New blog post: on the million-x sample efficiency gap between AIs and humans, and whether it matters:

"The reason it is relatively easy for open source and previous laggards to catch up to within months of the frontier is that data is the real driver of progress.

And data can be easily distilled from public APIs, whereas hyper-parameters and training tricks and architectural micro-optimizations cannot - if the latter were driving most of progress, then catching up would be harder than we are observing it to be.

It is easy to forget how much data these models are trained on, and how much more it is than what we humans see in our lifetimes.

We see these AIs as a galaxy glittering with capabilities, but at their center, invisible to the naked eye, holding all the constellations together, is an unimaginably massive black hole of data."

Post in link below

New blog post: on the million-x sample efficiency gap between AIs and humans, and whether it matters:

"The reason it is relatively easy for open source and previous laggards to catch up to within months of the frontier is that data is the real driver of progress.

And data can be easily distilled from public APIs, whereas hyper-parameters and training tricks and architectural micro-optimizations cannot - if the latter were driving most of progress, then catching up would be harder than we are observing it to be.

It is easy to forget how much data these models are trained on, and how much more it is than what we humans see in our lifetimes.

We see these AIs as a galaxy glittering with capabilities, but at their center, invisible to the naked eye, holding all the constellations together, is an unimaginably massive black hole of data."

Post in link below

I feel like the obsession with continual learning / sample efficiency leads the field in the wrong direction. It's the bad career strategy of focusing on addressing your weaknesses instead of maximizing your strengths.

Yes, there is an existence proof in the human brain, but it doesn't by any means guarantee that that'll be the most interesting AI. It may require $100T of R&D on chips and AI methods to get that unlock.

On the other side of things, it's obvious that the coming models are extremely transformative and built on technologies that we already have. There's great reason to focus on just maximizing this. In reality, this is what the frontier labs are doing. They're going as fast as possible down the current development tree. This is good for progress and mixed for safety/geopolitics.

Things like "automate white color work" and "replace the AI researcher job" are the guesses of labs because it's super hard to imagine futures for what these dramatic technologies will be. Don't take the labs too seriously about this being the exact goal. The exact goal is to push the frontier and monetize later.

Solving continual learning, sample efficiency, etc would be great, but its trying to predict when a scientific breakthrough will come instead of trying to grapple with how the 100% sure thing coming technological revolution will change our lives.

This isn't to say the Dwarkesh post is bad, it addresses some reasonable critiques, but it is the least bitter lesson pilled thing to be obsessed with human intelligence and how that can inform AI.

We are in the AGI era of research. This is about embracing the unknown, scaling resources, and seeing what is enabled by making a series of magical tweaks to complex recipes that build frontier models. Lean into the alchemy.

(it should be pretty clear that I personally, investing in open research agree we need fundamental science -- just not agreeing that this is what the "cutting edge of the frontier" is governed by)