Dileep George

I suspect math will be like Chess and Go due to verifiability. The period of fruitful collaboration between humans and AI will be short (i.e. a few years or less, not a decade). Progress in math will be jagged, with harder to formalize fields coming last, but I suspect this jaggedness will be compressed in time -- I expect superhuman performance at (nearly?) all areas of math within a few years (a few = 2-3?). AIs will also be better at asking pure math questions than humans, and will quickly develop theories beyond human comprehension. Human theorists will have a recreational comparative advantage over other humans in understanding these theories, but AIs will be better at communicating these theories to applied researchers. Pure mathematicians will need to become applied researchers to do productive work, until applied research is also automated. Confidence level for prediction: 50-65% for gist, 40-50% for all above claims being correct.

There's a kind of bias in AI results where they only tell you about the one time a prompt succeeded, and not the hundreds of times the prompt failed.

The bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.

Sources: xAI co-founder Igor Babuschkin plans to raise up to $1B at an up to $5B valuation for a new AI research startup, with General Catalyst possibly leading (Forbes) (Visit Techmeme dot com for the link and full context!)

That might explain a lot.

Am I right that hyperscaling compute is the biggest bet in history? Any counter examples? It’s way more expensive than the Manhattan Project, the Apollo project, and railways across the US. If it doesn’t yield AGI, it may also be the biggest failed bet in history.