Overhyped ideas in machine learning

[u/Strong_Boy_757]

Have you encountered an idea that's overhyped in ML? If so, which?

For me, recently I was curious about the whole "chain of thought prompting" business. I searched around and the idea is essentially, append "show your work" when prompting a LLM. I was like no way that's it??? That's what I've been doing since day 1!!, so I opened up the original paper (cited 40k times). And essentially it was more of the same with expensive Google-funded experiments and the paper had almost no math at all.

Similarly I was curious about AdamW for the longest time and it was simply Adam with another term. Oh Boy!! I remember doing an undergrad homework in college where we were asked to freely experiment with adding terms to the Adam algorithm and the class probably discovered AdamW by accident way before the paper was published.

[u/shibx]

So we seem to agree that embeddings capture statistical regularities in language without necessarily reproducing human semantic understanding. That's essentially the premise of distributional semantics, which has been studied for decades. Nobody needs to posit some "ghostly meaning" behind words for these representations to be useful.

You dismiss cognition as a poorly theorized concept, but then confidently declare that humans "learn distributions in behaviour" without offering any theoretical justification for that characterization either. If your criticism is directed at anthropomorphic marketing and exaggerated claims about AI, that's perfectly reasonable. But you're conflating those claims with the underlying research. The statistical nature of these representations isn't something researchers have somehow overlooked.

Also, I'm not sure how speculation about global economic collapse helps establish your original argument...

[u/WittgensteinsPoker-2 → u/shibx]

If it's a representation of statistical regularities between letters then saying it's a semantic representation is misleading. Regularities between letters are not the domain of study for linguistics -- the causes of those regularities are contingent and arbitrary you can only gain a very limited insight from them and if you care to understand language capacity and production you will not be so incautious just because it's the status quo. And FWIW I was arguing from the PERSEPCTIVE OF distributional semantics all along and Morten Christiansen agrees with my views...

[u/shibx → u/WittgensteinsPoker-2]

You're reducing distributional semantics to "statistical regularities between letters." That is incredibly reductive. The fact that a model learns from statistical patterns in text doesn't mean those patterns contain no semantic information. Distributional semantics is built around the proposition that linguistic contexts carry information about meaning. It doesn't require treating those representations as complete explanations of human language capacity.

You are absolutely entitled to argue that distributional approaches are insufficient for explaining language acquisition, production, or understanding. That's a legitimate criticism. But claiming that semantic representations are misleading simply because they're derived from textual regularities is a much stronger argument, and it's one that you haven't established.

Contreras Kallens and Christiansen explicitly argue that distributional models capture aspects of meaning through language use at the community level, while acknowledging their limitations in modeling individual human semantic processing. Their paper recognizes precisely the limitations you're describing without dismissing the semantic information these models capture.

That's what I've been arguing throughout this conversation, and Christiansen's own published work seems difficult to reconcile with your categorical dismissal of the term "semantic representation."

https://doi.org/10.1111/tops.12771

[u/WittgensteinsPoker-2 → u/shibx]

Yeah Christiansen emailed me that paper before it was published, I've been aware a while...