6 ms·
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 "si
by nilkn 1y ago
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.
- 4b11b4 1y agoThat resonates. There _are_ a lot of good approximation functions can be developed from deep learning and good data and now RL on top. But then, we really do need symbolism, and now we need to somehow combine them. And it'll be different for text vs vision... Lots of ...s ahead
- 4b11b4 1y agoBut how to even combine them. Is it only via another AGENT who has a symbolism tool. and if that (group of agents) cant extract multiple symbolisms from the context, of one which best fits, from the current approximation (context) then..