I don't quite follow his point. Is it: a) that we need a new foundational algorithm that integrates a goal (one with "taste") directly into the training step, or b) that we need to point trained models towards goals as they iterate?

If it's a), he doesn't propose such an algorithm, and I don't know how you'd do it at such a low level because how do you quantify abstract goals? Did he suggest such an algorithm and I misread? If it's b), that already exists, see AlphaEvolve or any number of things he said. Or, to be a bit of a smart-ass, just type /goal and let it rip ...

I also think he's just categorically wrong that LLMs cannot do good and novel things. And if it can, then you could just say "well that's not novel, that's derivative". A simple example, if I make up a programming language with an LLM and it works well for my purposes, then is that not novel and good? I mean, is any language other than FORTRAN not novel?

Everything is derivative and you can put an LLM in a loop to evaluate LLMs trying things. I must be misunderstanding because he's too smart to be this wrong.

No, I think I he’s saying that we have that, and we should use it more.

AlphaGo uses discovery when it evaluates potential moves and iterates.

Claude Code uses discovery when it generates a script and the evaluates whether it works or not.

He’s saying we need to allow ai systems to do the evaluation and iteration themselves for science and engineering the same way we do for code.

Basically, harness engineering for engineering.

LLMs possess the map but are unable to discern fertile from barren ground. For instance: how does Anthropic's new model generate promising 'medications'? Because, beyond the knowledge embedded within the model, it has assimilated AlphaFold's reasoning paradigm. By itself, Claude would be incapable of engineering a protein analysis method

Idk one of his yt video presentations was saying we're entering a "designer" age of the universe

https://youtu.be/ThFq87Rp21s?si=SrKj72_X8bjnB6ED

Around 35min mark