by chubot | 2025-11-28 16:39:16+00:00
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 ...

---

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

---

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