Forever would be good for me, even if it's eventually just rotating around in space.

Thank you!

I have a confession to make. After 6 years, I stopped teaching belief propagation (but still cover graphical models). It felt like tedious bookkeeping around orderings of sums and notation. Have I strayed?

I don't think those things seem boring. But most research directions honestly are quite boring, because they are geared towards people pleasing --- going with the herd, seeking approval from others, and taking no risks. It's a great way to avoid making a contribution that changes any minds.

While scaling laws typically predict the final loss, we show that good scaling rules enable accurate predictions of entire loss curves of larger models from smaller ones! Shikai Qiu did an amazing job leading the paper, in collaboration with L. Xiao, J. Pennington, A. Agarwala. 3/3

In particular, scaling collapse allows us to transfer insights from experiments conducted at a very small scale to much larger models! Much more in the paper, including supercollapse: collapse between curves less than the noise floor of per-model loss curves across seeds. 2/3

I love havarti!

It sometimes feels like papers are written this way. <Make claim that may or may not be true but aligns with the paper's narrative> <find arbitrary reference that supposedly supports that claim, but may be making a different point entirely>. I guess grammarly is giving the people what they want?

What's irrational is the idea that some group of authors writing a paper about something foundational also should be the team of people to put it into production in the real world and demonstrate its impact, all in one paper. That happens over years, and involves different interests and skills.