Whenever people talk about how GPT-3 can’t do a lot of things that humans can do, I always think back to the “Bitter Lesson”. I don’t want to believe that general AI will just come from a stupid amount of compute, but it might.
Ever since I had my first encounters with AI, somewhere in the early 90s, one thing has remained rather constant: the extraordinary successes of AI originate mostly from when it is used for tasks we humans perform naturally poorly at, and when confined to a narrow applications.
Most of the often touted amazing general purpose AI is largely just a lot of smoke and mirrors. The main reason for that is another thing that also hardly changed since the early 90s: a lot of profit is made from convincing (/fooling) investors into believing that AI is far more powerful/useful than it actually is.
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Whenever people talk about how GPT-3 can’t do a lot of things that humans can do, I always think back to the “Bitter Lesson”. I don’t want to believe that general AI will just come from a stupid amount of compute, but it might.
I highly doubt that.
Ever since I had my first encounters with AI, somewhere in the early 90s, one thing has remained rather constant: the extraordinary successes of AI originate mostly from when it is used for tasks we humans perform naturally poorly at, and when confined to a narrow applications.
Most of the often touted amazing general purpose AI is largely just a lot of smoke and mirrors. The main reason for that is another thing that also hardly changed since the early 90s: a lot of profit is made from convincing (/fooling) investors into believing that AI is far more powerful/useful than it actually is.