Connectionists: Stephen Hanson in conversation with Geoff Hinton

[gary@ucsd.edu]

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://www.linkedin.com/in/danko-nikolic/
> --- A progress usually starts with an insight ---
>
>
>
> <https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail> Virenfrei.
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>
> 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
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CSE Building, Room 4130
University of California San Diego                                      -
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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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