4 items tagged “francois-chollet”
2024
Reality is that LLMs are not AGI -- they're a big curve fit to a very large dataset. They work via memorization and interpolation. But that interpolative curve can be tremendously useful, if you want to automate a known task that's a match for its training data distribution.
Memorization works, as long as you don't need to adapt to novelty. You don't need intelligence to achieve usefulness across a set of known, fixed scenarios.
2023
Two things in AI may need regulation: reckless deployment of certain potentially harmful AI applications (same as any software really), and monopolistic behavior on the part of certain LLM providers. The technology itself doesn't need regulation anymore than databases or transistors. [...] Putting size/compute caps on deep learning models is akin to putting size caps on databases or transistor count caps on electronics. It's pointless and it won't age well.
If a LLM is like a database of millions of vector programs, then a prompt is like a search query in that database [...] this “program database” is continuous and interpolative — it’s not a discrete set of programs. This means that a slightly different prompt, like “Lyrically rephrase this text in the style of x” would still have pointed to a very similar location in program space, resulting in a program that would behave pretty closely but not quite identically. [...] Prompt engineering is the process of searching through program space to find the program that empirically seems to perform best on your target task.
2018
Without deep understanding of the basic tools needed to build and train new algorithms, he says, researchers creating AIs resort to hearsay, like medieval alchemists. "People gravitate around cargo-cult practices," relying on "folklore and magic spells," adds François Chollet, a computer scientist at Google in Mountain View, California.