Connectionists: Stephen Hanson in conversation with Geoff Hinton

Wed Feb 2 12:51:16 EST 2022

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://aclanthology.org/2020.acl-main.463.pdf>, 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, 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=>
> 
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