Connectionists: Stephen Hanson in conversation with Geoff Hinton

[gary@ucsd.edu]

Please avert your gaze while I apply Ockham’s Razor…

On Sat, Feb 5, 2022 at 2:12 AM Danko Nikolic <danko.nikolic at gmail.com>
wrote:

> Gary, you wrote: "What are the alternatives?"
>
> There is at least one alternative: the theory of practopoiesis which
> suggests that it is not the neural networks that "compute" the mental
> operations.
> It is instead the quick adaptations of neurons who are responsible for
> thinking and perceiving. The network only serves the function of bringing
> in the information and sending it out.
>
> The adaptations are suggested to do the central part of the cognition.
>
> So far, this is all hypothetical. If we develop these ideas into a working
> system, this would be an entirely new paradigm. It would be like the third
> paradigm:
> 1) manipulation of symbols
> 2) neural net
> 3) fast adaptations
>
>
> Danko
>
> Dr. Danko Nikolić
> www.danko-nikolic.com
> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=8RJsdJD2SsrrPj_IZmgVEQXJT9dvaULicZp8q_rzKYc&e=>
> https://www.linkedin.com/in/danko-nikolic/
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.linkedin.com_in_danko-2Dnikolic_&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=MaN2MbUvhlQAp0jsn9LMCu21V0PDAtTwbqvdr3uycSE&e=>
> --- A progress usually starts with an insight ---
>
>
> On Fri, Feb 4, 2022 at 7:19 PM gary at ucsd.edu <gary at eng.ucsd.edu> wrote:
>
>> This is an argument from lack of imagination, as Pat Churchland used to
>> say. All you have to notice, is that your brain is a neural net work. What
>> are the alternatives?
>>
>> On Fri, Feb 4, 2022 at 4:08 AM Danko Nikolic <danko.nikolic at gmail.com>
>> wrote:
>>
>>>
>>> I suppose everyone agrees that "the brain is a physical system",
>>> and that "There is no “magic” inside the brain",
>>> and that '“understanding” is just part of “learning.”'
>>>
>>> Also, we can agree that some sort of simulation takes place behind
>>> understanding.
>>>
>>> However, there still is a problem: Neural network's can't implement the
>>> needed simulations; they cannot achieve the same cognitive effect that
>>> human minds can (or animal minds can).
>>>
>>> We don't know a way of wiring a neural network such that it could
>>> perform the simulations (understandings) necessary to find the answers to
>>> real-life questions, such as the hamster with a hat problem.
>>>
>>> In other words, neural networks, as we know them today, cannot:
>>>
>>> 1) learn from a small number of examples (simulation or not)
>>> 2) apply the knowledge to a wide range of situations
>>>
>>>
>>> We, as scientists, do not understand understanding. Our technology's
>>> simulations (their depth of understanding) are no match for the simulations
>>> (depth of understanding) that the biological brain performs.
>>>
>>> I think that scientific integrity also covers acknowledging when we did
>>> not (yet) succeed in solving a certain problem. There is still significant
>>> work to be done.
>>>
>>>
>>> Danko
>>>
>>> Dr. Danko Nikolić
>>> www.danko-nikolic.com
>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=8RJsdJD2SsrrPj_IZmgVEQXJT9dvaULicZp8q_rzKYc&e=>
>>> https://www.linkedin.com/in/danko-nikolic/
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>>> --- A progress usually starts with an insight ---
>>>
>>>
>>>
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>>> <#m_8423976727351221435_m_-3229424020171779455_m_-1469727422087267219_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>
>>>
>>> On Thu, Feb 3, 2022 at 9:35 PM Asim Roy <ASIM.ROY at asu.edu> wrote:
>>>
>>>> First of all, the brain is a physical system. There is no “magic”
>>>> inside the brain that does the “understanding” part. Take for example
>>>> learning to play tennis. You hit a few balls - some the right way and some
>>>> wrong – but you fairly quickly learn to hit them right most of the time. So
>>>> there is obviously some simulation going on in the brain about hitting the
>>>> ball in different ways and “learning” its consequences. What you are
>>>> calling “understanding” is really these simulations about different
>>>> scenarios. It’s also very similar to augmentation used to train image
>>>> recognition systems where you rotate images, obscure parts and so on, so
>>>> that you still can say it’s a cat even though you see only the cat’s face
>>>> or whiskers or a cat flipped on its back. So, if the following questions
>>>> relate to “understanding,” you can easily resolve this by simulating such
>>>> scenarios when “teaching” the system. There’s nothing “magical” about
>>>> “understanding.” As I said, bear in mind that the brain, after all, is a
>>>> physical system and “teaching” and “understanding” is embodied in that
>>>> physical system, not outside it. So “understanding” is just part of
>>>> “learning,” nothing more.
>>>>
>>>>
>>>>
>>>> DANKO:
>>>>
>>>> What would happen to the hat if the hamster rolls on its back? (Would
>>>> the hat fall off?)
>>>>
>>>> What would happen to the red hat when the hamster enters its lair?
>>>> (Would the hat fall off?)
>>>>
>>>> What would happen to that hamster when it goes foraging? (Would the red
>>>> hat have an influence on finding food?)
>>>>
>>>> What would happen in a situation of being chased by a predator? (Would
>>>> it be easier for predators to spot the hamster?)
>>>>
>>>>
>>>>
>>>> Asim Roy
>>>>
>>>> Professor, Information Systems
>>>>
>>>> Arizona State University
>>>>
>>>> Lifeboat Foundation Bios: Professor Asim Roy
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=>
>>>>
>>>> Asim Roy | iSearch (asu.edu)
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> *From:* Gary Marcus <gary.marcus at nyu.edu>
>>>> *Sent:* Thursday, February 3, 2022 9:26 AM
>>>> *To:* Danko Nikolic <danko.nikolic at gmail.com>
>>>> *Cc:* Asim Roy <ASIM.ROY at asu.edu>; Geoffrey Hinton <
>>>> geoffrey.hinton at gmail.com>; AIhub <aihuborg at gmail.com>;
>>>> connectionists at mailman.srv.cs.cmu.edu
>>>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with
>>>> Geoff Hinton
>>>>
>>>>
>>>>
>>>> Dear Danko,
>>>>
>>>>
>>>>
>>>> Well said. I had a somewhat similar response to Jeff Dean’s 2021 TED
>>>> talk, in which he said (paraphrasing from memory, because I don’t remember
>>>> the precise words) that the famous 200 Quoc Le unsupervised model [
>>>> https://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised_icml2012.pdf
>>>> <https://urldefense.com/v3/__https:/static.googleusercontent.com/media/research.google.com/en/*archive/unsupervised_icml2012.pdf__;Lw!!IKRxdwAv5BmarQ!PFl2URDWVshfy1BPSwAMXKYyn1wszxpN4EPzShAm3sX83AOt05MQX07oVyVLEqo$>]
>>>> had learned the concept of a ca. In reality the model had clustered
>>>> together some catlike images based on the image statistics that it had
>>>> extracted, but it was a long way from a full, counterfactual-supporting
>>>> concept of a cat, much as you describe below.
>>>>
>>>>
>>>>
>>>> I fully agree with you that the reason for even having a semantics is
>>>> as you put it, "to 1) learn with a few examples and 2) apply the knowledge
>>>> to a broad set of situations.” GPT-3 sometimes gives the appearance of
>>>> having done so, but it falls apart under close inspection, so the problem
>>>> remains unsolved.
>>>>
>>>>
>>>>
>>>> Gary
>>>>
>>>>
>>>>
>>>> On Feb 3, 2022, at 3:19 AM, Danko Nikolic <danko.nikolic at gmail.com>
>>>> wrote:
>>>>
>>>>
>>>>
>>>> G. Hinton wrote: "I believe that any reasonable person would admit that
>>>> if you ask a neural net to draw a picture of a hamster wearing a red hat
>>>> and it draws such a picture, it understood the request."
>>>>
>>>>
>>>>
>>>> I would like to suggest why drawing a hamster with a red hat does not
>>>> necessarily imply understanding of the statement "hamster wearing a red
>>>> hat".
>>>>
>>>> To understand that "hamster wearing a red hat" would mean inferring, in
>>>> newly emerging situations of this hamster, all the real-life
>>>> implications that the red hat brings to the little animal.
>>>>
>>>>
>>>>
>>>> What would happen to the hat if the hamster rolls on its back? (Would
>>>> the hat fall off?)
>>>>
>>>> What would happen to the red hat when the hamster enters its lair?
>>>> (Would the hat fall off?)
>>>>
>>>> What would happen to that hamster when it goes foraging? (Would the red
>>>> hat have an influence on finding food?)
>>>>
>>>> What would happen in a situation of being chased by a predator? (Would
>>>> it be easier for predators to spot the hamster?)
>>>>
>>>>
>>>>
>>>> ...and so on.
>>>>
>>>>
>>>>
>>>> Countless many questions can be asked. One has understood "hamster
>>>> wearing a red hat" only if one can answer reasonably well many of such
>>>> real-life relevant questions. Similarly, a student has understood materias
>>>> in a class only if they can apply the materials in real-life situations
>>>> (e.g., applying Pythagora's theorem). If a student gives a correct answer
>>>> to a multiple choice question, we don't know whether the student understood
>>>> the material or whether this was just rote learning (often, it is rote
>>>> learning).
>>>>
>>>>
>>>>
>>>> I also suggest that understanding also comes together with effective
>>>> learning: We store new information in such a way that we can recall it
>>>> later and use it effectively  i.e., make good inferences in newly emerging
>>>> situations based on this knowledge.
>>>>
>>>>
>>>>
>>>> In short: Understanding makes us humans able to 1) learn with a few
>>>> examples and 2) apply the knowledge to a broad set of situations.
>>>>
>>>>
>>>>
>>>> No neural network today has such capabilities and we don't know how to
>>>> give them such capabilities. Neural networks need large amounts of
>>>> training examples that cover a large variety of situations and then
>>>> the networks can only deal with what the training examples have already
>>>> covered. Neural networks cannot extrapolate in that 'understanding' sense.
>>>>
>>>>
>>>>
>>>> I suggest that understanding truly extrapolates from a piece of
>>>> knowledge. It is not about satisfying a task such as translation between
>>>> languages or drawing hamsters with hats. It is how you got the capability
>>>> to complete the task: Did you only have a few examples that covered
>>>> something different but related and then you extrapolated from that
>>>> knowledge? If yes, this is going in the direction of understanding. Have
>>>> you seen countless examples and then interpolated among them? Then perhaps
>>>> it is not understanding.
>>>>
>>>>
>>>>
>>>> So, for the case of drawing a hamster wearing a red hat, understanding
>>>> perhaps would have taken place if the following happened before that:
>>>>
>>>>
>>>>
>>>> 1) first, the network learned about hamsters (not many examples)
>>>>
>>>> 2) after that the network learned about red hats (outside the context
>>>> of hamsters and without many examples)
>>>>
>>>> 3) finally the network learned about drawing (outside of the context of
>>>> hats and hamsters, not many examples)
>>>>
>>>>
>>>>
>>>> After that, the network is asked to draw a hamster with a red hat. If
>>>> it does it successfully, maybe we have started cracking the problem of
>>>> understanding.
>>>>
>>>>
>>>>
>>>> Note also that this requires the network to learn sequentially without
>>>> exhibiting catastrophic forgetting of the previous knowledge, which is
>>>> possibly also a consequence of human learning by understanding.
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> Danko
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> Dr. Danko Nikolić
>>>> www.danko-nikolic.com
>>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.danko-2Dnikolic.com&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=HwOLDw6UCRzU5-FPSceKjtpNm7C6sZQU5kuGAMVbPaI&e=>
>>>> https://www.linkedin.com/in/danko-nikolic/
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.linkedin.com_in_danko-2Dnikolic_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=b70c8lokmxM3Kz66OfMIM4pROgAhTJOAlp205vOmCQ8&e=>
>>>>
>>>> --- A progress usually starts with an insight ---
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=Ao9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4&e=>
>>>>
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>>>>
>>>>
>>>>
>>>> On Thu, Feb 3, 2022 at 9:55 AM Asim Roy <ASIM.ROY at asu.edu> wrote:
>>>>
>>>> Without getting into the specific dispute between Gary and Geoff, I
>>>> think with approaches similar to GLOM, we are finally headed in the right
>>>> direction. There’s plenty of neurophysiological evidence for single-cell
>>>> abstractions and multisensory neurons in the brain, which one might claim
>>>> correspond to symbols. And I think we can finally reconcile the decades old
>>>> dispute between Symbolic AI and Connectionism.
>>>>
>>>>
>>>>
>>>> GARY: (Your GLOM, which as you know I praised publicly, is in many ways
>>>> an effort to wind up with encodings that effectively serve as symbols in
>>>> exactly that way, guaranteed to serve as consistent representations of
>>>> specific concepts.)
>>>>
>>>> GARY: I have *never* called for dismissal of neural networks, but
>>>> rather for some hybrid between the two (as you yourself contemplated in
>>>> 1991); the point of the 2001 book was to characterize exactly where
>>>> multilayer perceptrons succeeded and broke down, and where symbols could
>>>> complement them.
>>>>
>>>>
>>>>
>>>> Asim Roy
>>>>
>>>> Professor, Information Systems
>>>>
>>>> Arizona State University
>>>>
>>>> Lifeboat Foundation Bios: Professor Asim Roy
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__lifeboat.com_ex_bios.asim.roy&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=oDRJmXX22O8NcfqyLjyu4Ajmt8pcHWquTxYjeWahfuw&e=>
>>>>
>>>> Asim Roy | iSearch (asu.edu)
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__isearch.asu.edu_profile_9973&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=jCesWT7oGgX76_y7PFh4cCIQ-Ife-esGblJyrBiDlro&e=>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> *From:* Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> *On
>>>> Behalf Of *Gary Marcus
>>>> *Sent:* Wednesday, February 2, 2022 1:26 PM
>>>> *To:* Geoffrey Hinton <geoffrey.hinton at gmail.com>
>>>> *Cc:* AIhub <aihuborg at gmail.com>; connectionists at mailman.srv.cs.cmu.edu
>>>> *Subject:* Re: Connectionists: Stephen Hanson in conversation with
>>>> Geoff Hinton
>>>>
>>>>
>>>>
>>>> Dear Geoff, and interested others,
>>>>
>>>>
>>>>
>>>> What, for example, would you make of a system that often drew the
>>>> red-hatted hamster you requested, and perhaps a fifth of the time gave you
>>>> utter nonsense?  Or say one that you trained to create birds but sometimes
>>>> output stuff like this:
>>>>
>>>>
>>>>
>>>> <image001.png>
>>>>
>>>>
>>>>
>>>> One could
>>>>
>>>>
>>>>
>>>> a. avert one’s eyes and deem the anomalous outputs irrelevant
>>>>
>>>> or
>>>>
>>>> b. wonder if it might be possible that sometimes the system gets the
>>>> right answer for the wrong reasons (eg partial historical contingency), and
>>>> wonder whether another approach might be indicated.
>>>>
>>>>
>>>>
>>>> Benchmarks are harder than they look; most of the field has come to
>>>> recognize that. The Turing Test has turned out to be a lousy measure of
>>>> intelligence, easily gamed. It has turned out empirically that the Winograd
>>>> Schema Challenge did not measure common sense as well as Hector might have
>>>> thought. (As it happens, I am a minor coauthor of a very recent review on
>>>> this very topic: https://arxiv.org/abs/2201.02387
>>>> <https://urldefense.com/v3/__https:/arxiv.org/abs/2201.02387__;!!IKRxdwAv5BmarQ!INA0AMmG3iD1B8MDtLfjWCwcBjxO-e-eM2Ci9KEO_XYOiIEgiywK-G_8j6L3bHA$>)
>>>> But its conquest in no way means machines now have common sense; many
>>>> people from many different perspectives recognize that (including, e.g.,
>>>> Yann LeCun, who generally tends to be more aligned with you than with me).
>>>>
>>>>
>>>>
>>>> So: on the goalpost of the Winograd schema, I was wrong, and you can
>>>> quote me; but what you said about me and machine translation remains your
>>>> invention, and it is inexcusable that you simply ignored my 2019
>>>> clarification. On the essential goal of trying to reach meaning and
>>>> understanding, I remain unmoved; the problem remains unsolved.
>>>>
>>>>
>>>>
>>>> All of the problems LLMs have with coherence, reliability,
>>>> truthfulness, misinformation, etc stand witness to that fact. (Their
>>>> persistent inability to filter out toxic and insulting remarks stems from
>>>> the same.) I am hardly the only person in the field to see that progress on
>>>> any given benchmark does not inherently mean that the deep underlying
>>>> problems have solved. You, yourself, in fact, have occasionally made that
>>>> point.
>>>>
>>>>
>>>>
>>>> With respect to embeddings: Embeddings are very good for natural
>>>> language *processing*; but NLP is not the same as NL*U* – when it
>>>> comes to *understanding*, their worth is still an open question.
>>>> Perhaps they will turn out to be necessary; they clearly aren’t sufficient.
>>>> In their extreme, they might even collapse into being symbols, in the sense
>>>> of uniquely identifiable encodings, akin to the ASCII code, in which a
>>>> specific set of numbers stands for a specific word or concept. (Wouldn’t
>>>> that be ironic?)
>>>>
>>>>
>>>>
>>>> (Your GLOM, which as you know I praised publicly, is in many ways an
>>>> effort to wind up with encodings that effectively serve as symbols in
>>>> exactly that way, guaranteed to serve as consistent representations of
>>>> specific concepts.)
>>>>
>>>>
>>>>
>>>> Notably absent from your email is any kind of apology for
>>>> misrepresenting my position. It’s fine to say that “many people thirty
>>>> years ago once thought X” and another to say “Gary Marcus said X in 2015”,
>>>> when I didn’t. I have consistently felt throughout our interactions that
>>>> you have mistaken me for Zenon Pylyshyn; indeed, you once (at NeurIPS 2014)
>>>> apologized to me for having made that error. I am still not he.
>>>>
>>>>
>>>>
>>>> Which maybe connects to the last point; if you read my work, you would
>>>> see thirty years of arguments *for* neural networks, just not in the
>>>> way that you want them to exist. I have ALWAYS argued that there is a role
>>>> for them;  characterizing me as a person “strongly opposed to neural
>>>> networks” misses the whole point of my 2001 book, which was subtitled
>>>> “Integrating Connectionism and Cognitive Science.”
>>>>
>>>>
>>>>
>>>> In the last two decades or so you have insisted (for reasons you have
>>>> never fully clarified, so far as I know) on abandoning symbol-manipulation,
>>>> but the reverse is not the case: I have *never* called for dismissal
>>>> of neural networks, but rather for some hybrid between the two (as you
>>>> yourself contemplated in 1991); the point of the 2001 book was to
>>>> characterize exactly where multilayer perceptrons succeeded and broke down,
>>>> and where symbols could complement them. It’s a rhetorical trick (which is
>>>> what the previous thread was about) to pretend otherwise.
>>>>
>>>>
>>>>
>>>> Gary
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Feb 2, 2022, at 11:22, Geoffrey Hinton <geoffrey.hinton at gmail.com>
>>>> wrote:
>>>>
>>>> 
>>>>
>>>> Embeddings are just vectors of soft feature detectors and they are very
>>>> good for NLP.  The quote on my webpage from Gary's 2015 chapter implies the
>>>> opposite.
>>>>
>>>>
>>>>
>>>> A few decades ago, everyone I knew then would have agreed that the
>>>> ability to translate a sentence into many different languages was strong
>>>> evidence that you understood it.
>>>>
>>>>
>>>>
>>>> But once neural networks could do that, their critics moved the
>>>> goalposts. An exception is Hector Levesque who defined the goalposts more
>>>> sharply by saying that the ability to get pronoun references correct in
>>>> Winograd sentences is a crucial test. Neural nets are improving at that but
>>>> still have some way to go. Will Gary agree that when they can get pronoun
>>>> references correct in Winograd sentences they really do understand? Or does
>>>> he want to reserve the right to weasel out of that too?
>>>>
>>>>
>>>>
>>>> Some people, like Gary, appear to be strongly opposed to neural
>>>> networks because they do not fit their preconceived notions of how the mind
>>>> should work.
>>>>
>>>> I believe that any reasonable person would admit that if you ask a
>>>> neural net to draw a picture of a hamster wearing a red hat and it draws
>>>> such a picture, it understood the request.
>>>>
>>>>
>>>>
>>>> Geoff
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Wed, Feb 2, 2022 at 1:38 PM Gary Marcus <gary.marcus at nyu.edu> wrote:
>>>>
>>>> Dear AI Hub, cc: Steven Hanson and Geoffrey Hinton, and the larger
>>>> neural network community,
>>>>
>>>>
>>>>
>>>> There has been a lot of recent discussion on this list about framing
>>>> and scientific integrity. Often the first step in restructuring narratives
>>>> is to bully and dehumanize critics. The second is to misrepresent their
>>>> position. People in positions of power are sometimes tempted to do this.
>>>>
>>>>
>>>>
>>>> The Hinton-Hanson interview that you just published is a real-time
>>>> example of just that. It opens with a needless and largely content-free
>>>> personal attack on a single scholar (me), with the explicit intention of
>>>> discrediting that person. Worse, the only substantive thing it says is
>>>> false.
>>>>
>>>>
>>>>
>>>> Hinton says “In 2015 he [Marcus] made a prediction that computers
>>>> wouldn’t be able to do machine translation.”
>>>>
>>>>
>>>>
>>>> I never said any such thing.
>>>>
>>>>
>>>>
>>>> What I predicted, rather, was that multilayer perceptrons, as they
>>>> existed then, would not (on their own, absent other mechanisms)
>>>> *understand* language. Seven years later, they still haven’t, except
>>>> in the most superficial way.
>>>>
>>>>
>>>>
>>>> I made no comment whatsoever about machine translation, which I view as
>>>> a separate problem, solvable to a certain degree by correspondance without
>>>> semantics.
>>>>
>>>>
>>>>
>>>> I specifically tried to clarify Hinton’s confusion in 2019, but,
>>>> disappointingly, he has continued to purvey misinformation despite that
>>>> clarification. Here is what I wrote privately to him then, which should
>>>> have put the matter to rest:
>>>>
>>>>
>>>>
>>>> You have taken a single out of context quote [from 2015] and
>>>> misrepresented it. The quote, which you have prominently displayed at the
>>>> bottom on your own web page, says:
>>>>
>>>>
>>>>
>>>> Hierarchies of features are less suited to challenges such as language,
>>>> inference, and high-level planning. For example, as Noam Chomsky famously
>>>> pointed out, language is filled with sentences you haven't seen
>>>> before. Pure classifier systems don't know what to do with such sentences.
>>>> The talent of feature detectors -- in  identifying which member of some
>>>> category something belongs to -- doesn't translate into understanding
>>>> novel  sentences, in which each sentence has its own unique meaning.
>>>>
>>>>
>>>>
>>>> It does *not* say "neural nets would not be able to deal with novel
>>>> sentences"; it says that hierachies of features detectors (on their own, if
>>>> you read the context of the essay) would have trouble *understanding *novel sentences.
>>>>
>>>>
>>>>
>>>>
>>>> Google Translate does yet not *understand* the content of the
>>>> sentences is translates. It cannot reliably answer questions about who did
>>>> what to whom, or why, it cannot infer the order of the events in
>>>> paragraphs, it can't determine the internal consistency of those events,
>>>> and so forth.
>>>>
>>>>
>>>>
>>>> Since then, a number of scholars, such as the the computational
>>>> linguist Emily Bender, have made similar points, and indeed current LLM
>>>> difficulties with misinformation, incoherence and fabrication all follow
>>>> from these concerns. Quoting from Bender’s prizewinning 2020 ACL article on
>>>> the matter with Alexander Koller,
>>>> https://aclanthology.org/2020.acl-main.463.pdf
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__aclanthology.org_2020.acl-2Dmain.463.pdf&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=K-Vl6vSvzuYtRMi-s4j7mzPkNRTb-I6Zmf7rbuKEBpk&e=>,
>>>> also emphasizing issues of understanding and meaning:
>>>>
>>>>
>>>>
>>>> *The success of the large neural language models on many NLP tasks is
>>>> exciting. However, we find that these successes sometimes lead to hype in
>>>> which these models are being described as “understanding” language or
>>>> capturing “meaning”. In this position paper, we argue that a system trained
>>>> only on form has a priori no way to learn meaning. .. a clear understanding
>>>> of the distinction between form and meaning will help guide the field
>>>> towards better science around natural language understanding. *
>>>>
>>>>
>>>>
>>>> Her later article with Gebru on language models “stochastic parrots” is
>>>> in some ways an extension of this point; machine translation requires
>>>> mimicry, true understanding (which is what I was discussing in 2015)
>>>> requires something deeper than that.
>>>>
>>>>
>>>>
>>>> Hinton’s intellectual error here is in equating machine translation
>>>> with the deeper comprehension that robust natural language understanding
>>>> will require; as Bender and Koller observed, the two appear not to be the
>>>> same. (There is a longer discussion of the relation between language
>>>> understanding and machine translation, and why the latter has turned out to
>>>> be more approachable than the former, in my 2019 book with Ernest Davis).
>>>>
>>>>
>>>>
>>>> More broadly, Hinton’s ongoing dismissiveness of research from
>>>> perspectives other than his own (e.g. linguistics) have done the field a
>>>> disservice.
>>>>
>>>>
>>>>
>>>> As Herb Simon once observed, science does not have to be zero-sum.
>>>>
>>>>
>>>>
>>>> Sincerely,
>>>>
>>>> Gary Marcus
>>>>
>>>> Professor Emeritus
>>>>
>>>> New York University
>>>>
>>>>
>>>>
>>>> On Feb 2, 2022, at 06:12, AIhub <aihuborg at gmail.com> wrote:
>>>>
>>>> 
>>>>
>>>> Stephen Hanson in conversation with Geoff Hinton
>>>>
>>>>
>>>>
>>>> In the latest episode of this video series for AIhub.org
>>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__AIhub.org&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=eOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A&e=>,
>>>> Stephen Hanson talks to  Geoff Hinton about neural networks,
>>>> backpropagation, overparameterization, digit recognition, voxel cells,
>>>> syntax and semantics, Winograd sentences, and more.
>>>>
>>>>
>>>>
>>>> You can watch the discussion, and read the transcript, here:
>>>>
>>>>
>>>> https://aihub.org/2022/02/02/what-is-ai-stephen-hanson-in-conversation-with-geoff-hinton/
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__aihub.org_2022_02_02_what-2Dis-2Dai-2Dstephen-2Dhanson-2Din-2Dconversation-2Dwith-2Dgeoff-2Dhinton_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=OY_RYGrfxOqV7XeNJDHuzE--aEtmNRaEyQ0VJkqFCWw&e=>
>>>>
>>>>
>>>>
>>>> About AIhub:
>>>>
>>>> AIhub is a non-profit dedicated to connecting the AI community to the
>>>> public by providing free, high-quality information through AIhub.org
>>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__AIhub.org&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=xnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL&s=eOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A&e=>
>>>> (https://aihub.org/
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__aihub.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=IKFanqeMi73gOiS7yD-X_vRx_OqDAwv1Il5psrxnhIA&e=>).
>>>> We help researchers publish the latest AI news, summaries of their work,
>>>> opinion pieces, tutorials and more.  We are supported by many leading
>>>> scientific organizations in AI, namely AAAI
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__aaai.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=wBvjOWTzEkbfFAGNj9wOaiJlXMODmHNcoWO5JYHugS0&e=>,
>>>> NeurIPS
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__neurips.cc_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=3-lOHXyu8171pT_UE9hYWwK6ft4I-cvYkuX7shC00w0&e=>,
>>>> ICML
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__icml.cc_imls_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=JJyjwIpPy9gtKrZzBMbW3sRMh3P3Kcw-SvtxG35EiP0&e=>,
>>>> AIJ
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.journals.elsevier.com_artificial-2Dintelligence&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=eWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U&e=>
>>>> /IJCAI
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.journals.elsevier.com_artificial-2Dintelligence&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=eWrRCVWlcbySaH3XgacPpi0iR0-NDQYCLJ1x5yyMr8U&e=>,
>>>> ACM SIGAI
>>>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__sigai.acm.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=7rC6MJFaMqOms10EYDQwfnmX-zuVNhu9fz8cwUwiLGQ&e=>,
>>>> EurAI/AICOMM, CLAIRE
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__claire-2Dai.org_&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=66ZofDIhuDba6Fb0LhlMGD3XbBhU7ez7dc3HD5-pXec&e=>
>>>> and RoboCup
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.robocup.org__&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=yl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1&s=bBI6GRq--MHLpIIahwoVN8iyXXc7JAeH3kegNKcFJc0&e=>
>>>> .
>>>>
>>>> Twitter: @aihuborg
>>>>
>>>>
>>>>
>>>>
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=Ao9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4&e=>
>>>>
>>>> Virus-free. www.avast.com
>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.avast.com_sig-2Demail-3Futm-5Fmedium-3Demail-26utm-5Fsource-3Dlink-26utm-5Fcampaign-3Dsig-2Demail-26utm-5Fcontent-3Dwebmail&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=wQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ&m=waSKY67JF57IZXg30ysFB_R7OG9zoQwFwxyps6FbTa1Zh5mttxRot_t4N7mn68Pj&s=Ao9QQWtO62go0hx1tb3NU6xw2FNBadjj8q64-hl5Sx4&e=>
>>>>
>>>>
>>>>
>>> --
>> Gary Cottrell 858-534-6640 FAX: 858-534-7029
>> Computer Science and Engineering 0404
>> IF USING FEDEX INCLUDE THE FOLLOWING LINE:
>> CSE Building, Room 4130
>> University of California San Diego                                      -
>> 9500 Gilman Drive # 0404
>> <https://www.google.com/maps/search/9500+Gilman+Drive+%23+0404+La+Jolla,+Ca.+92093-0404?entry=gmail&source=g>
>> La Jolla, Ca. 92093-0404
>> <https://www.google.com/maps/search/9500+Gilman+Drive+%23+0404+La+Jolla,+Ca.+92093-0404?entry=gmail&source=g>
>>
>> Email: gary at ucsd.edu
>> Home page: http://www-cse.ucsd.edu/~gary/
>> Schedule: http://tinyurl.com/b7gxpwo
>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__tinyurl.com_b7gxpwo&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=JROnxzCHvyXQkBeidNk_-g&m=E9yFOiPfc4VgGz5PkKq6_bU98iHB8uH4xFNF4zaju-RNnFzCXKbXhyHNdnPL8rre&s=GGPEyfU2SeT7eAbyCFhOW_DUw2QV8OKHoJ7OL7vHdds&e=>
>>
>> Blind certainty - a close-mindedness that amounts to an imprisonment so
>> total, that the prisoner doesn’t  even know that he’s locked up. -David
>> Foster Wallace
>>
>>
>> Power to the people! —Patti Smith
>>
>> Except when they’re delusional —Gary Cottrell
>>
>>
>> This song makes me nostalgic for a memory I don't have -- Tess Cottrell
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> *Listen carefully,Neither the VedasNor the Qur'anWill teach you this:Put
>> the bit in its mouth,The saddle on its back,Your foot in the stirrup,And
>> ride your wild runaway mindAll the way to heaven.*
>>
>> -- Kabir
>>
> --
Gary Cottrell 858-534-6640 FAX: 858-534-7029
Computer Science and Engineering 0404
IF USING FEDEX INCLUDE THE FOLLOWING LINE:
CSE Building, Room 4130
University of California San Diego                                      -
9500 Gilman Drive # 0404
La Jolla, Ca. 92093-0404

Email: gary at ucsd.edu
Home page: http://www-cse.ucsd.edu/~gary/
Schedule: http://tinyurl.com/b7gxpwo

Blind certainty - a close-mindedness that amounts to an imprisonment so
total, that the prisoner doesn’t  even know that he’s locked up. -David
Foster Wallace


Power to the people! —Patti Smith

Except when they’re delusional —Gary Cottrell


This song makes me nostalgic for a memory I don't have -- Tess Cottrell










*Listen carefully,Neither the VedasNor the Qur'anWill teach you this:Put
the bit in its mouth,The saddle on its back,Your foot in the stirrup,And
ride your wild runaway mindAll the way to heaven.*

-- Kabir
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