10 ms·

The Computational Theory of Mind (2015)

148 points by DanielleMolloy 7y ago

joe_the_user 7y ago
It seems like basic materialism to claim that the brain could be simulated by a large enough Turing machine. But that claim would not necessarily imply that a Turing machine is a useful model for brain. A Turing machine isn't necessarily even used as the working model of a computer though it conceivably could be. Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. It seems like the main implication someone would take away from that statement is that it's reasonably practical to describe mental processes with a computer but given that the question is formulated as a binary is/is-not. The mind "is" a Turing machine given ordinary physics which I believe say everything can be approximated by a large enough Turing machine. But this sense of "is" doesn't imply the Turing machine mind model is useful.
- p1esk 7y ago
  Your comment makes more sense if we replace “useful” with “practical”
- lidHanteyk 7y ago
  Nobody claims that Turing machines in particular are well-suited to the task. What we claim is something more open-ended: Turing-complete languages can simulate each other given a universal encoding (a program we typically call an "emulator") and the simulation is only polynomially slower and larger. To the details: We don't want just one tape on the Turing machine. It's fine but slow, like using unary Church numerals instead of binary Cantor numerals. We usually assume at least two tapes. Similarly, we often ignore tapes and use a random-access memory instead. Edit: I guess that this is how we're talking today? From the article: > It is common to summarize CCTM through the slogan “the mind is a Turing machine”. This slogan is also somewhat misleading, because no one regards Turing’s precise formalism as a plausible model of mental activity. The formalism seems too restrictive in several ways. The article then lists senses, finite memory, concurrency, and determinism as four ways in which the brain might differ from idealized Turing machines.
  - joe_the_user 7y ago
    Nobody claims that Turing machines in particular are well-suited to the task. What we claim is something more open-ended: Turing-complete languages can simulate each other given a universal encoding (a program we typically call an "emulator") and the simulation is only polynomially slower and larger. Well, that's the Church-Turing Thesis, which most people accept but isn't being discussed by me or the OP - the topic being "theories of the mind", I think.
  - it 7y ago
    Finite memory just means a limited subset of what a Turing machine can do. Concurrency doesn't make Turing computable processes capable of anything beyond what they can already do. At best it buys them speed, maybe some expressiveness for the programmer, and bugs. Nondeterminism is useful for some algorithms but it also doesn't make it possible for Turing computable processes to go beyond Turing computability. See for example https://arxiv.org/pdf/cs/0401019.pdf https://arxiv.org/pdf/cs/0401019.pdf. The most interesting item in that list would be the senses of organisms, as some of those might indeed involve processes that are not computable. Also, some aspects of the brain's activity may not be computable. See Roger Penrose's books for details, such as p. 377 of Shadows of the Mind (a section about possible noncomputability in some physical processes).
- proc0 7y ago
  I'm still reading through the paper/article, but a Turing machine would be the substrate in this case, not the model. I think the question is whether we can model the brain's way of computing in a Turing machine, and then it will follow we can also model the brain. Turing machines are a model of computation, and if the brain's computational model is Turing complete, it will follow that we can simulate a brain inside a Turing machine.
  - jblwps 7y ago
    > if the brain's computational model is Turing complete, it will follow that we can simulate a brain inside a Turing machine I don't believe this is correct. If the brain's model/the brain itself is Turing-complete, that doesn't mean a Turing machine can simulate; it means that the brain's model can simulate any Turing machine qua Turing machine. This does not imply the reverse, that any Turing machine can simulate it.
- mikorym 7y ago
  I think a Turing Machine is an infinite state automaton with the capability that you can remotely access state information. [1] Maybe someone can clarify whether this is correct. If you phrase it that way, then it seems more intuitive that the brain could be described as a Turing machine. The definition of a Turing machine is deceivingly simple and cause us to question what we perceive of as complex. The transition function (and the tape) actually make magic happen if you compare it to any "dumb" mechanical or electrical device.
  - ethn 7y ago
    This is correct w.r.t the formalization of Minsky. Further, there is a conflation among models and implementation from the parent commenter. Minsky propounds in his automaton theory that a machine may be implemented in any way and it is only its formal history of signals and responses to states which determine the machine. In this sense a biological machine made of organic bases can be considered the same to that of gears and another of semi-conducting transistors assuming their states are described by the same histories. More so, in the same automaton theory any environment which interacts with a machine in itself must be a machine by symmetry. The classification of each is arbitrary for consideration of the states you’re interested in. The author of the article remains correct as we must be able to model at least one faculty of the mind as Turing complete because we can also compute by the nature of computation itself. This remains useful as then we can project the guarantees of a Universal Turing machine onto that specific faculty of the mind which is Turing complete. This then allows us to explore those novel guarantees of the mind, those which are also of the Universal Turing Machine since by that faculty having the properties of UTM, it is to be considered a UTM, as opposed to having no guarantees.
  - mikorym 7y ago
    [1] https://en.wikipedia.org/wiki/Finite-state_machine https://en.wikipedia.org/wiki/Finite-state_machine
- kaffeemitsahne 7y ago
  It might seem like basic materialism, but a lot of philosophers suddenly loose their simple and clear-cut ontological stance as soon as the mind gets involved.
- TheOtherHobbes 7y ago
  Are you absolutely convinced that you can build a sentient mind out of clockwork parts? Because that's what your interpretation of basic materialism implies.
  - PixyMisa 7y ago
    In principle? No question. In practice? No chance.
    - pulisse 7y ago
      How would a clockwork model of the mind explain the phenomenon of consciousness (as opposed to computation)?
      - hackinthebochs 7y ago
        The idea is that consciousness is what a planning algorithm feels like from the inside. A planning algorithm requires representations of the entities relevant to planning, e.g. various objects in the world, relevant laws, internal states such as emotions, motivations, etc. It is these representations from an internal perspective that have something it is like to have them. But the fact that this planning algorithm is constructed out of mechanical components is irrelevant. Computation is just an abstract description of the behavior of some classes of systems. But it doesn't exhaust the set of possible descriptions for a system. Some subset of computational systems might also have a description in terms of consciousness.
        mrmonkeyman 7y ago
        Feels like? On.. "the inside"? How's that going to work? Don't you guys see the inherent problem in this? There is no "feeling" and there is no "inside". How such an illusion can come to be is totally unaccounted for and an ignored problem. You write like some "awareness" is watching these machinations, but in your model there is no awareness, nothing is watching this. There are just a bunch of bouncing billiard balls. Where is the "feeling"? Where, exactly, is the "illusion"?
- YeGoblynQueenne 7y ago
  >> Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. The article clarifies that "the mind is a Turing machine" is not what the Classic Computational Theory of Mind claims. See section 3 "The classical computational theory of mind": https://plato.stanford.edu/entries/computational-mind/#ClaComTheMin https://plato.stanford.edu/entries/computational-mind/#ClaCo... To summarise, the article suggests that, while: "It is common to describe CCTM as embodying “the computer metaphor”. This description is doubly misleading." (first because nobody claims that the mind is a computer and second because the comparison to Turing machines is not a metaphor). The article concludes that: "CCTM claims that mental activity is “Turing-style computation” [snipped for relevance]". In short, nobody really claims that the mind is a computer, a digital computer, a Turing machine, a metaphorical Turing machine, etc etc. The idea, if I'm not misrepresenting it myself, is that the mind can think like Turing machines can compute.
- ukj 7y ago
  > Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. It means that if we internalise this idea we can use all the language we have developed to say precise things about computers/software/hardware to also say precise things about our minds. After all “computer” was a human job description... All descriptions (theories) are, fundamentally symbolic/linguistic.
DanielleMolloy 7y ago
Please let me apologize for the lack of understanding, maybe someone can add to this: If biological intelligence works like a Turing machine, is it even possible to know what or how certain brain areas compute without simulating them bottom-up? Question is influenced by this idea originally from the 80s: https://en.m.wikipedia.org/wiki/Computational_irreducibility https://en.m.wikipedia.org/wiki/Computational_irreducibility
- mLuby 7y ago
  >biological intelligence works like a Turing machine We know it doesn't. There's no metaphorical linear tape in the brain; it's a network of neurons. But a Turing machine can simulate a network of neurons (see Machine Learning), just as a brain can model a Turing machine (see Programmer). There are currently things a brain can think about that a Turing machine cannot, but the question is whether that will continue to be true despite the steady advance of (computer) science.
  - michaelmrose 7y ago
    I don't think the idea that the brain can do anything a sufficiently fast computer can't deserves any credence. Those who argue this point always descend into hand wavy nonsense.
    - 0xBA5ED 7y ago
      If by computer, you mean a binary computer using transistors, there's no reason to think you can make it do anything a brain can do. Alternatively, if computer means any hypothetical hardware, it may very well end up looking like a brain, in which case you might just call it an artificial brain rather than a computer.
      - michaelmrose 7y ago
        Why couldn't a traditional computer fast enough simulate the brain you imagine?
    - 7y ago
yters 7y ago
How can such a theory be falsified?
- taneq 7y ago
  Simple: Find something that the brain does that could not, in principle, be emulated by a Turing machine or equivalent. So far we don't know of any such thing (since quantum mechanics is computable and everything including the brain is ultimately quantum mechanics).
  - tcgv 7y ago
    True random number generation can't be done by a deterministic computer, and it appears that human brains can, although not conclusive yet, and the precise mechanism is unclear: - https://www.ncbi.nlm.nih.gov/pubmed/15922090 https://www.ncbi.nlm.nih.gov/pubmed/15922090
    - yters 7y ago
      Turing machines don't have to be deterministic. You can have non deterministic Turing machines, and probabilistic Turing machines.
      - deleted 7y ago
        [deleted]
      - marcosdumay 7y ago
        Turing machines, without qualifiers, usually means deterministic ones. Extending it for probabilistic is quite ok, but non deterministic ones are a completely different beast.
    - criddell 7y ago
      Why are you limiting it to deterministic computers? There are electronic circuits that can provide genuine random numbers.
Yajirobe 7y ago
CTRL+F "chinese room" CTRL+W
- tsukurimashou 7y ago
  >CTRL+F >"chinese room" >read this comment >CTRL+W
omarhaneef 7y ago
In a different lifetime I got to take a few classes with Jerry Fodor, and although this publication references him extensively, one of his most succinct arguments for the computational theory of mind is only alluded to: the lack of alternatives. Fodor was a snappy writer and talker. I urge you to view his videos if they can be found on YouTube. Unfortunately he passed away recently. The argument goes something like this: 1. The computational theory of mind is the only remotely plausible theory of mind we have 2. A remotely plausible theory is better than none at all
- mjburgess 7y ago
  The problem with that argument is that it "the computational theory of mind" isn't a "theory of mind" -- so I don't think we have any. A TOM needs to explain "mental life", at best the CTOM provides a model of a very narrow sort of cognition (inference over propositions). There's a gigantic (and in my view, deeply implausible) leap from "hey this kinda works for modelling inference in animals" to "hey this is how The Mind! works". Not only do all CTOM models fail for actual inference in animals where we can be reasonably sure inference is taking place (due to the frame problem), it clearly fails for non-inferential processes and states (eg., emotions/environmental-action/...). These non-inferential processes are regarded by CTOMists as "black boxes" that just "plug into" the "Real Mind" (ie., inference over propositions). I dont think Fodor's argument holds here: it is tantamount to saying, "hey we've explained light with waves, why dont we just explain everything with waves!" -- the cost to that approach should be obvious. The problem this field has is that its being lead by computer scientists not neurobiologists. You ask a computer scientist what the right model of anything is, and they'd reply with a logic. We do not, however, model causal reality with logic. Temperature isnt computed from a 'logic of molecule motion', it is done via a causal model which relates causal variables to one another.
  - omarhaneef 7y ago
    You’re right in the sense that the CCTM (classical computational theory of mind) isn’t a complete theory. There are lot of problems. Studying it is essentially memorizing all the problems and the problems with various proposed solutions. As a theory, not only is it incomplete, it is only remotely plausible. He concedes that up front! It’s like the old joke about capitalism - worst but for any other. I would expect a response to give a better alternative, not to lay out admittedly deep problems with it.
YeGoblynQueenne 7y ago
>> The first argument emphasizes learning (Bechtel and Abrahamsen 2002: 51). A vast range of cognitive phenomena involve learning from experience. Many connectionist models are explicitly designed to model learning, through backpropagation or some other algorithm that modifies the weights between nodes. By contrast, connectionists often complain that there are no good classical models of learning [4.2 Arguments for connectionism]. There is no special need for a specific model of "learning" in a classical setting. Given an inference procedure, such as induction, adbuction or deduction, that can derive new facts and rules in a logical language from observations and a pre-existing theory (i.e. a pre-existing set of facts and rules), all it takes to "learn" is to store the newly derived facts and rules to a database. I mean "learning" in the sense of Mitchell's definition of _machine_ learning, as (informally) the ability of a system to improve its performance from experience. In this sense, a system that starts with a database of logical facts and rules and adds new facts and rules derived from new observations is "learning". You can find many examples of learning in a classical, logic setting in the early ('70s and '80s) machine learning literature, particularly with propositional logic learners such as decision list and decision tree learners, the most famous of which are J. Ross Quinlan's ID3 and C4.5 decision tree learners. The field of Inductive Logic Programming studies learning in First-Order Logic languages, especially logic programming languages such as Prolog and Answer Set Programming, and includes early systems such as Shapiro's Model Inference System, Quinlan's FOIL (First-Order Inductive Learner, essentialy a relational version of ID3), Muggleton's Progol and Srinivasan's Aleph (based on inverse entailment), and more recently ASP learners such as ASPAL (Mark Law), or Statistical Relational Learning techniques, e.g. by De Raedt, Kerstig, Getoor, Taschar and others; etc etc. Bottom line- there is a huge body of work on learning in a classical, logic setting. There is no serious objection that "there _are_ good classical models of learning". Such models are all over the place in machine learning. In fact, they tend to be the most carefully characterised models of machine learning.
YeGoblynQueenne 7y ago
>> However, the mechanisms that connectionists usually propose for implementing memory are not plausible. Existing proposals are mainly variants upon a single idea: a recurrent neural network that allows reverberating activity to travel around a loop (Elman 1990). There are many reasons why the reverberatory loop model is hopeless as a theory of long-term memory. For example, noise in the nervous system ensures that signals would rapidly degrade in a few minutes. Implementationist connectionists have thus far offered no plausible model of read/write memory. [4.3 Systematicity and Productivity] I wonder: is this information outdated? A Neural Turing Machine (first described in 2014 by Alex Graves) is a recurrent neural network architecture with an external memory store. Reads and writes from and to the memory are controlled by an attention mechanism. A newer version is the Differential Neural Computer (first described in 2016, also by Graves). The setup is not fundamentally different to the Elman networks or Long-Short Term Memory networks other than the mechanism by which "memory" is manipulated and storage, retrieval or discarding of "memories" is decided, although the mechanisms are very similar too (for instance, in LSTMs, you could say that training a network to decide when to "recall" a weight value is essentially similar to the "attention" mechanism). Is there a significant difference between an LSTM-based neural architecture with a "reverberatory" memory and one with an external storage, both controlled by similar mechanisms? I would say- yes.
logicallee 7y ago
For anyone here who thinks consciousness and all thinking aren't an emergent property of "computation" in the mind, what do you think would fail to happen if you simulated it in silicon, trying to emulate it exactly?

It seems like basic materialism to claim that the brain could be simulated by a large enough Turing machine. But that claim would not necessarily imply that a Turing machine is a useful model for brain. A Turing machine isn't necessarily even used as the working model of a computer though it conceivably could be. Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. It seems like the main implication someone would take away from that statement is that it's reasonably practical to describe mental processes with a computer but given that the question is formulated as a binary is/is-not. The mind "is" a Turing machine given ordinary physics which I believe say everything can be approximated by a large enough Turing machine. But this sense of "is" doesn't imply the Turing machine mind model is useful.

p1esk 7y ago
Your comment makes more sense if we replace “useful” with “practical”
lidHanteyk 7y ago
Nobody claims that Turing machines in particular are well-suited to the task. What we claim is something more open-ended: Turing-complete languages can simulate each other given a universal encoding (a program we typically call an "emulator") and the simulation is only polynomially slower and larger. To the details: We don't want just one tape on the Turing machine. It's fine but slow, like using unary Church numerals instead of binary Cantor numerals. We usually assume at least two tapes. Similarly, we often ignore tapes and use a random-access memory instead. Edit: I guess that this is how we're talking today? From the article: > It is common to summarize CCTM through the slogan “the mind is a Turing machine”. This slogan is also somewhat misleading, because no one regards Turing’s precise formalism as a plausible model of mental activity. The formalism seems too restrictive in several ways. The article then lists senses, finite memory, concurrency, and determinism as four ways in which the brain might differ from idealized Turing machines.
- joe_the_user 7y ago
  Nobody claims that Turing machines in particular are well-suited to the task. What we claim is something more open-ended: Turing-complete languages can simulate each other given a universal encoding (a program we typically call an "emulator") and the simulation is only polynomially slower and larger. Well, that's the Church-Turing Thesis, which most people accept but isn't being discussed by me or the OP - the topic being "theories of the mind", I think.
- it 7y ago
  Finite memory just means a limited subset of what a Turing machine can do. Concurrency doesn't make Turing computable processes capable of anything beyond what they can already do. At best it buys them speed, maybe some expressiveness for the programmer, and bugs. Nondeterminism is useful for some algorithms but it also doesn't make it possible for Turing computable processes to go beyond Turing computability. See for example https://arxiv.org/pdf/cs/0401019.pdf https://arxiv.org/pdf/cs/0401019.pdf. The most interesting item in that list would be the senses of organisms, as some of those might indeed involve processes that are not computable. Also, some aspects of the brain's activity may not be computable. See Roger Penrose's books for details, such as p. 377 of Shadows of the Mind (a section about possible noncomputability in some physical processes).
proc0 7y ago
I'm still reading through the paper/article, but a Turing machine would be the substrate in this case, not the model. I think the question is whether we can model the brain's way of computing in a Turing machine, and then it will follow we can also model the brain. Turing machines are a model of computation, and if the brain's computational model is Turing complete, it will follow that we can simulate a brain inside a Turing machine.
- jblwps 7y ago
  > if the brain's computational model is Turing complete, it will follow that we can simulate a brain inside a Turing machine I don't believe this is correct. If the brain's model/the brain itself is Turing-complete, that doesn't mean a Turing machine can simulate; it means that the brain's model can simulate any Turing machine qua Turing machine. This does not imply the reverse, that any Turing machine can simulate it.
mikorym 7y ago
I think a Turing Machine is an infinite state automaton with the capability that you can remotely access state information. [1] Maybe someone can clarify whether this is correct. If you phrase it that way, then it seems more intuitive that the brain could be described as a Turing machine. The definition of a Turing machine is deceivingly simple and cause us to question what we perceive of as complex. The transition function (and the tape) actually make magic happen if you compare it to any "dumb" mechanical or electrical device.
- ethn 7y ago
  This is correct w.r.t the formalization of Minsky. Further, there is a conflation among models and implementation from the parent commenter. Minsky propounds in his automaton theory that a machine may be implemented in any way and it is only its formal history of signals and responses to states which determine the machine. In this sense a biological machine made of organic bases can be considered the same to that of gears and another of semi-conducting transistors assuming their states are described by the same histories. More so, in the same automaton theory any environment which interacts with a machine in itself must be a machine by symmetry. The classification of each is arbitrary for consideration of the states you’re interested in. The author of the article remains correct as we must be able to model at least one faculty of the mind as Turing complete because we can also compute by the nature of computation itself. This remains useful as then we can project the guarantees of a Universal Turing machine onto that specific faculty of the mind which is Turing complete. This then allows us to explore those novel guarantees of the mind, those which are also of the Universal Turing Machine since by that faculty having the properties of UTM, it is to be considered a UTM, as opposed to having no guarantees.
- mikorym 7y ago
  [1] https://en.wikipedia.org/wiki/Finite-state_machine https://en.wikipedia.org/wiki/Finite-state_machine
kaffeemitsahne 7y ago
It might seem like basic materialism, but a lot of philosophers suddenly loose their simple and clear-cut ontological stance as soon as the mind gets involved.
TheOtherHobbes 7y ago
Are you absolutely convinced that you can build a sentient mind out of clockwork parts? Because that's what your interpretation of basic materialism implies.
- PixyMisa 7y ago
  In principle? No question. In practice? No chance.
  - pulisse 7y ago
    How would a clockwork model of the mind explain the phenomenon of consciousness (as opposed to computation)?
    - hackinthebochs 7y ago
      The idea is that consciousness is what a planning algorithm feels like from the inside. A planning algorithm requires representations of the entities relevant to planning, e.g. various objects in the world, relevant laws, internal states such as emotions, motivations, etc. It is these representations from an internal perspective that have something it is like to have them. But the fact that this planning algorithm is constructed out of mechanical components is irrelevant. Computation is just an abstract description of the behavior of some classes of systems. But it doesn't exhaust the set of possible descriptions for a system. Some subset of computational systems might also have a description in terms of consciousness.
      - mrmonkeyman 7y ago
        Feels like? On.. "the inside"? How's that going to work? Don't you guys see the inherent problem in this? There is no "feeling" and there is no "inside". How such an illusion can come to be is totally unaccounted for and an ignored problem. You write like some "awareness" is watching these machinations, but in your model there is no awareness, nothing is watching this. There are just a bunch of bouncing billiard balls. Where is the "feeling"? Where, exactly, is the "illusion"?
YeGoblynQueenne 7y ago
>> Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. The article clarifies that "the mind is a Turing machine" is not what the Classic Computational Theory of Mind claims. See section 3 "The classical computational theory of mind": https://plato.stanford.edu/entries/computational-mind/#ClaComTheMin https://plato.stanford.edu/entries/computational-mind/#ClaCo... To summarise, the article suggests that, while: "It is common to describe CCTM as embodying “the computer metaphor”. This description is doubly misleading." (first because nobody claims that the mind is a computer and second because the comparison to Turing machines is not a metaphor). The article concludes that: "CCTM claims that mental activity is “Turing-style computation” [snipped for relevance]". In short, nobody really claims that the mind is a computer, a digital computer, a Turing machine, a metaphorical Turing machine, etc etc. The idea, if I'm not misrepresenting it myself, is that the mind can think like Turing machines can compute.
ukj 7y ago
> Edit: My comment is basically asking what saying "the mind is a Turing machine" really means. It means that if we internalise this idea we can use all the language we have developed to say precise things about computers/software/hardware to also say precise things about our minds. After all “computer” was a human job description... All descriptions (theories) are, fundamentally symbolic/linguistic.

DanielleMolloy 7y ago

Please let me apologize for the lack of understanding, maybe someone can add to this: If biological intelligence works like a Turing machine, is it even possible to know what or how certain brain areas compute without simulating them bottom-up? Question is influenced by this idea originally from the 80s: https://en.m.wikipedia.org/wiki/Computational_irreducibility https://en.m.wikipedia.org/wiki/Computational_irreducibility

mLuby 7y ago
>biological intelligence works like a Turing machine We know it doesn't. There's no metaphorical linear tape in the brain; it's a network of neurons. But a Turing machine can simulate a network of neurons (see Machine Learning), just as a brain can model a Turing machine (see Programmer). There are currently things a brain can think about that a Turing machine cannot, but the question is whether that will continue to be true despite the steady advance of (computer) science.
- michaelmrose 7y ago
  I don't think the idea that the brain can do anything a sufficiently fast computer can't deserves any credence. Those who argue this point always descend into hand wavy nonsense.
  - 0xBA5ED 7y ago
    If by computer, you mean a binary computer using transistors, there's no reason to think you can make it do anything a brain can do. Alternatively, if computer means any hypothetical hardware, it may very well end up looking like a brain, in which case you might just call it an artificial brain rather than a computer.
    - michaelmrose 7y ago
      Why couldn't a traditional computer fast enough simulate the brain you imagine?
  - 7y ago

yters 7y ago

How can such a theory be falsified?

taneq 7y ago
Simple: Find something that the brain does that could not, in principle, be emulated by a Turing machine or equivalent. So far we don't know of any such thing (since quantum mechanics is computable and everything including the brain is ultimately quantum mechanics).
- tcgv 7y ago
  True random number generation can't be done by a deterministic computer, and it appears that human brains can, although not conclusive yet, and the precise mechanism is unclear: - https://www.ncbi.nlm.nih.gov/pubmed/15922090 https://www.ncbi.nlm.nih.gov/pubmed/15922090
  - yters 7y ago
    Turing machines don't have to be deterministic. You can have non deterministic Turing machines, and probabilistic Turing machines.
    - deleted 7y ago
      [deleted]
    - marcosdumay 7y ago
      Turing machines, without qualifiers, usually means deterministic ones. Extending it for probabilistic is quite ok, but non deterministic ones are a completely different beast.
  - criddell 7y ago
    Why are you limiting it to deterministic computers? There are electronic circuits that can provide genuine random numbers.

Yajirobe 7y ago

CTRL+F "chinese room" CTRL+W

tsukurimashou 7y ago
>CTRL+F >"chinese room" >read this comment >CTRL+W

omarhaneef 7y ago

In a different lifetime I got to take a few classes with Jerry Fodor, and although this publication references him extensively, one of his most succinct arguments for the computational theory of mind is only alluded to: the lack of alternatives. Fodor was a snappy writer and talker. I urge you to view his videos if they can be found on YouTube. Unfortunately he passed away recently. The argument goes something like this: 1. The computational theory of mind is the only remotely plausible theory of mind we have 2. A remotely plausible theory is better than none at all

mjburgess 7y ago
The problem with that argument is that it "the computational theory of mind" isn't a "theory of mind" -- so I don't think we have any. A TOM needs to explain "mental life", at best the CTOM provides a model of a very narrow sort of cognition (inference over propositions). There's a gigantic (and in my view, deeply implausible) leap from "hey this kinda works for modelling inference in animals" to "hey this is how The Mind! works". Not only do all CTOM models fail for actual inference in animals where we can be reasonably sure inference is taking place (due to the frame problem), it clearly fails for non-inferential processes and states (eg., emotions/environmental-action/...). These non-inferential processes are regarded by CTOMists as "black boxes" that just "plug into" the "Real Mind" (ie., inference over propositions). I dont think Fodor's argument holds here: it is tantamount to saying, "hey we've explained light with waves, why dont we just explain everything with waves!" -- the cost to that approach should be obvious. The problem this field has is that its being lead by computer scientists not neurobiologists. You ask a computer scientist what the right model of anything is, and they'd reply with a logic. We do not, however, model causal reality with logic. Temperature isnt computed from a 'logic of molecule motion', it is done via a causal model which relates causal variables to one another.
- omarhaneef 7y ago
  You’re right in the sense that the CCTM (classical computational theory of mind) isn’t a complete theory. There are lot of problems. Studying it is essentially memorizing all the problems and the problems with various proposed solutions. As a theory, not only is it incomplete, it is only remotely plausible. He concedes that up front! It’s like the old joke about capitalism - worst but for any other. I would expect a response to give a better alternative, not to lay out admittedly deep problems with it.

YeGoblynQueenne 7y ago

>> The first argument emphasizes learning (Bechtel and Abrahamsen 2002: 51). A vast range of cognitive phenomena involve learning from experience. Many connectionist models are explicitly designed to model learning, through backpropagation or some other algorithm that modifies the weights between nodes. By contrast, connectionists often complain that there are no good classical models of learning [4.2 Arguments for connectionism]. There is no special need for a specific model of "learning" in a classical setting. Given an inference procedure, such as induction, adbuction or deduction, that can derive new facts and rules in a logical language from observations and a pre-existing theory (i.e. a pre-existing set of facts and rules), all it takes to "learn" is to store the newly derived facts and rules to a database. I mean "learning" in the sense of Mitchell's definition of _machine_ learning, as (informally) the ability of a system to improve its performance from experience. In this sense, a system that starts with a database of logical facts and rules and adds new facts and rules derived from new observations is "learning". You can find many examples of learning in a classical, logic setting in the early ('70s and '80s) machine learning literature, particularly with propositional logic learners such as decision list and decision tree learners, the most famous of which are J. Ross Quinlan's ID3 and C4.5 decision tree learners. The field of Inductive Logic Programming studies learning in First-Order Logic languages, especially logic programming languages such as Prolog and Answer Set Programming, and includes early systems such as Shapiro's Model Inference System, Quinlan's FOIL (First-Order Inductive Learner, essentialy a relational version of ID3), Muggleton's Progol and Srinivasan's Aleph (based on inverse entailment), and more recently ASP learners such as ASPAL (Mark Law), or Statistical Relational Learning techniques, e.g. by De Raedt, Kerstig, Getoor, Taschar and others; etc etc. Bottom line- there is a huge body of work on learning in a classical, logic setting. There is no serious objection that "there _are_ good classical models of learning". Such models are all over the place in machine learning. In fact, they tend to be the most carefully characterised models of machine learning.

YeGoblynQueenne 7y ago

>> However, the mechanisms that connectionists usually propose for implementing memory are not plausible. Existing proposals are mainly variants upon a single idea: a recurrent neural network that allows reverberating activity to travel around a loop (Elman 1990). There are many reasons why the reverberatory loop model is hopeless as a theory of long-term memory. For example, noise in the nervous system ensures that signals would rapidly degrade in a few minutes. Implementationist connectionists have thus far offered no plausible model of read/write memory. [4.3 Systematicity and Productivity] I wonder: is this information outdated? A Neural Turing Machine (first described in 2014 by Alex Graves) is a recurrent neural network architecture with an external memory store. Reads and writes from and to the memory are controlled by an attention mechanism. A newer version is the Differential Neural Computer (first described in 2016, also by Graves). The setup is not fundamentally different to the Elman networks or Long-Short Term Memory networks other than the mechanism by which "memory" is manipulated and storage, retrieval or discarding of "memories" is decided, although the mechanisms are very similar too (for instance, in LSTMs, you could say that training a network to decide when to "recall" a weight value is essentially similar to the "attention" mechanism). Is there a significant difference between an LSTM-based neural architecture with a "reverberatory" memory and one with an external storage, both controlled by similar mechanisms? I would say- yes.

logicallee 7y ago

For anyone here who thinks consciousness and all thinking aren't an emergent property of "computation" in the mind, what do you think would fail to happen if you simulated it in silicon, trying to emulate it exactly?