Ian Waring

David Silver is one of the all-time greats. He was the creator of AlphaGo, which changed the course of history by showing that neural nets could achieve superhuman performance for the first time. He also has a unique intuition and knack for forecasting when things will happen in AI. With computer Go, he predicted the Lee Sodol outcome 10 years before, right to the year. He got the year right for Starcraft and won a bet inside Deepmind. Same for AlphaProof. Same for the IMO. David's prediction for superintelligence: 3 years. !!! He has a strong sense that now is the time to build a superleaner, and that superlearners are now possible, thanks to the enormous progress in compute, simulators, autoresearch, and the sophistication of technology in the stack since the AlphaGo days. What is a “super learner”? A super learner discovers all knowledge from its own experience. Start from nothing (no internet pre-training), and go beyond human knowledge. Drop it into any stream of experience, simulated or physical, and the superlearner learns. He expects the progress will be rapid. When you get the recipe for a superlearner just right and remove humans from the development loop altogether, a superlearner can learn extremely quickly. Just look at AlphaGo. AlphaGo took two years to develop with humans in the loop. When David and his team at @GoogleDeepMind got the formula for AlphaZero correctly, it surpassed the human-developed algorithms in just a few hours. Human data is a shortcut, but you have to let go of the human data to shoot beyond it. In January, David left GDM to found @IneffableLabs, a research-led lab with one mission: build a "super learner" that discovers all knowledge from its own experience, and make first contact with superintelligence. He joins us with co-founder Lasse Espeholt, who built IMPALA and SEED RL, the foundations of scaled-up RL, and now runs Ineffable's compute. A few things we cover in this conversation. — What a super learner is: depth (start from nothing, go beyond human knowledge) and breadth (drop it into any stream of experience, simulated or physical). The human brain is the only existence proof — Why they're giving up human data: AlphaGo took two years with humans in the loop. AlphaZero outperformed it in a few hours. "You have to let go of the human data to shoot past the competition" — Crucibles: simulated worlds with enough emergent complexity (physics engines, millions of interacting agents) for an agent to rediscover technology, science, maths and language on its own — Simple rewards, emergent complexity: evolution optimized fitness and got all of life. Don't breadcrumb the objective, give the agent something simple to optimize in a world with a lot to learn about — Go as the existence proof: one agent is the most boring game imaginable, two agents is "an infinite onion of layers of complexity" — Why human language is probably not the endpoint for reasoning, and how training on it bakes in a flat earth and the four humors — Can superintelligence be built in simulation? If the sim is Turing complete, computation, economics, language and maths are all expressible, and it happens 10-100x faster and safer — Alignment: language-based monitoring is reaching its limits as agents learn to say one thing and do another. Shadow actions, superintelligences monitoring superintelligences, and building a real science of how they interact peacefully — Accelerate in simulation, pace in the real world: Ineffable will gate real-world deployment on evidence that it goes well — Timing as the determinant: Silver called Computer Go to the year, then StarCraft, then the IMO. He thinks this is the moment — Predictions: True superintelligence within three years. Timestamps: 0:00 – Introduction 1:09 – What held true from Go to today, and what the LLM era changed 3:35 – Why leave DeepMind to start a new lab 5:58 – Why "Ineffable" (and the secret second reason) 7:03 – Lasse's path: scaling RL at DeepMind and Google Brain 9:31 – Defining a super learner and first contact 12:35 – Super learners vs. LLMs that make discoveries 13:59 – Day to day: the RL stack, crucibles and human-journey milestones 16:13 – Don't breadcrumb the objective: simple rewards, emergent complexity 21:16 – What a crucible looks like 24:14 – Go: one agent is boring, two is infinite 25:41 – Will agents rediscover language? 27:22 – Removing human priors: AlphaGo to AlphaZero 32:05 – Keeping human bias out of the crucibles 33:35 – Confidence, timing and super forecasting 36:24 – Is compute the driver? 38:13 – The ceiling on simulation and Rich Sutton's objection 43:44 – Alignment and the science of superintelligence 47:45 – Monitoring agents that don't think in English 51:01 – Pacing the frontier 52:54 – The compute setup: Vera Rubin on GCP 55:18 – Training, inference, simulation and RL scaling laws 57:40 – IMPALA and SEED RL 1:00:58 – 20 people and the compute-per-person ratio 1:04:49 – A research-led culture 1:05:47 – Predictions

Databricks' @alighodsi on why most companies wouldn't even notice if frontier AI models stopped advancing: "The models are smart enough, but they just don't have the context that exists inside any organization. They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "There's always a couple of employees who know everything in every organization. You go tap on their shoulder, and everybody's like, 'Oh my God, what would happen if he or she quits?' [The models] don't have that context." "If you just fused that and gave that context to the AI models... I think there are so many productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models." "We don't need a smarter model that can solve Navier-Stokes or conjectures or do better on Humanity's Last Exam." "If the frontier doesn't advance, it doesn't actually matter, I think, for the vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." @alighodsi