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"/
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.
I 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.
He adopts a conspiratorial lens, (or at least implies it), that neurosymbolic AI was "kept down" over the last 4 decades, which is a very funny reframing of the fact that it simply never was useful enough to lift itself off the ground by the virtue of its own merits in the first place. If a ground-up neurosymbolic approached had shown promise in getting an AI system to the general level of intelligence LLM's have reached, it would have been adopted and scaled up. The money, research and effort went to what was useful, and transformers won out by virtue of their undeniable utility.
As far as I can tell, putting the conspiratorial thinking aside, he's not really wrong, but I'm also not sure it matters that much.
If neurosymbolic AI was "sidelined" in favor of "connectionist" pure NN scaling, I don't think it was part of a conspiracy or deeply embedded ideological bias. I mean, maybe that's the case, but it seems far more likely to me that pure deep learning scaling just provided a more incremental and accessible on-ramp to building real-world systems that are genuinely useful for hundreds of millions of users. If anything, I think the lesson here was to spend less time theory-crafting and more time building. In this case, it looks like it was the builders who got to the endpoint that was only imagined by the theory-crafters, and that's what matters at the end of the day.
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[ 3.7 ms ] story [ 27.2 ms ] thread> 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.
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...
[6]: https://arxiv.org/pdf/2501.05435
"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.
Filip Pieknewski next.
If neurosymbolic AI was "sidelined" in favor of "connectionist" pure NN scaling, I don't think it was part of a conspiracy or deeply embedded ideological bias. I mean, maybe that's the case, but it seems far more likely to me that pure deep learning scaling just provided a more incremental and accessible on-ramp to building real-world systems that are genuinely useful for hundreds of millions of users. If anything, I think the lesson here was to spend less time theory-crafting and more time building. In this case, it looks like it was the builders who got to the endpoint that was only imagined by the theory-crafters, and that's what matters at the end of the day.