Deep Learning: A Critical Appraisal

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

or so years. He and others paint themselves as sceptics and bulwarks against the current hype (which I can assure you, I hate at least as much as they do). But, to me, they are cynics, not sceptics. I try to maintain a positive and open mind of other researchers, but Marcus lost me pretty much at "first contact" when a student in the group who leaned towards cognitive science had us read "Deep Learning: A Critical Appraisal" by Marcus (2018) [1] back around when it was published. Finally I could get into the mind of this guy so many people were talking about! 27 pages and yet I learned next to nothing new as the criticism was just the same one we have heard for decades: "Statistical learning has limits! It may not lead to 'truly" intelligent machines!". Not only that, the whole piece consistently conflates deep learning and statistical learning for no reason at all, reads as if it was rushed (and not proofed), emphasises the author's research strongly rather than giving a broad overview, etc. In short, it is bad, very bad as a scientific piece. At times, I read short excerpts of an article Marcus has written and yet sadly it is pretty much the same thing all over again. [1]: https://arxiv.org/abs/1801.00631 There is a horrible market to "sell" hype when it comes to artificial intelligence, but there is also a horrible market to "sell" anti-hype. Sadly, both brings traffic, attention, talk invitations, etc. Two largely unscientific tribes, that I personally would rather do without, with their own profiting gurus.

conversation. Gary Marcus is an AI practitioner with a psychology background: https://en.wikipedia.org/wiki/Gary_Marcus he has a decidely different perspective that I think is a lot more grounded and reasoned: https://arxiv.org/abs/1801.00631 Also related: https://idlewords.com/talks/superintelligence.htm

preceding AI methods in failing to solve general AI problems, that is, a failure to generalize and scale up beyond serving a specific narrow task, AKA brittleness.) For instance, Gary Marcus at NYU has addressed the present limits of DL in terms of cognition in several papers and talks: https://arxiv.org/abs/1801.00631 https://medium.com/@GaryMarcus/the-deepest-problem-with-deep... Likewise, Doug Hofstadter has been critical of using DL to automate machine translation's semantic nuances: https://qz.com/1088714/qa-douglas-hofstadter-on-why-ai-is-fa... At present, it's still unclear how DL could surmount single-task brittleness to address these shortcomings, much less diversify into the use of logic or the modeling of concepts, their dependencies, causality, explain itself, and much more. Unbridled faith in DL's ability to jump these sharks should be tempered by the AI community's past mismeasures of earlier AI tech that we were sure would conquer everest over the years, but in fact fell far short (e.g. perceptrons, expert systems, backprop-driven shallow nets, and oh so much more).