It’s easy to say that we should throw AI at a problem and in a few years it will solve it, but most of the time it doesn’t actually work that way. If you think about the Turing Test itself, where the history goes back to the 1950s, how many decades did it take for us to get to anything that could reasonably come close to passing it? So anytime you think to yourself that one of these days AI is going to get there, remember that one of these days might actually be a half century from now.
The other aspect to this challenge, or rather specifically with regards to this challenge, is that the setup involves humans organizing code in a certain way according to some kind of reasoning that the authors know about, and then that being compiled away, and then another computer program trying to get back what the original authors might have been thinking when they designed the thing originally. That’s a steep hill to climb. Can it be done on a small scale? It certainly can. On a large scale? Don’t hold your breath.
FaceDeer@fedia.io 5 months ago
There's a lot of outright rejection of the possibilities of AI these days, I think because it's turning out to be so capable. People are getting frightened of it and so jump to denial as a coping mechanism.
I recalled reading about an LLM that had been developed just a couple of weeks ago for translating source code into intermediate representations (a step along the way to full compilation) and when I went hunting for a reference to refresh my memory I found this article from March about exactly what's being discussed here - an LLM that translates assembly language into high-level source code. Looks like this one's just a proof of concept rather than something highly practical, but prove the concept it does.
I wonder if there are research teams out there sitting on more advanced models right now, fretting about how big a bombshell it'll be when this gets out.