Gary Marcus has made himself the most prominent proponent of "deep learning is a parlor trick and cannot create real AI" (note: deep learning, not just LLMs), which he has been saying almost unmodified from before LLMs even existed to now.

Though I think he might have stopped setting specific concrete goalposts to move, sometime between when I last checked in on him and now. After (often almost instantly) losing a couple dozen consecutive rounds of "LLMs/deep learning fundamentally cannot/will never", while never acknowledging any of it.

What does it mean when someone is sticking to their guns? Is it a bad thing? I do appreciate consistency, albeit a fair consistency and Gary Marcus's points do stand. When these criticisms are addressed (if it's possible to) you'd probably hear less from Gary Marcus.

There's also a perspective that all of the ongoing problems have been the same while newer techniques shove them under different rugs. So I can see how that would look like that to the credulous.

This is exactly what's happening, with the additional feature that the newer techniques likewise come with their own hype.

Show me the goalposts I have moved, with actual quotes to prove it. nobody ever has when I have asked.

Aso consider eg the bets I have made with Miles Brundage (and offered to Musk(, with money where I have backed up my views.

good summary of predictions i made - mostly correct – is here: https://open.substack.com/pub/garymarcus/p/25-ai-predictions...

I'm subscribed to his substack because he's curmudgeonly and it's funny, and he occasionally makes good points, but he's constantly beating the same anti-hype drum. He might not get any particular facts wrong but you can count on him only focusing on the facts that let him continue to show AI through that same anti-hype lense.

How would it be possible to, say, show the reality of a forest fire's devastation while not appearing to show a bias for showing charred trees?