o3 and Grok 4 accidentally vindicate neurosymbolic AI
garymarcus.substack.com
o3 and Grok 4 accidentally vindicate neurosymbolic AI
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Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#2motte, 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"/
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#3motte, 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"/
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. '''
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#4motte, 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"/
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.
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#5I mostly agree with Gary on the core premise of this post, which I interpret generally as "it would be a good idea to pursue neuro-symbolic AI, not just deep learning."
A couple of additional thoughts:
1. She goes on to point out that the field has become an intellectual monoculture, with the neurosymbolic approach largely abandoned, and massive funding going to the pure connectionist (neural network) approach
Just to nitpick... that is largely true, but with the caveat that there has been something of a resurgence of interest in neuro-symbolic AI over just the last couple of years. There's been a series of "Neuro-Symbolic AI Summer School" events[1][2][3] going on since 2022 with the next one coming up in August. And there have been recent books[4][5] published specifically on neuro-symbolic AI. You'll also find recent papers on neuro-symbolic AI on arXiv[6]. So for those who are interested in this topic, there is definitely activity underway "out there".
2. Including LLMs somewhere in the next evolution of AI makes sense to me, but leaving them at the core may be a mistake.
I've spent a lot of time thinking about this, and generally agree with this sentiment. Some kind of fusion of LLM's (or "connectionism" in general) and symbolic processing seems desirable, but I'm not sure that we should rely on LLM's to be "core" and try to just layer symbolic processing on top of what we get from the LLM. I have my own thoughts on how such an integration might work, but it's all still speculative at the moment. But I find the whole notion worthy enough to invest time and attention into it, for whatever that is worth.
[1]: https://ibm.github.io/neuro-symbolic-ai/events/ns-summerscho...
[2]: https://neurosymbolic.github.io/nsss2023/
[3]: https://neurosymbolic.github.io/nsss2024/
[4]: https://www.amazon.com/Neuro-Symbolic-AI-transparent-trustwo...
[5]: https://www.iospress.com/catalog/books/handbook-on-neurosymb...
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#6motte, 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"/
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.
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#7motte, 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"/
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…
Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#8motte, 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"/
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, wrench…
Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#9motte, 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"/
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 manipulati…
Thanks for your reply; I can’t edit the original comment but I have updated my personal understanding of Marcus’ position.
Re: o3 and Grok 4 accidentally vindicate neurosymbolic AI
#10It's a very funny read.
"See, LLMs that are allowed to use Python perform better than ones that aren't, and Python is symbolic, so I was right all along!"
Looks like a surrender to me.