3 ms·
You're making a different argument to what you started the thread with. > Having worked in the space on-and-off since my PhD, the pace is not "overwhelming". Y
by adamsmith143 4y ago
You're making a different argument to what you started the thread with.
> Having worked in the space on-and-off since my PhD, the pace is not "overwhelming". You just started paying attention to it.
>ChatGPT and Stable Diffusion are good, but they're incremental gains over what came before, which were incremental gains over what came before that
>The AlphaFold example is apt: yes, it outperformed methods at CASP. It was indeed a dramatic leap
>Are these large transformers a dramatic improvement? Absolutely. No question.
>Has this been an evolutionary change over the last decade or so? Not a smooth curve, but yes. The world didn't suddenly change overnight.
Starting with AlexNet there have been numerous discontinuous improvements in CV, NLP, Game Playing, etc. It's just not true to say otherwise.
- timr 4y ago> You're making a different argument to what you started the thread with. No, I'm not. The field has been making gradual improvement for a long time, with transformers (ca 2017) being a notable jump forward. Research is always a form of punctuated equilibrium. > Starting with AlexNet there have been numerous discontinuous improvements in CV, NLP, Game Playing, etc. It's just not true to say otherwise. Nor did I. I said that these models have been advancing gradually for a long time. The fact that particular steps can be characterized as "jumps" when viewed in isolation is not a rebuttal.