it’s a shame to see someone like @NateSilver538 does not understand a basic, fundamental technical distinction. (@tristanharris and Kevin Ro…

it’s a shame to see someone like @NateSilver538 does not understand a basic, fundamental technical distinction. (@tristanharris and Kevin Roose missed it the other day, too.) pure LLMs were (very roughly and certainly not exactly) like stochastic parrots. harnesses, tools, code interpreters heralded the spread of neurosymbolic AI. the new systems are hybrids; they work differently. the somewhat weak metaphor is much weaker with them. but the fact that they are less like stochastic parrots doesn’t mean the original systems weren’t, nor that pure scaling worked. it means that neurosymbolic AI was an important next step. this still doesn’t mean we have AGI; most of the progress works best in verifiable domains; truly reliable general intelligence has certainly not yet arrived. (my 2020 article Next Decade in AI foresaw all of this, and what’s still missing, including world models, which i called cognitive models there.) OK then give Astra or Fable a 10,000-line program, ask it to debug it, ask it to understand what's going on, ask it to make revisions. It's certainly not perfect. I'm bullish on human expertise fwiw. But it sure as hell is way more than a "stochastic parrot."

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Source: Gary Marcus (X) | 2026-09-25