zlacker

>>wavela+(OP)
Interesting, both Karpathy and Sutskever are gone from OpenAI now. Looks like it is now the Sam Altman and Greg Brockman show.

I have to admit, of the four, Karpathy and Sutskever were the two I was most impressed with. I hope he goes on to do something great.

>>zoogen+Ix
Top 6 science guys are long gone. Open AI is run by marketing, business, software and productization people.

When the next wave of new deep learning innovations sweeps the world, Microsoft eats whats left of them. They make lots of money, but don't have future unless they replace what they lost.

>>nabla9+pH
I don’t feel that OpenAI has a huge moat against say Anthropic. And I don’t know OpenAI needs Microsoft nearly as much as Microsoft needs OpenAI

>>fnordp+SH
But is it even clear what is the next big leap after LLM? I have the feeling many tend to extrapolate the progress of AI from the last 2 years to the next 30 years but research doesn't always work like that (though improvements in computing power did).

>>cm2187+bN
Extrapolating 2 years might give you a wrong idea, but extrapolating the last year suggests making another leap that was GPT3 or GPT4 is much, much more difficult. The only considerable breakthrough I can think of is Google's huge context window which I hope will be the norm one day, but in terms of actual results they're not mind-blowing yet. We see little improvements everyday and for sure there will be some leaps, but I wouldn't count on a revolution.

>>benter+7R
Unlike AI in the past, there is now massive amounts of money going into AI. And the number things humans are still doing significantly better than AI is going down continously now.

If something like Q* is provided organically with GPT5 (which may have a different name), and allows proper planning, error correction and direct interaction with tools, that gaps is getting really close to 0.

>>trasht+E21
AI in the past (adjusted for 1980s) was pretty well funded. It's just that fundamental scientific discovery bears little relationship to the pallets of cash.

>>varjag+d41
Funding in the 1980s was sometimes very good. My company bought me an expensive Lisp Machine in 1982 and after that, even in “AI winters” it mostly seemed that money was available.

AI has a certain mystique that helps get money. In the 1980s I was on a DARPA neural network tools advisory panel, and I concurrently wrote a commercial product that included the 12 most common network architectures. That allowed me to step in when a project was failing (a bomb detector we developed for the FAA) that used a linear model, with mediocre results. It was a one day internal consult to provide software for a simple one hidden layer backprop model. During that time I was getting mediocre results using symbolic AI for NLP, but the one success provided runway internally in my company to keep going.

>>mark_l+vh1
That funding may have felt good at the time compared to some other academic fields.

But compared to the 100s of billions (possibly trillions, globally) that is currently being plowed into AI, that's peanuts.

I think the closest recent analogy to the current spending on AI, was the nuclear arms race during the cold war.

If China is able to field ASI before the US even have full AGI, nukes may not matter much.

>>trasht+Si1
You are right about funding levels, even taking inflation into account. Some of the infrastructure, like Connection Machines and Butterfly Machines seemed really expensive at the time though.

>>mark_l+lr1
They only seem expensive because they're not expected to generate a lot of value (or military/strategic benefit).

Compare that the 6+ trillions that were spent in the US alone on nuclear weapons, and then consider, what is of greater strategic importance: ASI or nukes?