Connectionists: Stephen Hanson in conversation with Geoff Hinton: Some biological neural models of of causality, ambiguous visual percepts, and handwritten letters

Sat Feb 5 12:53:20 EST 2022

Dear All,

The problems that have been raised below about by Geoffrey Hinton and others concerning causality,  ambiguous visual percepts, and handwritten letters have been at least partially solved by specific neural models. Below I take the liberty of mentioning some of these models and where you can read more if you are interested:

CAUSALITY AND ADAPTIVE RESONANCE THEORY

Many of you may already know that Adaptive Resonance Theory, or ART, is currently the most advanced cognitive and neural theory that explains how humans learn to consciously attend, recognize, and predict objects and events in a changing world.

ART does this by explaining how we learn to attend to the critical feature patterns that successfully predict what will happen next in a familiar environment, while suppressing irrelevant features. These critical feature patterns can be learned at multiple levels of brain organization, ranging from concrete to abstract. Critical features are the ones that we come to believe cause predicted outcomes.

It is fair to ask: Why should anyone believe this ART proposal? There are several types of reasons:

First, all the foundational hypotheses of ART have been supported by subsequent psychological and neurobiological experiments.

Second, ART proposes principled and unifying explanations of hundreds of additional experiments. It has also made numerous predictions, many of which have been supported by subsequent psychological and/or neurobiological experiments.

These successes may derive from the fact that ART explains how human brains solve the stability-plasticity dilemma; that is, how we can learn quickly without experiencing catastrophic forgetting.

Third, there is a deeper reason to believe that ART explains fundamental properties of how our brains make our minds:

In 1980, in the journal Psychological Review, I derived ART from a thought experiment whose hypotheses are facts that are familiar to us all because they are ubiquitous constraints on the evolution of our brains. The words mind and brain are not mentioned during this thought experiment.

The thought experiment from which ART is derived emerges from an analysis of how any system can autonomously correct predictive errors in a changing world that is filled with unexpected events.

ART and its variations are thus universal solutions of this fundamental problem. Perhaps that it why ART has been applied to help design autonomous adaptively intelligent systems for large-scale applications.

I was particularly excited when I realized that ART also helps to explain how, where in our brains, and why evolution created conscious states of seeing, hearing, feeling, and knowing, and how these conscious states enable planning and action to realize valued goals.

ART has also been extended to explain and predict a lot of data about cognitive-emotional interactions and affective neuroscience.

When these cognitive and/or cognitive-emotional processes break down in specific ways, then behavioral symptoms of mental disorders emerge, including Alzheimer's disease, autism, Fragile X syndrome, medial temporal amnesia, schizophrenia, ADHD, PTSD, auditory and visual neglect and agnosia, and disorders of slow-wave sleep.

Here are two articles that discuss how ART learns the critical feature patterns that predict future outcomes and thus that embody our understanding of their causes:

Grossberg, S. (2017). Towards solving the Hard Problem of Consciousness: The varieties of brain resonances and the conscious experiences that they support. Neural Networks, 87, 38–95.
https://www.sciencedirect.com/science/article/pii/S0893608016301800

Grossberg, S. (2021). A canonical laminar neocortical circuit whose bottom-up, horizontal, and top-down pathways control attention, learning, and prediction. Frontiers in Systems Neuroscience. Published online: 23 April 2021.
https://www.frontiersin.org/articles/10.3389/fnsys.2021.650263/full

I have also discussed these issues in my recent book:

Conscious Mind, Resonant Brain: How Each Brain Makes a Mind

which just won the 2022 PROSE book award in Neuroscience from the Association of American Publishers:

https://global.oup.com/academic/product/conscious-mind-resonant-brain-9780190070557?cc=us&lang=en&

https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552

Some of the following results are also summarized in the book:

AMBIGUOUS VISUAL PERCEPTS AND BISTABILITY

The following articles propose model explanations of ambiguous visual percepts that are more complicated than the ones that Geoffrey Hinton mentioned. They show how attention shifts can dramatically alter the way in which we consciously see ambiguous visual percepts of various kinds:

Grossberg, S., and Swaminathan, G. (2004). A laminar cortical model for 3D perception of slanted and curved surfaces and of 2D images: development, attention and bistability. Vision Research, 44, 1147-1187.
https://sites.bu.edu/steveg/files/2016/06/GroSwa2004VR.pdf

Grossberg, S., Yazdanbakhsh, A., Cao, Y., and Swaminathan, G. (2008). How does binocular rivalry emerge from cortical mechanisms of 3-D vision? Vision Research, 48, 2232-2250.
https://sites.bu.edu/steveg/files/2016/06/GroYazCaoSwaVR2008.pdf

PRODUCTION AND PERCEPTION OF HANDWRITTEN LETTERS

The following articles propose how children and adults learn and recognize cursive script, which includes the problem of producing and recognizing isolated cursive letters:

Bullock, D., Grossberg, S., and Mannes, C. (1993). A neural network model for cursive script production. Biological Cybernetics, 70, 15-28.
https://sites.bu.edu/steveg/files/2016/06/BulGroMan1993BiolCyb.pdf

Grossberg, S. and Paine, R.W.(2000). A neural model of corticocerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements. Neural Networks, 13, 999-1046.
https://sites.bu.edu/steveg/files/2016/06/GroPai00.pdf

Paine, R.W., Grossberg, S., and Van Gemmert, A.W.A. (2004). A quantitative evaluation of the AVITEWRITE model of handwriting learning . Human Movement Science, 23, 837-860.
https://sites.bu.edu/steveg/files/2016/06/PaiGrovanGem2004HMS.pdf

If you have any comments or questions about the above results, please feel free to send them to me. I will do my best to reply.

Best,

Steve Grossberg

Stephen Grossberg
http://en.wikipedia.org/wiki/Stephen_Grossberg
http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en
https://youtu.be/9n5AnvFur7I
https://www.youtube.com/watch?v=_hBye6JQCh4
https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552

Wang Professor of Cognitive and Neural Systems
Director, Center for Adaptive Systems
Professor Emeritus of Mathematics & Statistics,
       Psychological & Brain Sciences, and Biomedical Engineering
Boston University
sites.bu.edu/steveg
steve at bu.edu

________________________________
From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu> on behalf of Geoffrey Hinton <geoffrey.hinton at gmail.com>
Sent: Friday, February 4, 2022 3:24 PM
To: Dietterich, Thomas <tgd at oregonstate.edu>
Cc: AIhub <aihuborg at gmail.com>; connectionists at mailman.srv.cs.cmu.edu <connectionists at mailman.srv.cs.cmu.edu>
Subject: Re: Connectionists: Stephen Hanson in conversation with Geoff Hinton

I agree that it's nice to have a causal explanations. But I am not convinced there will ever be a simple causal explanation for how you recognize that a handwritten 2 is a 2. We can introspect on how we do it and this may or may not give some insight into how we check our answer, but the immediate sense that a handwritten 2 is a 2 is computed by a neural net that is not functionally equivalent to any simple and easily explainable procedure.

This does not mean that we should give up on trying to make artificial neural nets work more like real ones. People can see a tilted square as either an upright diamond or a tilted square and, so far as I know, a convnet does not exhibit this type of alternate percept.  People seem to impose hierarchical structural descriptions on images and sound waves and they clearly impose intrinsic coordinate frames on wholes and parts. If this is what Gary means by symbolic then I don’t disagree that neural nets should do symbol processing. However, there is a very different meaning of "symbolic". A pure atomic symbol has no internal structure. The form of the symbol itself tells you nothing about what it denotes. The only relevant properties it has are that it's identical to other instances of the same symbol and different from all other symbols.  That's totally different from a neural net that uses embedding vectors.  Embedding vectors have a rich internal structure that dictates how they interact with other embedding vectors. What I really object to is the following approach: Start with pure symbols and rules for how to manipulate structures made out of pure symbols. These structures themselves can be denoted by symbols that correspond to  memory addresses where the bits in the address tell you nothing about the content of the structure at that address.  Then when the rule-based approach doesn't work for dealing with the real world (e.g. machine translation) try to use neural nets to convert the real world into pure symbols and then carry on with the rule-based approach. That is like using an electric motor to inject the gasoline into the same old gasoline engine instead of just replacing the gasoline engine with an electric motor.

On Fri, Feb 4, 2022 at 2:32 AM Dietterich, Thomas <tgd at oregonstate.edu<mailto:tgd at oregonstate.edu>> wrote:

“Understanding” is not a Boolean. It is a theorem that no system can enumerate all of the consequences of a state of affairs in the world.

For low-stakes application work, we can be satisfied by a system that “does the right thing”. If the system draws a good picture, that’s sufficient. It “understood” the request.

But for higher-stakes applications---and for advancing the science---we seek a causal account of how the components of a system cause it to do the right thing. We are hoping that a small set of mechanisms can produce broad coverage of intelligent behavior. This gives us confidence that the system will respond correctly outside of the narrow tasks on which we have tested it.

--Tom

Thomas G. Dietterich, Distinguished Professor Emeritus

School of Electrical Engineering and Computer Science

US Mail: 1148 Kelley Engineering Center

Office: 2067 Kelley Engineering Center

Oregon State Univ., Corvallis, OR 97331-5501

Voice: 541-737-5559; FAX: 541-737-1300

URL: http://web.engr.oregonstate.edu/~tgd/

From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto:connectionists-bounces at mailman.srv.cs.cmu.edu>> On Behalf Of Gary Marcus
Sent: Thursday, February 3, 2022 8:26 AM
To: Danko Nikolic <danko.nikolic at gmail.com<mailto:danko.nikolic at gmail.com>>
Cc: connectionists at mailman.srv.cs.cmu.edu<mailto:connectionists at mailman.srv.cs.cmu.edu>; AIhub <aihuborg at gmail.com<mailto:aihuborg at gmail.com>>
Subject: Re: Connectionists: Stephen Hanson in conversation with Geoff Hinton

[This email originated from outside of OSU. Use caution with links and attachments.]

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://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstatic.googleusercontent.com%2Fmedia%2Fresearch.google.com%2Fen%2F%2Farchive%2Funsupervised_icml2012.pdf&data=04%7C01%7Ctgd%40oregonstate.edu%7C3db6ca275cc748415eaa08d9e732a318%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637795026944990348%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=l1WwVtsu%2BBMn0UfnWdN7tHCdsWTdIi9P%2Ffd50ThMgEs%3D&reserved=0>] 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<mailto: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
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On Thu, Feb 3, 2022 at 9:55 AM Asim Roy <ASIM.ROY at asu.edu<mailto: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

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From: Connectionists <connectionists-bounces at mailman.srv.cs.cmu.edu<mailto: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<mailto:geoffrey.hinton at gmail.com>>
Cc: AIhub <aihuborg at gmail.com<mailto:aihuborg at gmail.com>>; connectionists at mailman.srv.cs.cmu.edu<mailto: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://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.com%2Fv3%2F__https%3A%2Farxiv.org%2Fabs%2F2201.02387__%3B!!IKRxdwAv5BmarQ!INA0AMmG3iD1B8MDtLfjWCwcBjxO-e-eM2Ci9KEO_XYOiIEgiywK-G_8j6L3bHA%24&data=04%7C01%7Ctgd%40oregonstate.edu%7C3db6ca275cc748415eaa08d9e732a318%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637795026944990348%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=muB9%2FpE63uI65lV8LwulXTZQWRRVVsH89PCIcp6TAcA%3D&reserved=0>) 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 NLU – 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<mailto: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<mailto: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://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aclanthology.org_2020.acl-2Dmain.463.pdf%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DxnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL%26s%3DK-Vl6vSvzuYtRMi-s4j7mzPkNRTb-I6Zmf7rbuKEBpk%26e%3D&data=04%7C01%7Ctgd%40oregonstate.edu%7C3db6ca275cc748415eaa08d9e732a318%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637795026944990348%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=mhs4l94QkmKDeNH2BalecsEBsKbCIlOYa7BX4TkXS4U%3D&reserved=0>, 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<mailto:aihuborg at gmail.com>> wrote:

?

Stephen Hanson in conversation with Geoff Hinton

In the latest episode of this video series for AIhub.org<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttp-3A__AIhub.org%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3DxnFSVUARkfmiXtiTP_uXfFKv4uNEGgEeTluRFR7dnUpay2BM5EiLz-XYCkBNJLlL%26s%3DeOtzMh8ILIH5EF7K20Ks4Fr27XfNV_F24bkj-SPk-2A%26e%3D&data=04%7C01%7Ctgd%40oregonstate.edu%7C3db6ca275cc748415eaa08d9e732a318%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637795026944990348%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=2QAoxDbJIG9ogvM30Xq42Qdm4y8Cx7iOy3HpDpph0sM%3D&reserved=0>, 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://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Furldefense.proofpoint.com%2Fv2%2Furl%3Fu%3Dhttps-3A__aihub.org_2022_02_02_what-2Dis-2Dai-2Dstephen-2Dhanson-2Din-2Dconversation-2Dwith-2Dgeoff-2Dhinton_%26d%3DDwMFaQ%26c%3DslrrB7dE8n7gBJbeO0g-IQ%26r%3DwQR1NePCSj6dOGDD0r6B5Kn1fcNaTMg7tARe7TdEDqQ%26m%3Dyl7-VPSvMrHWYKZFtKdFpThQ9UTb2jW14grhVOlAwV21R4FwPri0ROJ-uFdMqHy1%26s%3DOY_RYGrfxOqV7XeNJDHuzE--aEtmNRaEyQ0VJkqFCWw%26e%3D&data=04%7C01%7Ctgd%40oregonstate.edu%7C3db6ca275cc748415eaa08d9e732a318%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637795026944990348%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=wNqUnQ5%2BASwQHX76s9dIWeiL5cQWSeKSCQBHd6yhQ6U%3D&reserved=0>

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