaii.edu (Greg Lee) (12/21/89)
From article <5610@rice-chex.ai.mit.edu>, by miken@wheaties.ai.mit.edu (Michael N. Nitabach): >In article <5767@uhccux.uhcc.hawaii.edu>, lee@uhccux.uhcc.hawaii.edu >(Greg Lee) says: > >>`causal' and >>`structure' have to do with theory-making -- we attribute >>cause/effect and structure to something in our efforts to >>understand it. > >This view of the fundamental nature of causation derives from a particular >metaphysical tradition, beginning with the British Empiricists, e.g. Locke >and Hume. This is the view that causation is not an aspect of the world >which our mentality can recognize, but rather a schema which our mind imposes >on events with appropriate spatiotemporal relations. ... No, that's not quite the view I expressed. In saying that causation is something we attribute in theory-making, I do not need to go so far as to say "causation is not an aspect of the world". And I don't. It may be, in the case of very good theories, that it is reasonable to confound what the theory says about a thing with the thing itself, or to take the theory to be a discovery rather than an invention. But in the case of not-so-good theories, where there is some doubt as to whether what the theory says is a cause is indeed a cause, confusing the theory with what it describes ought to be avoided. In the present discussion, we are dealing with not-so-good theories. Surely there's no one who is going to try to defend the view that one should never distinguish between a theory and what that theory purports to describe. Greg, lee@uhccux.uhcc.hawaii.edu
dejongh@peirce.cis.ohio-state.edu (Matt Dejongh) (12/21/89)
In article <6724@cbnewsh.ATT.COM> mbb@cbnewsh.ATT.COM (martin.b.brilliant) writes: >From article <31821@iuvax.cs.indiana.edu>, by dave@cogsci.indiana.edu >(David Chalmers)... > >Slightly edited to make the bones barer: > > 1. Systems with an appropriate causal structure think. > >I take it that (1) is an acceptable definition. Does anybody think it >begs the question? I do. What is "an appropriate causal structure?" Give me a definition and an example. matt ---------------------------------------------------------------------------- Matt DeJongh | Laboratory for Artificial Intelligence Research | Department of Computer and Information Sciences dejongh@cis.ohio-state.edu | The Ohio State University, Columbus, Ohio 43210 -=- ---------------------------------------------------------------------------- Matt DeJongh | Laboratory for Artificial Intelligence Research | Department of Computer and Information Sciences dejongh@cis.ohio-state.edu | The Ohio State University, Columbus, Ohio 43210
ladkin@icsib (Peter Ladkin) (12/21/89)
In article <5610@rice-chex.ai.mit.edu>, miken@wheaties (Michael N. Nitabach) writes: >This view of the fundamental nature of causation derives from a particular >metaphysical tradition, beginning with the British Empiricists, e.g. Locke >and Hume. This is the view that causation is not an aspect of the world >which our mentality can recognize, but rather a schema which our mind imposes >on events with appropriate spatiotemporal relations. this is hardly locke's view, and barely that of hume. locke rather strongly held that primary qualities of matter caused secondary qualities. this causation was not a product of anything like a mental schema. and `events with appropriate spatiotemporal relations' were not the only inhabitants of the physical world. you might also count bishop berkeley in with the other two, and for him causation was `in the world'. of course, it got there by being in the intention of a god, for him. all this is well-known and well-researched material. so much for summarising the views of the british empiricists. peter ladkin
dave@cogsci.indiana.edu (David Chalmers) (12/21/89)
gene edmon writes: >...David Chalmers writes: >>Systems with an appropriate causal structure think. > >Could you elaborate on this a bit? Well, seeing as you ask. The basic idea is that "it's not the meat, it's the motion." At the bottom line, the physical substance of a cognitive system is probably irrelevant -- what seems fundamental is the pattern of causal interactions that is instantiated. Reproducing the appropriate causal pattern, according to this view, brings along with it everything that is essential to cognition, leaving behind only the inessential. (Incidentally, I'm by no means arguing against the importance of the biochemical or the neural -- just asserting that they only make a difference insofar as they make a *functional* difference, that is, play a role in the causal dynamics of the model. And such a functional difference, on this view, can be reproduced in another medium.) And yes, of course this is begging the question. I could present arguments for this point of view but no doubt it would lead to great complications. Just let's say that this view ("functionalism", though this word is a dangerous one to sling around with its many meanings) is widely accepted, and I can't see it being unaccepted soon. The main reason I posted was not to argue for this view, but to delineate the correct role of the computer and the program in the study of mind. The other slightly contentious premise is the one that states that computers can capture any causal structure whatsoever. This, I take it, is the true import of the Church-Turing Thesis -- in fact, when I look at a Turing Machine, I see nothing so much as a formalization of the notion of causal system. And this is why, in the philosophy of mind, "computationalism" is often taken to be synonymous with "functionalism". Personally, I am a functionalist first, but accept computationalism because of the plausibility of this premise. Some people will argue against this premise, saying that computers cannot model certain processes which are inherently "analog". I've never seen the slightest evidence for this, and I'm yet to see an example of such a process. (The multi-body problem, by the way, is not a good example -- lack of a closed-form solution does not imply the impossibility of a computational model.) Of course, we may need to model processes at a low, non-superficial level, but this is not a problem. The other option for those who argue against the computational metaphor is to say "yes, but computation doesn't capture causal structure *in the right way*". (For instance, the causation is "symbolic", or it has to be mediated by a central processor.) I've never found much force in these arguments. -- Dave Chalmers (dave@cogsci.indiana.edu) Concepts and Cognition, Indiana University. "It is not the least charm of a theory that it is refutable" -- Fred
kp@uts.amdahl.com (Ken Presting) (12/22/89)
In article <31945@iuvax.cs.indiana.edu> dave@cogsci.indiana.edu (David Chalmers) writes: >The other slightly contentious premise is the one that states that computers >can capture any causal structure whatsoever. This, I take it, is the true >import of the Church-Turing Thesis -- in fact, when I look at a Turing >Machine, I see nothing so much as a formalization of the notion of causal >system. . . . > . . . Some people will argue against this premise, saying that >computers cannot model certain processes which are inherently "analog". I've >never seen the slightest evidence for this, and I'm yet to see an example of >such a process. (The multi-body problem, by the way, is not a good example -- >lack of a closed-form solution does not imply the impossibility of a >computational model.) Of course, we may need to model processes at a low, >non-superficial level, but this is not a problem. What makes the multi-body problem a counter-example is not just the fact that the problem has no closed-form solution, but the chaotic nature of the mechanical system. In a chaotic system, an arbitrarily small change in initial conditions will over time produce an arbitrarily *large* difference in subsequent states. It is true that for any physical system, a numerical solution of the differential equations can generate a prediction of the future state with an error as small as desired. But if a numeric model of a physical system is to run in real time (as needed for the Turing test), or just proportional to real time, then there will be a fixed minimum error in the transitions from state to state. The error may be reduced by using faster processors or faster algorithms, but for a given combination of processor, algorithm, and lag behind real-time, there must be a limit on the number of terms evaluated, and a minimum error. So at the end of the first time slice, there will be a finite difference between the state of the real system and the calculated state of the model. The real system will proceed chaotically (as will the model), to amplify the discrepancy in initial state and each subsequent state until (sooner or later) the real system will be in a state which diverges from the state of the model by any amount (up to the size of the system). That was a rough sketch of a proof that not all causal systems can be modeled by programs. Let me add a plausibility argument, so that the claim will not seem counter-intuitive. What makes the analog causal system different from the algorithm is that each state of the analog system encodes an infinite amount of information. This holds even for electrons bound into the quantum orbitals of an atom. There is a finite number of electrons, and transitions between levels are discrete, but there are infininitely many energy levels (most of them very close together). Of course, a processor has finitely many states, and encodes a finite amount of information. In addition, an analog system need not time-slice its calculations. It can make infinitely many transitions in any interval. A Turing machine would need to have both infinitely many states and run arbitrarily fast to prescisely match an analog system using a numerical algorithm. Now, the lack of precision is insignificant for many analog systems in many applications, where the error is constant or grows slowly. But in a chaotic system, the error in the model can grow very rapidly. If the numerical model cannot be allowed to lag arbitrarily far behind real-time (thus trading time for memory) then the amplification of error will make the model useless. The "solutions in closed form" of rational mechanics gurantee that that a numerical model for an analog system will have a rapidly computable algorithm (often a polynomial). So the fact that the multi-body problem has no solution in closed form is relevant, but that's not the whole story. More important is chaotic amplification of initial differences. This does *not* show that strong AI is impossible with algorithms. There is no way to know whether intelligence requires chaos. But the brain is certainly complicated enough to be as chaotic as fluid flow. Modeling human behavior may well have to deal with this problem. BTW, I'd like to know if anyone has heard this kind of argument before in connection with AI. It must be old hat in simulation circles.
dhw@itivax.iti.org (David H. West) (12/22/89)
In article <ebfl02Ef76Hs01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes: |What makes the multi-body problem a counter-example is not just the fact |that the problem has no closed-form solution, but the chaotic nature of |the mechanical system. | |In a chaotic system, an arbitrarily small change in initial conditions |will over time produce an arbitrarily *large* difference in subsequent |states. [...] |That was a rough sketch of a proof that not all causal systems can be |modeled by programs. Let me add a plausibility argument, so |that the claim will not seem counter-intuitive. | |What makes the analog causal system different from the algorithm is that |each state of the analog system encodes an infinite amount of information. Real intelligent systems (e.g. humans) function quite successfully at a finite temperature despite the influence of thermal fluctuations (Brownian motion), which cause finite random perturbations of everything. A finite system embedded in a thermal environment cannot encode an infinite amount of information. This would seem to indicate that your argument has no bearing on what may be necessary for intelligence, at least over a time-scale short enough that the physical embodiment of the intelligence is not disrupted by thermal fluctuations, whether or not these are chaotically amplified. -David West dhw@iti.org
dove@portia.Stanford.EDU (Dav Amann) (12/22/89)
Here we go. Recently, several posters have debated the concept of thinking machines without explicitly discussing thought. We all know that we think but I doubt that many individuals can articulate what it means to think. Does, for example, thinking imply conciousness? In other words, do things think without being aware that they are thinking? If thought is merely the process of solving problems, then answers become obvious. Solving problems does not imply conciousness. My Mac solves all sorts of problems for me, but it certainly does not spend its off hours contemplating the Eternal Void. (At least, I don't think that it does.) However, I believe that when individuals talk about thought they imply some sort of conciousness, or awareness. When we say a machine that thinks, we mean a machine that understands, reasons, draws conclusions, learns, and is somehow aware. Thus when Searle talks of the Chinese room, he is questioning the awareness of the machine rather than its imitation of reasoning processes. (At least, as far as I can tell.) I believe that the problem of conciousness comes from a choice of metaphysics. Most of us in the Western world are disciples of the ancient Greek metaphysician, Democritus. Democritus, you'll recall, was the philosopher who theorized that all of reality was made of atoms. Understand the atoms and you'll understand reality. Mathematics, physics, logic all went a long way towards cementing this mind set. The whole is equal to the sum of its parts. Understand the parts, and you'll understand the whole. When this viewpoint is applied to a theory of mind, you get a lot of folks saying, "All that's in there is a bunch of neurons firing in certain patterns. All that's happening is the exchange of neurochemicals. Understand those, and you'll understand the mind." Well, perhaps not. Somehow, intuitively speaking, I think that the mind is more than the firing of neurons, though it does seem to encompass the firing of neurons. There's more there, I say, than the exchange of certain neurochemicals. Plato knew more about the human mind than me and he knew much less about the construction of the brain. Perhaps this intuition explains the vigorous defense of Cartesian dualism since Descartes without very much empirical evidence. Lately, however, one of the newer sciences has been breaking away from Democritus. Biology discusses and understands more and more about the individual cell, yet they find it harder and harder to explain the relationship between cells within the pretext of the individual cell. Etymologysts understand a lot about termites but they cannot explain why five termites together will build arches the Romans would be proud of. The whole is more than the sum of its parts. Perhaps the mind is much the same way. Understanding the switches and chemicals inside of the brain may in some way add to the knowledge of our selves, but I don't think that it can ever fully explain our selves and our consciousness. So the question arises, How can we understand ourselves or our consciousness? How can we tell whether a machine thinks? To these questions I profess my ignorance, but I do not think that any method which only looks at the parts of the brain will accomplish that lofty goal. Dav Amann dove@portia.stanford.edu
dave@cogsci.indiana.edu (David Chalmers) (12/23/89)
Ken Presting writes: >What makes the multi-body problem a counter-example is not just the fact >that the problem has no closed-form solution, but the chaotic nature of >the mechanical system. Chaos is only a problem if we need to model the behaviour of a particular system over a particular period of time exactly -- i.e., not just capture how it might go, but how it *does* go. This isn't what we're trying to do in cognitive science, so it's not a problem. We can model the system to a finite level of precision, and be confident that what we're missing is only random "noise." So while we won't capture the exact behaviour of System X at 3 p.m. on 12/22/89, we'll generate equally plausible behaviour -- in other words, how the system *might* have gone, if a few unimportant random parameters had been different. This leads to a point which is a central tenet of functionalism -- you don't need to capture a system's causal dynamics exactly, but only at a certain level of abstraction. Which level of abstraction? Well, this is usually specified teleologically, depending on what you're trying to capture. Usually, it's a level of abstraction that captures plausible input/output relationships. Anything below this, we can consider either implementational detail, or noise. Just what this level of abstraction is, of course, is a matter of some debate. The most traditional functionalists, including the practitioners of "symbolic" AI, believe that you may go to a very high level of abstraction before missing anything important. The move these days seems to be towards a much less abstract modelling of causal dynamics, in the belief that what goes on at a low level (e.g. the neural level) makes a fundamental difference. (This view is sometimes associated with the name "eliminative materialism", but it's really just another variety of functionalism. Even at the neural level, what we're trying to capture are causal patterns, not substance.) >What makes the analog causal system different from the algorithm is that >each state of the analog system encodes an infinite amount of information. Arguable. My favourite "definition" of information is due to Bateson, I think (no endorsement of Bateson's other views implied): "Information is a difference that makes a difference." An infinite number of bits may be required to descibe the state of a system, but in any real-world system, all of these after a certain point will not make any difference at all, except as random parameter settings. (The beauty of Bateson's definition is that the final "difference" depends on our purposes. If we wanted a precise simulation of the universe, these bits would indeed be "information". If we want a cognitive model, they're not.) Incidentally, you can concoct hypothetical analog systems which contain an infinite amount of information, even in this sense -- by coding up Chaitin's Omega for instance (and thus being able to solve the Halting Problem, and be better than any algorithm). In the real world, quantum mechanics makes all of this irrelevant, destroying all information beyond N bits or so. Happy Solstice. -- Dave Chalmers (dave@cogsci.indiana.edu) Concepts and Cognition, Indiana University. "It is not the least charm of a theory that it is refutable"
mbb@cbnewsh.ATT.COM (martin.b.brilliant) (12/24/89)
In article <32029@iuvax.cs.indiana.edu> dave@cogsci.indiana.edu (David Chalmers) writes: >Chaos is only a problem if we need to model the behaviour of a particular >system over a particular period of time exactly -- i.e., not just capture >how it might go, but how it *does* go. This isn't what we're trying to do >in cognitive science, so it's not a problem. We can model the system to >a finite level of precision, and be confident that what we're missing is >only random "noise." ..... I second that. The goal of AI is not to model a particular mind, but to create a mind. One thing we know from experience about minds - which is reinforced by the argment based on chaos - is that two minds never think exactly alike. That would almost prove that if we modeled a given mind exactly, we would NOT have created a mind, because a REAL mind never duplicates another mind. To prove we have created a mind, we have to have one that does not exactly model another. The chaos argument proves that if we create a mind, we will automatically meet that requirement. How fortunate! M. B. Brilliant Marty AT&T-BL HO 3D-520 (201) 949-1858 Holmdel, NJ 07733 att!hounx!marty1 or marty1@hounx.ATT.COM After retirement on 12/30/89 use att!althea!marty or marty@althea.UUCP Disclaimer: Opinions stated herein are mine unless and until my employer explicitly claims them; then I lose all rights to them.
sarge@metapsy.UUCP (Sarge Gerbode) (12/26/89)
In article <1989Dec19.061822.27585@athena.mit.edu> crowston@athena.mit.edu (Kevin Crowston) writes: >>[After object] code is loaded, there is actually a different >>physical machine there, just as much as if one had gone out and >>bought a different machine. >But even so, the program still exists in both cases, right? Good question. What *is* a "program", anyway? The ascii source characters, taken as an aggregate? The machine-language code, as a sequence of octal or hex characters? The magnetic patterns on the disc? The electronic patterns in RAM when the programis loaded? Or is it, as I suspect, the detailed *concept* the programmer had in mind when he wrote the source code? Perhaps the program (or, if you will, the overall algorithm) is a *possibility* that can be actualized (implemented) in a variety of ways. This possibility exists in the mind of a conscious being as the concept called "the program". Without the concept, you would not have a "program" but a mere pattern of electronic whatevers. -- Sarge Gerbode -- UUCP: pyramid!thirdi!metapsy!sarge Institute for Research in Metapsychology 431 Burgess Drive; Menlo Park, CA 94025
sarge@metapsy.UUCP (Sarge Gerbode) (12/26/89)
In article <24Yy02PR76bt01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes: >In article <968@metapsy.UUCP>sarge@metapsy.UUCP (Sarge Gerbode) writes: >>On reflection, I don't think you can dispose of the issue that easily >>by differentiating between the program and the hardware. The program >>is a schema that describes the electronic state the hardware should >>be in when the code file is loaded. In a very real sense, then, the >>shape of the physical machine has been altered by loading the code >>file, just as much as if you had flipped switches within the machine >>(as we used to do with the old panel switches). So after the code is >>loaded, there is actually a different physical machine there, just as >>much as if one had gone out and bought a different machine. >This is a very good point, and often overlooked. The physical >instantiation of data immensely complicates the concept of "symbol >system". >When machines were built from gears and axles, it was trivial to >distinguish symbols from mechanisms. Symbols are part of a language, >are written or spoken, and (most importantly) have no mechanical >functions. But communication and computing devices blur the >distinction. In these machines, an instance of a symbol (a charge, a >current pulse, a switch) has a mechanical role in the operation of >the device. I may have a somewhat radical viewpoint on this, but to me a symbol is defined as such by the intention of the conscious being using it. A symbol is a perceivable or detectable entity that is used to direct attention to a particular reality or potential reality. Charges, current pulses, etc., are rightly regraded as symbols only to the extent that they are intended (ultimately) to be comprehended by some sort of conscious entity as indicating certain realities (or potential relaities). In the absence of such intentions, they are not symbols but mere charges, current pulses, etc. Of course, things can be decoded without being *intended* to be so decoded. Scientists are continually decoding (understanding) elements of the physical universe. But these elements are (rightly) not thought of as symbols because (unless one thinks of the universe as a communication from God) they are not intended to be decoded in a particular way. -- Sarge Gerbode -- UUCP: pyramid!thirdi!metapsy!sarge Institute for Research in Metapsychology 431 Burgess Drive; Menlo Park, CA 94025
bsimon@stsci.EDU (Bernie Simon) (12/28/89)
I would like to make a few point that seem clear to me, but apparently aren't clear to others in this discussion. 1) All physical objects are not machines. For example, stones, clouds, flowers, butterflies, and people are not machines. This should be obvious, but some people use the word machine to include all physical objects. Not only is this contrary to ordinary usage, it obscures an important distinction between what is an artifact and what is not. 2) Not all machines are computers. Lamps, screwdrivers, and cars are not computers. 3) There are some activities which can be performed by physical objects and machines which cannot be peformed by computers. Birds can fly and airplanes can fly, but computers cannot fly. Of course, a computer can control an airplane, but this misses the distinction I am trying to make. The distinction is that all computers, as computers, are equivalent to Turing machines. If the computers performs some other activity during its operation than executing a program (for example, flying) it is because the machine which contains the computer is capable of the activity (as airplanes are capable of flying). 4) The simulation of a physical activity by a computer cannot be identified with the physical activity. A computer running a flight simulation program is not flying. 5) Hence, while it may be possible to build a machine that thinks, it does not follow that it will be possible to build a computer that thinks, as not all physical activities can be performed by computers. 6) While there are good reasons to believe that thinking is a physical activity, there are no good reasons for believing that thinking is the execution of a computer program. Nothing revealed either through introspection or the examination of the anatomy of the brain leads to the conclusion that the brain is operating as a computer. If someone claims that it is, the burden of proof is on that person to justify that claim. Such proof must be base on analysis of the brain's structure and not on logical, mathematical, or philosophical grounds. Since even the physical basis of memory is poorly understood at present, any claim that the brain is a computer is at best an unproven hypothesis. Bernie Simon
andrew@dtg.nsc.com (Lord Snooty @ The Giant Poisoned Electric Head ) (12/28/89)
. 1) All physical objects are not machines. . 2) Not all machines are computers. What is a machine? It could be said that: A stone is a machine; slow crystallisation processes within. The internal elementary particle dynamics too. This is machinery. What is a computer? It could be said that: Lamps could be computers if composed of Finite State Automata on the molecular level. The program ensures that output intensity remains approximately constant, and that the structural form remains relatively invariant during illumination. This is computing. . 3) There are some activities which can be performed by physical objects . and machines which cannot be peformed by computers. . The distinction is that all computers, as computers, are . equivalent to Turing machines. What if the physical environment were used to compute with, instead of electrical energy? - think abacus. Flying then, for example, might be an emergent and NECESSARY property of such a computer. . 4) The simulation of a physical activity by a computer cannot be . identified with the physical activity. Irrelevant in light of rebuttal of 3) . 5) Hence, while it may be possible to build a machine that thinks, it . does not follow that it will be possible to build a computer that . thinks, as not all physical activities can be performed by computers. There is no known constraint on the physical activities which can be performed by computers, including those of the brain (I naturally exclude violations of the known physical laws). . 6) While there are good reasons to believe that thinking is a physical . activity, there are no good reasons for believing that thinking is the . execution of a computer program. Nothing revealed either through . introspection or the examination of the anatomy of the brain leads to . the conclusion that the brain is operating as a computer. If someone . claims that it is, the burden of proof is on that person to justify that . claim. Such proof must be base on analysis of the brain's structure and . not on logical, mathematical, or philosophical grounds. Since even the . physical basis of memory is poorly understood at present, any claim that . the brain is a computer is at best an unproven hypothesis. Repeat - what is a computer? -- ........................................................................... Andrew Palfreyman a wet bird never flies at night time sucks andrew@dtg.nsc.com there are always two sides to a broken window
mbb@cbnewsh.ATT.COM (martin.b.brilliant) (12/28/89)
In article <1037@ra.stsci.edu> bsimon@stsci.EDU (Bernie Simon) writes:
!I would like to make a few point that seem clear to me, but apparently
!aren't clear to others in this discussion.
Good thing to do.
!1) All physical objects are not machines. For example, stones, clouds,
!flowers, butterflies, and people are not machines...
Meaning that a machine has to be an artifact. OK, people sometimes
call people machines to emphasize that people and machines are governed
by the same physics. But saying that people are machines really begs
the question "Can machines think," doesn't it?
!2) Not all machines are computers. Lamps, screwdrivers, and cars are not
!computers.
OK. But all computers are machines. And a machine can contain a computer.
!3) There are some activities which can be performed by physical objects
!and machines which cannot be peformed by computers. Birds can fly and
!airplanes can fly, but computers cannot fly...
!4) The simulation of a physical activity by a computer cannot be
!identified with the physical activity. A computer running a flight
!simulation program is not flying.
True.
!5) Hence, while it may be possible to build a machine that thinks, it
!does not follow that it will be possible to build a computer that
!thinks, as not all physical activities can be performed by computers.
We seem to have got off the track. The question was not whether
computers can think, but whether machines can think. If you put a
computer into a machine that can accept sensory input and create
motor output, it might be able to do what we call thinking.
!6) While there are good reasons to believe that thinking is a physical
!activity, there are no good reasons for believing that thinking is the
!execution of a computer program....
I wouldn't believe that for a minute. I don't know exactly what
thinking is, but it is probably something a computer can't do alone,
but a machine with a computer in it might be able to do.
!.... Nothing revealed either through
!introspection or the examination of the anatomy of the brain leads to
!the conclusion that the brain is operating as a computer....
Is that a requirement for machines to think? Consider a machine with
sensory inputs and motor outputs. It needs a controller. Do you have
to have an actual brain inside, or will it be sufficient to have a
computer that simulates the brain?
Flying. We talked about flying. A computer can't fly. But if you
build a machine with eyes, and wings, and feet, and it needs a
controller, a machine that simulates a brain will be just as effective
as a genuine brain.
M. B. Brilliant Marty
AT&T-BL HO 3D-520 (201) 949-1858
Holmdel, NJ 07733 att!hounx!marty1 or marty1@hounx.ATT.COM
After retirement on 12/30/89 use att!althea!marty or marty@althea.UUCP
Disclaimer: Opinions stated herein are mine unless and until my employer
explicitly claims them; then I lose all rights to them.dg1v+@andrew.cmu.edu (David Greene) (12/28/89)
Excerpts from netnews.comp.ai: 27-Dec-89 Re: Can Machines Think? martin.b.brilliant@cbnew (2932) > !In article <1037@ra.stsci.edu> bsimon@stsci.EDU (Bernie Simon) writes: > !5) Hence, while it may be possible to build a machine that thinks, it > !does not follow that it will be possible to build a computer that > !thinks, as not all physical activities can be performed by computers. > We seem to have got off the track. The question was not whether > computers can think, but whether machines can think. If you put a > computer into a machine that can accept sensory input and create > motor output, it might be able to do what we call thinking. I would welcome some clarification... Let's assume there is some agreement on what constitutes "what we call thinking" -- a big assumption. Is it the case that machines alone can think? Or is it that a machine requires a computer (as a necessary but not sufficient condition) to think? (and that a computer alone is insufficient) If it is only the machine+computer combination that is capable, what is it about the combination? Is it the ability to control its sensory inputs and outputs (the machine part) or some other distinction? -David -------------------------------------------------------------------- David Perry Greene || ARPA: dg1v@andrew.cmu.edu, dpg@isl1.ri.cmu.edu Carnegie Mellon Univ. || BITNET: dg1v%andrew@vb.cc.cmu.edu Pittsburgh, PA 15213 || UUCP: !harvard!andrew.cmu.edu!dg1v --------------------------------------------------------------------
kp@uts.amdahl.com (Ken Presting) (12/29/89)
Here is the original argument under discussion: 1. Systems with an appropriate causal structure think. 2. Programs are a way of formally specifying causal structures. 3. Physical systems implement programs. 4. Physical systems which implement the appropriate program think. I have been arguing that this argument is unsound because (2) is false. By no means do I dispute the conclusion, though of course others would. David Chalmers writes: >Chaos is only a problem if we need to model the behaviour of a particular >system over a particular period of time exactly -- i.e., not just capture >how it might go, but how it *does* go. This isn't what we're trying to do >in cognitive science, so it's not a problem. We can model the system to >a finite level of precision, and be confident that what we're missing is >only random "noise." So while we won't capture the exact behaviour of System X >at 3 p.m. on 12/22/89, we'll generate equally plausible behaviour -- in other >words, how the system *might* have gone, if a few unimportant random >parameters had been different. M. B. Brilliant writes: >I second that. The goal of AI is not to model a particular mind, but >to create a mind. These objections seem to grant at least a part of my point - some of the characteristics of some causal systems cannot be specified by programs. I agree that an AI need not model any particular person at a particular time. But since the error in a numerical model is cumulative over time slices, it's not just the behavior of the system at a given time that won't match, but also the general shape of the trajectories though the state space of the system. If a numerical model of the brain is claimed to be accurate except for "noise", and therefore claimed to be conscious, then it must be shown that what is called "noise" is irrelevant to consciousness (or thinking). Fluctuations that seem to be "noise" may have significant consequences in a chaotic system. David Chalmers continues: >Incidentally, you can concoct hypothetical analog systems which contain >an infinite amount of information, even in this sense -- by coding up >Chaitin's Omega for instance (and thus being able to solve the Halting >Problem, and be better than any algorithm). In the real world, quantum >mechanics makes all of this irrelevant, destroying all information beyond >N bits or so. Quantum mechanics can't destroy any information - it just make the information statistical. Note that in the wave formulation of QM, the probability waves are continuous, and propagate and interfere deterministically. No probability information is ever lost, but retrieving the probabilistic information can be time consuming. The physical system need not "retrieve" the probabilistic information; it can react directly. John Nagle writes: > 2. Recent work has resulted in an effective way to solve N-body > problems to an arbitrary level of precision and with high > speed. See "The Rapid Evaluation of Potential Fields in > Particle Systems", by L.F. Greengard, MIT Press, 1988. > ISBN 0-262-07110-X. > > Systems with over a million bodies are now being solved using > these techniques. It's not enough to do fast and accurate calculations; the calculations must remain fast no matter how accurate the simulation has to be. Every computer must have a finite word size, so when accuracy levels require multiple words to represent values in a state vector, the model will slow down in proportion to the number of words used. This effect is independent of the efficiency of the basic algorithm.
dave@cogsci.indiana.edu (David Chalmers) (12/30/89)
In article <1037@ra.stsci.edu> bsimon@stsci.EDU (Bernie Simon) writes: >I would like to make a few point that seem clear to me, but apparently >aren't clear to others in this discussion. Hmmm, do I take it from the references line that you mean me? >1) All physical objects are not machines. >2) Not all machines are computers. >3) There are some activities which can be performed by physical objects >and machines which cannot be performed by computers. 1) Arguable but not relevant. 2) Of course. 3) Of course. >Birds can fly and airplanes can fly, but computers cannot fly. [...] >4) The simulation of a physical activity by a computer cannot be >identified with the physical activity. A computer running a flight >simulation program is not flying. >5) Hence, while it may be possible to build a machine that thinks, it >does not follow that it will be possible to build a computer that >thinks, as not all physical activities can be performed by computers. If you recall, the *premise* of the current discussion was that thinking is thinking in virtue of its abstract causal structure, and not in virtue of physical details of implementation. If you want to argue with this premise -- functionalism -- then fine. The point was not to defend it, but to defend a view of the relation between computation and cognition which is less simple-minded than "the mind is a computer". Of course, I also believe that functionalism is true. The functionalist believes that thinking is fundamentally different to flying (and heating, swimming, and nose-blowing). The essence of flying certainly *cannot* be captured in an abstract causal structure. This is because there are substantive *physical* criteria for flying. An object, *by definition*, is not flying unless it is (very roughly) engaged in ongoing motion without any connection to the ground. Nothing abstract about this -- it's a solid, physical criterion. If you capture the causal patterns without the correct physical realization, then it's not flying, period. Similarly for nose-blowing and the rest. Thinking, on the other hand, has no such solid criteria in its definition. The only definitive criterion for thought is "having such and such a subjective experience" -- which is far away from physical details (and a criterion which is understood notoriously badly). Of course, this doesn't *prove* that thinking is not nevertheless inseparable from physical details -- a correct theory of mind *might* just require that for these experiences, you can't get away with anything but pointy-headed neurons. But at the very least, physical details are out of the *definition*, and there is thus a principled difference between thinking and flying. Which makes the jump to functionalism much more plausible. Maybe "thinking" is more like "adding" than like "flying". Most arguments against functionalism are in terms of "funny instantiations" -- as in "but *this* has the right causal dynamics, and surely *this* doesn't think". Generally Chinese objects seem to be favoured for these arguments -- whether Rooms, Gyms or Nations. Some people find these intuitively compelling. As for me, I find the arguments sufficiently unconvincing that my "faith" is not only affirmed but strengthened. >6) While there are good reasons to believe that thinking is a physical >activity, there are no good reasons for believing that thinking is the >execution of a computer program. Nothing revealed either through >introspection or the examination of the anatomy of the brain leads to >the conclusion that the brain is operating as a computer. If someone >claims that it is, the burden of proof is on that person to justify that >claim. Such proof must be base on analysis of the brain's structure and >not on logical, mathematical, or philosophical grounds. Since even the >physical basis of memory is poorly understood at present, any claim that >the brain is a computer is at best an unproven hypothesis. I agree. Did you read my first note? The whole point is that you can accept the computational metaphor for mind *without* believing somewhat extreme statements like "the brain is a computer", "the mind is a program", "cognition is just symbol-manipulation" and so on. The role of computer programs is that they are very useful formal specifications of causal dynamics (which happen to use symbols as an intermediate device). Implementations of computer programs, on the other hand, possess *physically* the given causal dynamics. So if you accept (1) functionalism, and (2) that computer programs can capture any causal dynamics, then you accept that implementations of the right computer programs think. -- Dave Chalmers (dave@cogsci.indiana.edu) Concepts and Cognition, Indiana University. "It is not the least charm of a theory that it is refutable"
bwk@mbunix.mitre.org (Kort) (12/30/89)
In article <1037@ra.stsci.edu> bsimon@stsci.EDU (Bernie Simon) writes: > 6) While there are good reasons to believe that thinking is a physical > activity, there are no good reasons for believing that thinking is the > execution of a computer program. Nothing revealed either through > introspection or the examination of the anatomy of the brain leads to > the conclusion that the brain is operating as a computer. If someone > claims that it is, the burden of proof is on that person to justify that > claim. Such proof must be base on analysis of the brain's structure and > not on logical, mathematical, or philosophical grounds. Since even the > physical basis of memory is poorly understood at present, any claim that > the brain is a computer is at best an unproven hypothesis. The brain is a collection of about 400 anatomically identifiable neural networks, interconnected by trunk circuits called nerve bundles, and connected to the outside world by sensory organs (eyes, ears, nose, tactile sensors) and effectors (muscles, vocal cords). Neural networks are programmable computational devices, capable of categorizing stimuli into cases, and capable of instantiating any computable function (some more easily than others). Artificial neural networks are used today for classifying applicants for credit or insurance. They have also been used to read ASCII text and drive a speech synthesizer, thereby demonstrating one aspect of language processing. As to memory, you might want to explore recent research on the Hebb's synapse. --Barry Kort
bwk@mbunix.mitre.org (Kort) (12/30/89)
In article <f6xk02b078qO01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes: > I agree that an AI need not model any particular person at a particular > time. But since the error in a numerical model is cumulative over time > slices, it's not just the behavior of the system at a given time that > won't match, but also the general shape of the trajectories though the > state space of the system. If a numerical model of the brain is claimed > to be accurate except for "noise", and therefore claimed to be conscious, > then it must be shown that what is called "noise" is irrelevant to > consciousness (or thinking). Fluctuations that seem to be "noise" may > have significant consequences in a chaotic system. Noise is to thinking as genetic mutations are to evolution. Most noise is counterproductive, but occasionally the noise leads to a cognitive breakthrough. That's called serendipity. --Barry Kort
cam@aipna.ed.ac.uk (Chris Malcolm) (12/30/89)
In article <973@metapsy.UUCP> sarge@metapsy.UUCP (Sarge Gerbode) writes: >In article <1989Dec19.061822.27585@athena.mit.edu> crowston@athena.mit.edu >(Kevin Crowston) writes: >>>[After object] code is loaded, there is actually a different >>>physical machine there, just as much as if one had gone out and >>>bought a different machine. >>But even so, the program still exists in both cases, right? >Good question. What *is* a "program", anyway? > ... >is it, as I suspect, the detailed *concept* the programmer had in >mind when he wrote the source code? Computer programs, like knitting patterns, sometimes arise by serendipitous accident. "Gee, that looked good - wonder if I can do it again?" There are also those cases where one computer program invents another. In these cases there need never have been any mind which had a detailed concept, or even intention, behind the source code. Even in fully deliberate programs, it is sometimes the case that the programmer fixes a bug by accident without understanding it - sometimes the only way, for obscure bugs, just to tinker until it goes away. But I would not like to say that programs which have never been understood are therefore not programs, any more than I would like to say that if God does not understand how I'm built then I'm not really a person. -- Chris Malcolm cam@uk.ac.ed.aipna 031 667 1011 x2550 Department of Artificial Intelligence, Edinburgh University 5 Forrest Hill, Edinburgh, EH1 2QL, UK
gall@yunexus.UUCP (Norm Gall) (12/31/89)
bwk@mbunix.mitre.org (Kort) writes: | Noise is to thinking as genetic mutations are to evolution. Most | noise is counterproductive, but occasionally the noise leads to a | cognitive breakthrough. That's called serendipity. Don't you think you are playing fast and loose with these concepts? What you say noise is when you equate it with genetic mutation is not the same as what a radio operator knows it to be, what a philosopher knows it to be, and what the mother of a teenager in Windsor, ON knows it to be. I'm not saying that you haven't defined your concepts well enough (ai scientists have more that adequately defined it, for their purposes). My question is "What licenses you to shift the meaning of any particular term?" nrg -- York University | "Philosophers who make the general claim that a Department of Philosophy | rule simply 'reduces to' its formulations Toronto, Ontario, Canada | are using Occam's razor to cut the throat _________________________| of common sense.' - R. Harris
miron@fornax.UUCP (Miron Cuperman ) (12/31/89)
In article <f6xk02b078qO01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes: > David Chalmers writes: >>So while we won't capture the exact behaviour of System X >>at 3 p.m. on 12/22/89, we'll generate equally plausible behaviour -- in other >>words, how the system *might* have gone, if a few unimportant random >>parameters had been different. > >These objections seem to grant at least a part of my point - some of the >characteristics of some causal systems cannot be specified by programs. >I agree that an AI need not model any particular person at a particular >time. But since the error in a numerical model is cumulative over time >slices, it's not just the behavior of the system at a given time that >won't match, but also the general shape of the trajectories though the >state space of the system. My thoughts: Let us say you see a leaf falling. Since you are a chaotic system your trajectory through space-time may be completely different than it would be if you did not see that leaf. But did that leaf make you non human? Did it 'kill' you because it changed your future so drastically? I don't think so. Let us say that we model someone on a computer but we do not capture everything. Because of the imperfections of the model the resulting system will diverge. (Also because the inputs to this system and to the original are different.) Isn't that equivalent to the falling leaf incident? (assuming the model is close enough so it does not cause a breakdown in the basic things that make a human -- whatever those are.) I don't agree that some characteristics cannot be specified by programs. They can be specified up to any precision we would like. I don't think the human brain is so complex that it has an infinite number of *important* parameters (that without them you will fail the 'thinking test'). Actually I don't think there are so many $10M will not capture today (if we knew how to model them). You also wrote that chaotic systems are specificaly hard to model. A computer is a chaotic system. It is very easy to model a computer. Therefore it may be possible to model other chaotic systems. You have to justify your claim better. Summary: Inaccurate modeling may have an effect similar to 'normal' events. Since the inputs will be different anyway, the inaccuracy may not matter. Miron Cuperman miron@cs.sfu.ca
jpp@tygra.UUCP (John Palmer) (12/31/89)
}In article <85217@linus.UUCP> bwk@mbunix.mitre.org (Barry Kort) writes: }In article <1037@ra.stsci.edu} bsimon@stsci.EDU (Bernie Simon) writes: } } } 6) While there are good reasons to believe that thinking is a physical } } activity, there are no good reasons for believing that thinking is the } } execution of a computer program. Nothing revealed either through } } introspection or the examination of the anatomy of the brain leads to } } the conclusion that the brain is operating as a computer. If someone } } claims that it is, the burden of proof is on that person to justify that } } claim. Such proof must be base on analysis of the brain's structure and } } not on logical, mathematical, or philosophical grounds. Since even the } } physical basis of memory is poorly understood at present, any claim that } } the brain is a computer is at best an unproven hypothesis. } }The brain is a collection of about 400 anatomically identifiable }neural networks, interconnected by trunk circuits called nerve bundles, }and connected to the outside world by sensory organs (eyes, ears, nose, }tactile sensors) and effectors (muscles, vocal cords). Neural networks }are programmable computational devices, capable of categorizing stimuli }into cases, and capable of instantiating any computable function (some }more easily than others). Artificial neural networks are used today }for classifying applicants for credit or insurance. They have also }been used to read ASCII text and drive a speech synthesizer, thereby }demonstrating one aspect of language processing. As to memory, you }might want to explore recent research on the Hebb's synapse. } }--Barry Kort But the brain is not structurally programmable. The tradeoff principle states that no system can have structural programmability, evolutionary adaptability and efficiency at the same time. Digital computers are programmable, but lack efficiency (I may post more on this later) and evolutionary adaptability. The brain (ie: humans) has evolutionary adaptability and (relative) efficiency. Biological neurons are much more complex than their weak cousins (artificial neurons) and contain internal dynamics which play a very important role in their function. Things like second messenger systems and protein/substrate interactions are important. Internal dynamics rely heavily on the laws of physics and we cannot determine what "function" a neuron "computes" unless we do a physics experiment first. Computer engineers work very hard to mask off the effects of the laws of physics (ie: by eliminating the effects of background noise) in order to produce a device which is structurally programmable. Biological neurons, on the other hand, RELY on the laws of physics to do their work. The basic computing element of biological systems, the protein, operates by recognizing a substrate. This is accomplished by Brownian Motion and depends on weak bonds (VanderWaals interactions, etc). Thus, there is a structure/function relationship which is essential. Artificial neural nets will still be unable to solve hard problems (patttern recognition, REAL language processing, etc) because they are implemented in silicon (usually as a virtual machine on top of a standard digital computer) and are therefore inherently inefficient. In theory (Church-Turing Thesis) it is possible for such problems to be solved by digital computers, but most of the hard problems are intractable. We are very quickly reaching the limits of speed of silicon devices. The only hope of solving these hard problems is by developing devices which take advantage of the laws of physics and that have a very strong structure/function relationship. Of course, these devices will not be structurally programmable, but will have to be developed by an evolutionary process. My point: We are not going to solve the hard problems of AI by simply developing programs for our digital computers. We have to develope hardware that has a strong structure/function relationship. Sorry if this posting seems a little incoherent. Its 5am and I just woke up. I'll post more on this later. Most of these ideas are to be attributed to Dr. Michael Conrad, Wayne State University, Detroit, MI. -- = CAT-TALK Conferencing Network, Prototype Computer Conferencing System = - 1-800-446-4698, 300/1200/2400 baud, 8/N/1. New users use 'new' - = as a login id. E-Mail Address: ...!uunet!samsung!sharkey!tygra!jpp = - <<<Redistribution to GEnie PROHIBITED!!!>>>> -
sarge@metapsy.UUCP (Sarge Gerbode) (01/02/90)
In article <1779@aipna.ed.ac.uk> cam@aipna.ed.ac.uk (Chris Malcolm) writes: >In article <973@metapsy.UUCP>sarge@metapsy.UUCP (Sarge Gerbode) writes: >>What *is* a "program", anyway? >>... >>is it, as I suspect, the detailed *concept* the programmer had in >>mind when he wrote the source code? >Computer programs, like knitting patterns, sometimes arise by >serendipitous accident. "Gee, that looked good - wonder if I can do it >again?" >There are also those cases where one computer program invents >another. In these cases there need never have been any mind which had a >detailed concept, or even intention, behind the source code. You haven't really defined "program", yet. Do you mean the ascii code? I can see how that could arise from a random source, but doesn't it take a conscious being to look at the source code and label it as a "program"? I suppose it would be easy to design a program that would randomly generate syntactically correct ascii C code that would compile and run without run-time errors (probably has been done). But would you really call such a random product a program? And if so, what's so interesting about programs as such? >Even in fully deliberate programs, it is sometimes the case that the >programmer fixes a bug by accident without understanding it - >sometimes the only way, for obscure bugs, just to tinker until it >goes away. But I would not like to say that programs which have never >been understood are therefore not programs, any more than I would >like to say that if God does not understand how I'm built then I'm >not really a person. Good point. But even if a program were accidentally generated randomly (like the fabled monkeys accidentally producing a Shakespeare play), would it not require a conscious being to *label* such a production a "program", in order for it to be one? I'm not sure about this point. I suppose there might be an argument for saying that the enterprise of science is to discover the programs that exist in Nature, so that we can understand, predict, and control Nature. In particular, the DNA system could be (has been) described as a program. I'm not sure if this usage is legitimate, or if we are engaging in a bit of anthropomorphizing, here. Or "theomorphizing", if we find ourselves thinking as if the universe was somehow programmed by some sort of intelligent Being and we are discovering what that program is. -- Sarge Gerbode -- UUCP: pyramid!thirdi!metapsy!sarge Institute for Research in Metapsychology 431 Burgess Drive; Menlo Park, CA 94025
dhw@itivax.iti.org (David H. West) (01/03/90)
In article <191@fornax.UUCP> miron@cs.sfu.ca (Miron Cuperman) writes: >You also wrote that chaotic systems are specificaly hard to model. A >computer is a chaotic system. How so?
byoder@smcnet.UUCP (Brian Yoder) (01/03/90)
In article <979@metapsy.UUCP>, sarge@metapsy.UUCP (Sarge Gerbode) writes: > In article <24Yy02PR76bt01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com > (Ken Presting) writes: > >In article <968@metapsy.UUCP>sarge@metapsy.UUCP (Sarge Gerbode) writes: > > I may have a somewhat radical viewpoint on this, but to me a symbol > is defined as such by the intention of the conscious being using it. > A symbol is a perceivable or detectable entity that is used to direct > attention to a particular reality or potential reality. > > Charges, current pulses, etc., are rightly regraded as symbols only to > the extent that they are intended (ultimately) to be comprehended by > some sort of conscious entity as indicating certain realities (or > potential relaities). In the absence of such intentions, they are not > symbols but mere charges, current pulses, etc. Consider the real implementation of most programs though. THey are written in a high-level language like C, Pascal, FORTRAN, or COBOL. That's what the programmer knew about. The Compiler turns those symbols into symbols that no human (usually) ever looks at or understands. The end user sees neither of these, he sees the user interface and understands what the {{program is doing from yet another perspective. What is the intelligence that understands the machine language symbols? One more step higher in complexity is to consider systems with complex memories that load memory as they go (virtual memory kinds of systems) which have a different physical configuration each time they are executed. One more step takes us to self-modifying languages like LISP which can execute and build statements in their own language. No human ever sees these intermediate symbols, but those constructs are processed and are reflected in the behavior of the program. Finally, we have really dynamic systems like neural networks that aren't so much "loaded with a program" as "taught" what to do. They like us, don't have a static program controling the behavior outputs. In a sense our brains become "different machines" from minute to minute as we learn and act. (Some might say that large portions of the population remain changeless through video stimulation, but this effect has not yet been proven :-) Are all of these working with "symbols"? If not which are? Is it only humans that can identify a symbol? What if all of the records about punched cards were destroyed while card readers still existed, would the little holes in card decks still be symbols? After the readers were destroyed? Brian Yoder > -- > Sarge Gerbode -- UUCP: pyramid!thirdi!metapsy!sarge > Institute for Research in Metapsychology > 431 Burgess Drive; Menlo Park, CA 94025 -- -<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>- | Brian Yoder | answers *byoder(); | | uunet!ucla-cs!smcnet!byoder | He takes no arguments and returns the answers | -<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
byoder@smcnet.UUCP (Brian Yoder) (01/03/90)
In article <6902@cbnewsh.ATT.COM>, mbb@cbnewsh.ATT.COM (martin.b.brilliant) writes: > In article <1037@ra.stsci.edu> bsimon@stsci.EDU (Bernie Simon) writes: [Parts 1-4 deleted for brevity] > !5) Hence, while it may be possible to build a machine that thinks, it > !does not follow that it will be possible to build a computer that > !thinks, as not all physical activities can be performed by computers. I think that this is being a little bit too restrictive. It is pretty clear (at least to those of us who believe that humans can think ;-) that brains think. However without the "machine" through which it operates it couldn't do much about changing the world or discovering facts about it. To be fair, our potentially intelligent computer would have to have some kind of "body" with senses and output devices (hands, wheels, or at least a video display). > !6) While there are good reasons to believe that thinking is a physical > !activity, there are no good reasons for believing that thinking is the > !execution of a computer program.... > > I wouldn't believe that for a minute. I don't know exactly what > thinking is, but it is probably something a computer can't do alone, > but a machine with a computer in it might be able to do. What would be missing is something for the computer/machine to think about and a way for it to let us know that it thought something. There's not much to think about without any input. As for what thinking is, the definition ought to include interpretation of information, the deduction of new information, and decisions about courses of action. Isn't that something both brains and programs both do pretty well? > !.... Nothing revealed either through > !introspection or the examination of the anatomy of the brain leads to > !the conclusion that the brain is operating as a computer.... Maybe we should look at it the other way around, we could have a computer acting as a brain in this machine/computer. If it interpreted sensory data selected actions, and orchestrated their implementation (say, by flapping wings) isn't that accomplishing the same end as a brain would? Brian Yoder -- -<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>- | Brian Yoder | answers *byoder(); | | uunet!ucla-cs!smcnet!byoder | He takes no arguments and returns the answers | -<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
bwk@mbunix.mitre.org (Kort) (01/03/90)
In article <6126@yunexus.UUCP> gall@yunexus.UUCP writes: > In article <85218@linus.UUCP> bwk@mbunix.mitre.org (Barry Kort) writes: > > Noise is to thinking as genetic mutations are to evolution. Most > > noise is counterproductive, but occasionally the noise leads to a > > cognitive breakthrough. That's called serendipity. > Don't you think you are playing fast and loose with these concepts? > What you say noise is when you equate it with genetic mutation is not > the same as what a radio operator knows it to be, what a philosopher > knows it to be, and what the mother of a teenager in Windsor, ON > knows it to be. I'm using noise as a metaphor and analogizing it to random perturbations in an otherwise deterministic system. I find that analogies and metaphors are useful tools in creative thinking, helping to direct the mind toward deeper understanding of complex processes. > I'm not saying that you haven't defined your concepts well enough (AI > scientists have more that adequately defined it, for their purposes). > My question is "What licenses you to shift the meaning of any > particular term?" My birthright licenses me to use my brain and mind to seek knowledge and understanding, and to communicate interesting and intriguing ideas with like-minded philosophers. I hope you share the same birthright. I would hate to see you voluntarily repress your own opportunity to participate in the exploration of interesting ideas. --Barry Kort
flink@mimsy.umd.edu (Paul V Torek) (01/04/90)
kp@amdahl.uts.amdahl.com (Ken Presting) writes: >If a numerical model of the brain is claimed to be accurate except for >"noise", and therefore claimed to be conscious, then it must be shown >that what is called "noise" is irrelevant to consciousness (or thinking). Are you suggesting that (a) Some types of conscious thought might go wrong were it not for the "noise", or (b) Although a "noiseless" system might pass the Turing Test, "noise" might be necessary for consciousness to exist at all? (Or something else?) Most of the rest of your article suggests (a), but (b) strikes me as a more interesting thesis. I can't think of any argument against (b). -- "There ain't no sanity clause" --Marx Paul Torek flink@mimsy.umd.edu
gilham@csl.sri.com (Fred Gilham) (01/04/90)
Brian Yoder writes: | Consider the real implementation of most programs though. THey are written | in a high-level language like C, Pascal, FORTRAN, or COBOL. That's what the | programmer knew about. The Compiler turns those symbols into symbols | that no human (usually) ever looks at or understands. The end user sees | neither of these, he sees the user interface and understands what the | {{program is doing from yet another perspective. What is the intelligence | that understands the machine language symbols? I'm pretty sure that you are using the word `symbol' here in different ways. In the case of a programmer writing in some programming language, I would say that symbols (at various levels of abstraction) are being used. However, when the program is compiled, the symbols disappear. To say that the compiler turns the symbols into other symbols is, I believe, to speak metaphorically. The point is that symbols only exist when there is someone to give them a meaning. I envision the process in this way: meaning (in the mind) | computerized |====>symbol==>(some physical pattern)==>syntactic transformation | | meaning<==symbol<==(some physical pattern)<=======| (back in the mind) It seems to me that the computer starts and ends with the physical patterns. Everything else happens in our heads. The fact that the transformations themselves can be described symbolically tends to fool people into thinking that the computer is actually using and manipulating symbols, or even manipulating meaning. This has been described as a ``hermeneutical hall of mirrors'', where we project onto the computer our own thought processes. The computer manipulates the patterns in ways that are meaningful to us; therefore the computer must be doing something involving meaning. But it isn't, any more than the Eliza program actually understood the people that talked to it, even though THEY thought it did. -Fred Gilham gilham@csl.sri.com
kp@uts.amdahl.com (Ken Presting) (01/04/90)
In article <21606@mimsy.umd.edu> flink@mimsy.umd.edu (Paul V Torek) writes: >kp@amdahl.uts.amdahl.com (Ken Presting) writes: >>If a numerical model of the brain is claimed to be accurate except for >>"noise", and therefore claimed to be conscious, then it must be shown >>that what is called "noise" is irrelevant to consciousness (or thinking). > >Are you suggesting that >(a) Some types of conscious thought might go wrong were it not for the > "noise", or >(b) Although a "noiseless" system might pass the Turing Test, "noise" > might be necessary for consciousness to exist at all? >(Or something else?) > >Most of the rest of your article suggests (a), but (b) strikes me as a >more interesting thesis. I can't think of any argument against (b). I have in mind the "something else". The point about noise in chaotic systems arises as an objection to the argument that if all other attempts at AI fail, at least we can numerically model the phyics of the brain. For this argument to work, we need to be sure that we really *can* make an accurate model. Chaotic systems can mechanically amplify small discrepancies in initial state, such as noise. Numerical models trade speed for precision, so if a model is to have the arbitrary precision needed to eliminate all discrepancies, the model would run well behind real time, and fail the Turing test. I think (b) is reversed. Random brain events are probably important in human behavior, thus affecting the Turing test. But at least the sort of thinking that is used to evaluate decision functions or logical arguments seems to depend little on randomness. Creative thinking - inventing proofs, constructing metaphors - could very well profit from random influences.
flink@mimsy.umd.edu (Paul V Torek) (01/11/90)
I asked: pt>Are you suggesting that pt>(a) Some types of conscious thought might go wrong were it not for the pt> "noise", or pt>(b) Although a "noiseless" system might pass the Turing Test, "noise" pt> might be necessary for consciousness to exist at all? pt>(Or something else?) kp@amdahl.uts.amdahl.com (Ken Presting) writes: >I have in mind the "something else". > >The point about noise in chaotic systems arises as an objection to the >argument that if all other attempts at AI fail, at least we can >numerically model the phyics of the brain. For this argument to work, >we need to be sure that we really *can* make an accurate model. Chaotic >systems can mechanically amplify small discrepancies in initial state But that doesn't matter unless (a) is true. As many people pointed out in reply to you, the fact that an AI system doesn't duplicate the thought processes of any *particular* person, is no problem for strong AI. The fact that my thought processes are different from yours doesn't necessarily mean I'm wrong (or that I'm not really thinking) -- it just means I'm different. Now suppose that (a) *were* true -- the "noiseless" system goes wrong, because (say) it can't think creatively, because "noise" is necessary to do so. Now *that* would be a problem. >I think (b) is reversed. Random brain events are probably important in >human behavior, thus affecting the Turing test. But at least the sort >of thinking that is used to evaluate decision functions or logical >arguments seems to depend little on randomness. I agree that any particular person's behavior probably depends on random events in her brain, but I doubt that this would affect the Turing test -- a noiseless system would not respond "wrongly", just differently. That's my hunch. But let's let that pass. Your last sentence says that a noiseless system would probably pass those aspects of the Turing Test which involve such tasks as you mention. I agree, but what (b) was suggesting was that the Turing Test might not be an adequate test of whether a "thinker" is conscious. (And if you define "thought" such that it must be conscious, then non-conscious things can't think.) -- "There ain't no sanity clause" --Marx Paul Torek flink@mimsy.umd.edu
kp@uts.amdahl.com (Ken Presting) (01/11/90)
In article <21745@mimsy.umd.edu> flink@mimsy.umd.edu (Paul V Torek) writes: >I asked: >pt>Are you suggesting that >pt>(a) Some types of conscious thought might go wrong were it not for the >pt> "noise", or > >kp@amdahl.uts.amdahl.com (Ken Presting) writes: > ..... For this {"backstop"} argument to work, >>we need to be sure that we really *can* make an accurate model. Chaotic >>systems can mechanically amplify small discrepancies in initial state > >But that doesn't matter unless (a) is true. As many people pointed out >in reply to you, the fact that an AI system doesn't duplicate the thought >processes of any *particular* person, is no problem for strong AI. The >fact that my thought processes are different from yours doesn't >necessarily mean I'm wrong (or that I'm not really thinking) -- it just >means I'm different. The divergence of the model from the real system means that for any person, at any time, the model would diverge significantly from that person's states (assuming that the brain is significantly chaotic, for the purpose of discussion). So it's not just that the numerical model can't simulate me or you, it can't simulate *anybody*, *ever*. So if we want to claim that the simulation is close enough to brain function to be simulated thought, then we have to show that the chaotic aspects of brain function are inessential to thought. BTW, my thanks to you and to the others in comp.ai who are participating in the philosophical discussions here. You folks have helped me to clarify my ideas by your constructive and thoughtful comments. This group is a good example of the solid intellectual value of Usenet.
hankin@sauron.osf.org (Scott Hankin) (01/11/90)
kp@uts.amdahl.com (Ken Presting) writes: >The divergence of the model from the real system means that for any >person, at any time, the model would diverge significantly from that >person's states (assuming that the brain is significantly chaotic, for the >purpose of discussion). So it's not just that the numerical model can't >simulate me or you, it can't simulate *anybody*, *ever*. So if we want to >claim that the simulation is close enough to brain function to be >simulated thought, then we have to show that the chaotic aspects of brain >function are inessential to thought. However, if the brain is sufficiently chaotic, we would have to assume that a "perfect duplicate" (such as might come out of a matter duplicator) would immediately diverge from the original. I fail to see how that matters. Would the duplicate therefore not be thinking? Would he/she not be the same as the original? I suspect the answer to the first question is no, for the duplicate would still function in the same manner as the original, who, we can only assume, thinks. I also suspect that the answer to the second is no. The duplicate would cease being the same as the original at the point of duplication. They would be substantially the same, to be sure, but start to differ almost immediately. I don't feel that the issue is the simulation of any given personality, but rather whether a simulation could have thought processes as close to yours as yours are to mine. - Scott ------------------------------ Scott Hankin (hankin@osf.org) Open Software Foundation
bls@cs.purdue.EDU (Brian L. Stuart) (01/12/90)
In article <85YE02kP7duL01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes: >The divergence of the model from the real system means that for any >person, at any time, the model would diverge significantly from that >person's states (assuming that the brain is significantly chaotic, for the >purpose of discussion). So it's not just that the numerical model can't >simulate me or you, it can't simulate *anybody*, *ever*. So if we want to >claim that the simulation is close enough to brain function to be >simulated thought, then we have to show that the chaotic aspects of brain >function are inessential to thought. > This is not what's really at issue here. To simulate (or possess) intelligence is not to simulate one that possesses intelligence. We don't need to accurately simulate anyone. The questions that are significant here are: first, are the chaotic properties of the brain necessary for intelligence? If so, then what characteristics of the brain's attractor are necessary? If these characteristics are also sufficient, then there is no reason that any system possessing the same characteristics in its attractor will not also be intelligent. If the attractor characteristics are not sufficient, then we have the problem of finding out what else is necessary. In general, just because small changes in the input to chaotic systems can lead to qualatitivly different behavior does not mean that that behavior is unconstrained. It is still constrained by the system's attractor. Simulating an existing intelligence is a red herring; natural intelligent systems don't simulate others, so artificial ones likewise need not. >BTW, my thanks to you and to the others in comp.ai who are participating >in the philosophical discussions here. You folks have helped me to >clarify my ideas by your constructive and thoughtful comments. This group >is a good example of the solid intellectual value of Usenet. Ditto. Brian L. Stuart Department of Computer Science Purdue University
ercn67@castle.ed.ac.uk (M Holmes) (01/12/90)
Just a thought while hairs are being split on the difference between thinking computers, thinking machines, and thinking hybrids of computers and machines. It seems to have been suggested that computers would need to be able to manipulate the environment (basically have senses and have hands) in order to do what we call thinking. I'm not sure I'd agree but I think it's irrelevant anyway, for the following reasons. As a thought experiment (which is all thinking computers/machines are at the present time) suppose that we simulate a world within a computer system. Then we build an artificial intelligence embedded withing this simulation and allow "it" a simulated ability to sense and manipulate the simulated environment. This would seem to fulfill the criteria for a hybrid computer/machine which can sense and manipulate the "real" world. It would however simply be a program in a computer system. The point being that both sense and manipulation are simply a form of information processing which is what computers do anyway. It could be argued that this would just be "simulated thinking" but it isn't clear that this would be any different from the real thing. -- A Friend of Fernando Poo