Toutes les curations

Making novel variations of evals is very useful, public evals tend to get memorized!

I see some are drawing the wrong inference from this study: they are focusing on this meta benchmark as yet another benchmark for model picking, i.e. open vs closed models, model families etc. That’s too narrow of an interpretation! You need to do your own private evals on

holy shit, an art form of scientific grift next METR/Epoch/AA employee right here you all need to work on your epistemic hygiene

GPT-5.6 Sol and Luna were built by roughly the same people using roughly the same infra, I don't think we can blame the 20 point difference in Luna just on memorization. There's something else being tracked here in addition to memorization. Maybe MaxxBench questions are slightly

It isn't that weird to think that "this time is different" - we had a couple of Industrial Revolutions over the past couple centuries, just because we got used to a relatively steady-state regime post World War Two with a regular pace of growth doesn't mean it continues forever.

Pre-announcing the pre-announcement

1000 scientific breakthroughs drop from Google incoming?

Been working for this moment for over a decade. What I've witnessed behind the scenes, together with what we have been building, tells me we are about to enter a truly incredible era of AI-powered scientific discovery. It's an incredible time to be doing research. I will be

2026 is the turning point for humanity. Google DeepMind’s Pushmeet Kohli says AI is accelerating scientific discovery and that upcoming releases will offer a glimpse of the progress. He says the team also aims to help the wider scientific community use these tools responsibly.

it’s all over. the finances will never work. absolute vindication for me, of course.

That's actually incredible Vastly better than LLM scaling laws

I’ve been actively on the hunt for non LLM based AI models and I’ve invested into one already Looking for totally new designs

i disagree with the first point, there is nothing that says that scaling up current transformer recipe is not enough for "intelligence". you take kimi K3 or your favorite open arch + some stability fixes, you scale it to 50T parameters, you do classical training end to end, you

We need a model that learns *in spite* of its inductive bias, not thanks to it. We have to stop being cross-entropy fetishists.

True! This is the era of the RL objective. The biggest economy on this earth (US stock market) runs on subscriptions. Subscriptions require vendor lock-in. Currently, the frontier is pushing “Variance” learning from customer data, like how Google won the search market. But I

Could Jensen’s judgement in this have been any worse?

concerning