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Discriminator-generator gap seems to be the most important idea in AI for scientific innovation. With compute + clever search, anything that we can measure will be optimized. First up will be environments that can be verified quickly, with continuous reward, and at scale.

The AI milestone that would touch my heart the most is not solving International Math Olympiad or even breakthrough scientific discovery, but if AI could animate another season of Avatar the Last Airbender. Only show that has made me cry, would do anything to watch another season

The most impressive thing that Tesla FSD can do that I’ve never seen any human is that it stays calm and doesn’t get upset after my mom criticizes its driving for the whole trip

A recent clarity that I gained is viewing AI research as a “max-performance domain”, which means that you can be world-class by being very good at only one part of your job. As long as you can create seminal impact (e.g., train the best model, start a new paradigm, or create

The 80-20 rule happens often in AI research, where you get 80% of the payoff from the first 20% of the effort. But there is often also an inverse rule, where it’s actually the final 20% of that yields 80% of the payout. Some examples: 1. When your eval is already good in many

Check out some new roles at OpenAI for AI safety research!

Was attending a talk in a big lecture hall and the guy in front of me had the craziest conversation with ChatGPT for the whole hour about how to get his girlfriend back. Dozens of messages of pasting screenshots of text conversations to analyze tone of responses; whether to

It’s actually a good thing these days to have subtle grammar errors in your writing. It sprinkles on a clear human touch. You never want your reader questioning if what they’re reading was written or edited by chatgtp

RL environment specs are among the most consequential things we can write as AI researchers. A relatively short spec (e.g., <1000 words of instructions saying what problems to create and how to grade them) often gets expanded either by humans or via synthetic methods into

One way of thinking about what AI will automate first is via the “description-execution gap”: how much harder is it to describe the task than to actually do it? Tasks with large description-execution gaps will be ripe for automation because it’s easy to create training data and

My favorite thing an old OpenAI buddy of mine told me is, whenever he hears that someone is a “great AI researcher”, he just directly spends 5 minutes looking at that person‘s PRs and wandb runs. People can do all kinds of politics and optical shenanigans, but at the end of the

AI research is strange in that you spend a massive amount of compute on experiments to learn simple ideas that can be expressed in just a few sentences. Literally things like “training on A generalizes if you add B”, “X is a good way to design rewards”, or “the fact that method M

The greatest contribution of human language is bootstrapping language model training

I would say that we are undoubtedly at AGI when AI can create a real, living unicorn. And no I don’t mean a $1B company you nerds, I mean a literal pink horse with a spiral horn. A paragon of scientific advancement in genetic engineering and cell programming. The stuff of

The most rewarding thing about working in the office on nights and weekends is not the actual work you get done, but the spontaneous conversations with other people who are always working. They’re the people who tend to do big things and will become your most successful friends

We don’t have AI self-improves yet, and when we do it will be a game-changer. With more wisdom now compared to the GPT-4 days, it's obvious that it will not be a “fast takeoff”, but rather extremely gradual across many years, probably a decade. The first thing to know is that

Normal people dating advice: Don’t marry early if you’re growing and changing a lot every year AI buddy (Yi Tay): You are like a neural net in the middle of training and loss is still improving. Better to train to convergence instead of taking an early checkpoint snapshot