Comment by highfrequency
6 hours ago
Per the author’s links, he warned that deep learning was hitting a wall in both 2018 and 2022. Now would be a reasonable time to look back and say “whoops, I was wrong about that.” Instead he seems to be doubling down.
The author is a bit of a stopped clock that who has been saying deep learning is hitting a wall for years and I guess one day may be proved right?
He probably makes quite good money as the go to guy for saying AI is rubbish? https://champions-speakers.co.uk/speaker-agent/gary-marcus
a contrarian needs to keep spruiking the point, because if he relents, he loses the core audience that listened to him. That's why it's also the same with those who keep predicting market crashes etc.
Well the same can be said about non contrarians ...
Well..... tbf. Each approach has hit a wall. It's just that we change things a bit and move around that wall?
But that's certainly not a nuanced / trustworthy analysis of things unless you're a top tier researcher.
Indeed. A mouse that runs through a maze may be right to say that it is constantly hitting a wall, yet it makes constant progress.
An example is citing Mr Sutskever's interview this way:
> in my 2022 “Deep learning is hitting a wall” evaluation of LLMs, which explicitly argued that the Kaplan scaling laws would eventually reach a point of diminishing returns (as Sutskever just did)
which is misleading, since Sutskever said it didn't hit a wall in 2022[0]:
> Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling
The larger point that Mr Marcus makes, though, is that the maze has no exit.
> there are many reasons to doubt that LLMs will ever deliver the rewards that many people expected.
That is something that most scientists disagree with. In fact the ongoing progress on LLMs has already accumulated tremendous utility which may already justify the investment.
[0]: https://garymarcus.substack.com/p/a-trillion-dollars-is-a-te...
I like how when you click the "key achievements" tab on this site it just says
> 1997 - Professor of Psychology and Neural Science
I thought the point though was that Sutskever is saying it too.
If something hits a wall and then takes a trillion dollars to move forward but it does move forward, I'm not sure I'd say it was just bluster.
Even further back:
> Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)
(From "Deep Learning: A Critical Appraisal")
I read Deep Learning: A Critical Appraisal ? in 2018, and just went back and skimmed it
https://arxiv.org/abs/1801.00631
Here are some of the points
Is deep learning approaching a wall? - He doesn't make a concrete prediction, which seems like a hedge to avoid looking silly later. Similarly, I noticed a hedge in this post:
Of course it ain’t over til it’s over. Maybe pure scaling ... will somehow magically yet solve ...
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But the paper isn't wrong either:
Deep learning thus far is data hungry - yes, absolutely
Deep learning thus far is shallow and has limited capacity for transfer - yes, Sutskeyer is saying that deep learning doesn't generalize as well as humans
Deep learning thus far has no natural way to deal with hierarchical structure - I think this is technically true, but I would also say that a HUMAN can LEARN to use LLMs while taking these limitations into account. It's non-trivial to use them, but they are useful
Deep learning thus far has struggled with open-ended inference - same point as above -- all the limitations are of course open research questions, but it doesn't necessarily mean that scaling was "wrong". (The amount of money does seem crazy though, and if it screws up the US economy, I wouldn't be that surprised)
Deep learning thus far is not sufficiently transparent - absolutely, the scaling has greatly outpaced understanding/interpretability
Deep learning thus far has not been well integrated with prior knowledge - also seems like a valuable research direction
Deep learning thus far cannot inherently distinguish causation from correlation - ditto
Deep learning presumes a largely stable world, in ways that may be problematic - he uses the example of Google Flu Trends ... yes, deep learning cannot predict the future better than humans. That is a key point in the book "AI Snake Oil". I think this relates to the point about generalization -- deep learning is better at regurgitating and remixing the past, rather than generalizing and understanding the future.
Lots of people are saying otherwise, and then when you call them out on their predictions from 2 years ago, they have curiously short memories.
Deep learning thus far works well as an approximation, but its answers often cannot be fully trusted - absolutely, this is the main limitation. You have to verify its answers, and this can be very costly. Deep learning is only useful when verifying say 5 solutions is significantly cheaper than coming up with one yourself.
Deep learning thus far is difficult to engineer with - this is still true, e.g. deep learning failed to solve self-driving ~10 years ago
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So Marcus is not wrong, and has nothing to apologize for. The scaling enthusiasts were not exactly wrong either, and we'll see what happens to their companies.
It does seem similar to be dot com bubble - when the dust cleared, real value was created. But you can also see that the marketing was very self-serving.
Stuff like "AGI 2027" will come off poorly -- it's an attempt by people with little power to curry favor with powerful people. They are serving as the marketing arm, and oddly not realizing it.
"AI will write all the code" will also come off poorly. Or at least we will realize that software creation != writing code, and software creation is the valuable activity
Several OpenAI people said in 2023 that they were surprised by the acceptance of the public. Because they thought that LLMs were not so impressive.
The public has now caught up with that view. Familiarity breeds contempt, in this case justifiably so.
EDIT: It is interesting that in a submission about Sutskever essentially citing Sutskever is downvoted. You can do it here, but the whole of YouTube will still hate "AI".
> in this case justifiably so
Oh please. What LLMs are doing now was complete and utter science fiction just 10 years ago (2015).
This.
I’m under the impression that people who are still saying LLMs are unimpressive might just be not using them correctly/effectively.
Or as Primagean says: “skill issue”
Why would the public care what was possible in 2015? They see the results from 2023-2025 and aren't impressed, just like Sutskever.
What exactly are they doing? I've seen a lot of hype but not much real change. It's like a different way to google for answers and some code generation tossed in, but it's not like LLMs are folding my laundry or mowing my lawn. They seem to be good at putting graphic artists out of work mainly because the public abides the miserable slop produced.
Not really.
Any fool could have anticipated the eventual result of transformer architecture if pursued to its maximum viable form.
What is impressive is the massive scale of data collection and compute resources rolled out, and the amount of money pouring into all this.
But 10 years ago, spammers were building simple little bots with markov chains to evade filters because their outputs sounded plausibly human enough. Not hard to see how a more advanced version of that could produce more useful outputs.
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> learning was hitting a wall in both 2018 and 2022
He wasn't wrong though.