[YuriNiyazov]

motte, meet bailey. Gary Marcus' shtick the entire time has been "LLMs are the wrong approach", and now the claim is "actually, the entire time I've been claiming something much weaker: LLMs that call out to code interpreters are sufficient for neurosymbolic AI"/

[qwertylicious]

It says a lot about the current discourse around AI that 6 years ago Marcus would write:

> Despite all of the problems I have sketched, I don’t think that we need to abandon deep learning.

And that would somehow be spun, today, as "LLMs are the wrong approach".

Meanwhile, another attempt to post this article here got straight up flagged, I can only assume because this whole topic has become about religious orthodoxy vs the heretics.

[YuriNiyazov]

Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.

[4b11b4]

have also updated

[hooah]

He’s been saying that LLM isn’t a “universal solvent”, not as a “recent claim”.

''' In my 2018 Deep Learning: A Critical Appraisal for example, I wrote

Despite all of the problems I have sketched, I don’t think that we need to abandon deep learning.

Rather, we need to reconceptualize it: not as a universal solvent, but simply as one tool among many, a power screwdriver in a world in which we also need hammers, wrenches, and pliers, not to mentions chisels and drills, voltmeters, logic probes, and oscilloscopes. '''

[YuriNiyazov]

Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.

[kgwgk]

2001: Resisting the conventional wisdom that says that if the mind is a large neural network it cannot simultaneously be a manipulator of symbols, Marcus outlines a variety of ways in which neural systems could be organized so as to manipulate symbols, and he shows why such systems are more likely to provide an adequate substrate for language and cognition than neural systems that are inconsistent with the manipulation of symbols.

2018: While none of this work has yet fully scaled towards anything like full-service artificial general intelligence, I have long argued (Marcus, 2001) that more on integrating microprocessor-like operations into neural networks could be extremely valuable.

2022: Where people like me have championed “hybrid models” that incorporate elements of both deep learning and symbol-manipulation, Hinton and his followers have pushed over and over to kick symbols to the curb.

[YuriNiyazov]

Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.