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People ask me, "didn't you say before ChatGPT that deep learning had hit a wall and there would be no more progress?" I have never said this. I was saying the opposite (that scaling DL would deliver). You might be thinking of Gary Marcus. My pre-ChatGPT position (below) was that scaling up DL would keep delivering better and better results, and *also* that it wasn't the way to AGI (as I defined it: human-level skill acquisition efficiency). This was a deeply unpopular position at the time (neither AI skeptic nor AGI-via-DL-scaling prophet). It is now completely mainstream. <a target="_blank" href="https://twitter.com/fchollet/status/1529756619533430784" color="blue">x.com/fchollet/statu…</a>
People also ask, "didn't you say in 2023 that LLMs could not reason?" I have also never said this. I am on the record across many channels (Twitter, podcasts...) saying that "can LLMs reason?" was not a relevant question, just semantics, and that the more interesting question was, "could they adapt to novel tasks beyond what they had been trained on?" -- and that the answer was no. Also correct in retrospect, and a mainstream position today.
I have been consistently bullish on deep learning since 2013, back when deep learning was maybe a couple thousands of people. I have also been consistently bullish on scaling DL -- not as a way to achieve AGI, but as a way to create more useful models.
Back in 2017 my book had an entire chapter on generative AI, including language modeling and image generation. I wrote that content in 2016 and early 2017. This was some of the earliest textbook content that covered generative AI. All the way back in 2014 I was convinced that AI would one day become a major source of cultural content creation -- which was a completely outlandish position at the time.
