neurosymbolic

Lisan al Gaib reposted
Andrew Lampinen @AndrewLampinen
The question is "how much is each component is the system contributing to its intelligence & generality" — and there I think it's pretty clear that the neural component is still the thing doing the interesting hypothesis or plan generation, deciding what went wrong, etc. 1/

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François Chollet @fchollet · 13h
I would have assumed it was fairly obvious, but in case it's not: a million-line codebase (also known as a "harness"), running at inference time, orchestrating thousands of calls to a neural network for any given task, is the exact definition of a "neurosymbolic ...

7:39 AM · Aug 6, 2026 · 25.3K Views
12 replies, 14 reposts, 191 likes, 64 bookmarks
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Andrew Lampin... @AndrewLampin... · 10h
This is very clearly different from the vision that many neurosymbolic advocates had a few years ago, e.g. these quotes (taken from arxiv.org/abs/2305.00813 and arxiv.org/abs/1801.00631) in which symbol manipulation did the "intelligent" part. 2/

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These arguments, together with similar ones from others, drove a longstanding trend to dismiss neural networks as only capable of modeling simple perceptual processing, rather than "real" higher-level cognition, which is symbolic and systematic. For example "while data driven neural network-based AI algorithms effectively model machine perception, symbolic knowledge-based AI is better [cut off]

ai capabilityharness vs modelneurosymbolicandrew lampinenfrancois chollet

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For example "while data driven neural network-based AI algorithms effectively model machine perception, symbolic knowledge-based AI is better suited for modeling machine cognition," or "The right move may to be to integrate deep learning, which excels at perceptual classification, with symbolic systems, which excel at inference and abstraction. One might think such a potential merger on analogy to the brain; perceptual input systems, like primary sensory cortex, seem to do something like what deep learning does, but there are other areas, like Broca's area and prefrontal cortex, that seem to operate at much higher level of abstraction." Like the earlier advocates of symbols, these perspectives suggest that what's going on in the brain to make intelligent inferences is fundamentally symbolic processing, and that neural networks are not suited to these kinds of inferences — at best, just to perception.

2 replies, 3 reposts, 53 likes, 1.7K

Andrew Lampin... @AndrewLampin... · 10h
Humans also benefit from being "wrapped" in rules that make us recheck our behavior / respect constraints (e.g., code we submit must pass tests, email clients that say "don't click this, we think it's phishing") — but nobody would mistake them for part of our intelligence. 3/
3 replies, 1 repost, 35 likes, 1.2K

Andrew Lampin... @AndrewLampin... · 10h
I would love to see more harness on/off comparisons of systems published to illustrate the point, though! 4/4

ai capabilityharness vs modelneurosymbolicandrew lampinen