Comment by dave1010uk
7 hours ago
1. Solve reinforcement learning.
2. solve unsupervised learning.
3. gradually tackle more complicated things.
> what was the "real reason" they couldn't achieve their original goals?
I assume this is referring to why they gave up being a non-profit. The answer is that they needed more money.
Huh, I guess ML people weren't aware of "divide and conquer" that has been successfully employed in software engineering since basically forever?
> I assume this is referring to why they gave up being a non-profit. The answer is that they needed more money.
Ugh, that was more boring than even I expected, thanks a lot for saving me the time though, seems avoiding watching the full thing was worth it.
Not that they wanted more money personally, but that they needed more money for compute.
"Financially, what will take me to $1B?" -Greg Brockman, August 2017
> The answer is that they needed more money.
isn't it still an odd choice for a nonprofit? it's hard to imagine a world without OpenAI and ChatGPT now, but at some point they decided being the best is most important. and presumably most profitable, since why just need a little more money?
Don't all nonprofits need more money to improve their sustainment?
Maybe, but somehow I doubt the American Heart Association is planning to open a chain of pork barbecue restaurants to support its mission against heart disease.
Trivial to imagine everyone switching to Anthropic or Google or on-device LLMs.