And from my perspective, if some math folks typing in a few questions to OpenAI provides sufficient training data for OpenAI to solve a big problem... that's amazing! A few conversations/prompts out of the billions that OpenAI trains on lead to this result- that means there is an awful lot of low-hanging fruit that could be exploited cheaply.
In the transcripts, Brubeck is very cagey and evasive about the prompt, when it was supplied, and its contents.
Either they had a pretty good idea that investing this type of money in that compute on a model in training would lead to these specific results, or they gambled with other people's money.
I want to hear about the gambles and expenditures they don't brag about. In America's energy economy, there's finite resources to expend.
> learning the answer might be in model X’s training data made them believe that model X specifically might be able to solve the question, and they were able to very quickly find enough certainty about the former to commit millions of dollars to the latter.
They don’t need to know, because their IP stealing machine knows for them. They just have to buy enough compute, and someone else’s work is theirs.
(Naturally, I have far too much respect for OpenAI’s legal team to suggest this is what happened in their project.)