Dwarkesh Patel

It explained the last 20 years of gradual innovation and explained how each step made the RL learning process more stable or more sample efficient or more scalable.

I asked Deep Research to put all of this together like an Andrej Karpathy style tutorial.

And it did that.

What was cool is that it combined this whole lesson together into one coherent, cohesive document in the style that I wanted.

It was also great that it assembled all of the best links in the same place so that if I wanted to understand any specific algorithm better, I could just access the right explainer right there.

Go to gemini.google.com to try it out yourself.

All right, back to Richard.

I want to zoom out and ask about being in the field of AI for longer than almost anybody who is commentating on it or working in it now.

I'm just curious about what the biggest surprises have been, how much new stuff you feel like is coming out, or does it feel like people are just playing with old ideas?

Zooming out, you got into this even before deep learning was popular, so...

How do you see this trajectory of this field over time and how new ideas have come about and everything?

And what's been surprising?

Have there felt like whenever the public conception has been changed because some new technique was... Sorry, some new application was developed.

For example, when AlphaZero became this viral sensation, to you as somebody who has...

Literally came up with many of the techniques that were used.

Did it feel to you like new breakthroughs were made or does it feel like, oh, we've had these techniques since the 90s and people are simply combining them and applying them now?

Okay.

Some sort of left-field questions for you, if you'll tolerate them.

So the way I read the bitter lesson is that it's not saying necessarily that human artisanal researcher tuning doesn't work, but that...

it obviously scales much worse than compute, which is growing exponentially.