I recently got a Tesla, and using full self-driving has been a wake up call to just how many advantages AI has over humans. The few times I disengaged it because I thought it was going into the wrong lane, it turned out that the car was right and I was wrong. I realized that there is no hope of me driving better than a neural net that knows every road, sees in every direction at once, and never…
Becoming an RL diehard in the past year and thinking about RL for most of my waking hours inadvertently taught me an important lesson about how to live my own life. One of the big concepts in RL is that you always want to be “on-policy”: instead of mimicking other people’s successful trajectories, you should take your own actions and learn from the reward given by the environment. Obviously…
Asymmetry of verification is the idea that some tasks are much easier to verify than to solve. With reinforcement learning (RL) that finally works in a general sense, asymmetry of verification is becoming one of the most important ideas in AI. Understanding asymmetry of verification through examples Asymmetry of verification is everywhere, if you look for it. Some prime examples: Sudoku and…
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 widely adopted benchmarks), it doesn’t matter if you’re incompetent at adjacent skills. For example, I have seen…
There is a dopamine cycle in doing AI research that is pretty interesting. Every day you wake up and you think about what experiment to run. You think thing X matters so you decide to improve it or ablate it. Then you write the code and pay some compute to find out the answer. Then you get dopamine or confusion depending on whether the result matched expectations. For experiments that are small…
Everybody uses evaluation benchmarks (“evals”), but I think they deserve more attention than they are currently getting. Evals are incentives for the research community, and breakthroughs are often closely linked to a huge performance jump on some eval. In fact, I’d argue that a key job of the team lead is to dictate what eval to optimize. What is the definition of a successful eval? I’d say that…
An open question these days is why large language models work so well. In this blog post I will discuss six basic intuitions about large language models. Many of them are inspired by manually examining data, which is an exercise that I’ve found helpful and would recommend. Language models are pre-trained to simply predict the next word in a corpus of text, and they learn a surprising amount from…
In early 2022 I realized that being a good researcher isn’t just about doing good research—it’s also about communicating ideas well and persuading others to engage with them. In other words, research is a highly social activity. In AI research, the social component largely revolves around Twitter, which distributes ideas in many different ways—people discuss research papers, learn about job…
This blog post doesn’t represent the positions of my employer (past, present, or future). I’ll review some common arguments that come up when discussing emergent abilities of large language models. Last year we wrote a position paper that defined emergent abilities as “abilities that are not present in small language models but are present in large language models.” I showed that emergent…
I’m not a particularly experienced researcher (despite my title being “Senior” Research Scientist), but I’ve worked with some talented collaborators and spent a fair amount of time thinking about how to do research, so I thought I might write about how I go about it. My perspective is this: doing research is a skill that can be learned through practice, much like sports or music. The way I…
Doing research that is enjoyable is critical to producing outstanding work. Research is a long-term endeavor (done over decades!), involving challenges, failures, and drama. It will be hard to sustain a career doing work that is not enjoyable. In this blog post I reflect on my work in the past few years, focusing in particular on how I feel about the papers I’ve written. I realized that I’m…
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