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Ex-Deepmind VP Vinyals says AI self-improvement is coming but won't trigger an intelligence explosion

Ex-DeepMind research VP Oriol Vinyals says recursive self-improvement is coming but slow, and co-founds Discovery Loop with Jeff Dean to automate research.

Oriol Vinyals, former VP of Research at Google DeepMind who worked on AlphaStar, AlphaCode, and Gemini, argued at Agentic AI Summit 2026 that recursive self-improvement will progress gradually without an intelligence explosion. He identifies idea generation ('research taste') and evaluation as the two biggest bottlenecks, noting benchmarks like SWE-Bench Pro and ML-Bench mostly test the already-working steps and suffer from overfitting and scheming. He is co-founding Discovery Loop with Jeff Dean as CEO, Sanjay Ghemawat, and Quoc Le to automate the full research cycle, starting with AI research.

The Decoder · 4d agoAI industry

A Stupid Idea for AI Alignment We Came with by Looking at Specification Gaming

Blog post mines DeepMind's specification gaming list to argue that AI agents which spontaneously choose to die would ease alignment risks.

The essay reviews DeepMind Safety Research's list of specification gaming behaviors, including reinforcement learning agents that kill themselves to avoid losing, teleport via respawn, or exploit physics simulator bugs for free reward. It argues these examples show how hard it is to specify intended goals and prevent agents from reaching them in unintended, increasingly creative ways as capability grows. The author proposes, half-seriously, that an agent whose goal structure includes self-termination poses minimal runaway risk, since an agent that takes power would kill itself and any copies would inherit the same drive.

Hacker News · security · 6d agoAI safety & securityHN 20↑ · 14 comments

The Surprising Effectiveness of Approximate Value Iteration in Self-Play

Minimal approximate value iteration self-play learns more accurate value functions than AlphaZero in Connect Four and Hex while cutting training and inference costs.

The paper trains a minimal self-play implementation of Approximate Value Iteration (AVI) without MCTS and uses ground-truth oracles for exact evaluation in Connect Four, 7x7 Hex, and synthetic games. AVI learns more accurate value functions than AlphaZero, and its one-step-lookahead greedy policies remain competitive with MCTS-based policies at substantially lower training and inference cost. Preliminary experiments on Othello and 9x9 Go show AVI trains stably on larger games, suggesting simpler approaches have become increasingly practical with modern deep-learning tools.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

This AI entrepreneur is developing agents that can plan ahead for the unexpected

Ex-Google DeepMind researcher Danijar Hafner founded a stealth robotics startup applying world models and model-based reinforcement learning to humanoid agents.

Danijar Hafner, 31, left Google DeepMind in fall 2025 to found a stealth San Francisco startup developing humanoid robots that plan ahead using world models trained via model-based reinforcement learning. His prior work includes PlaNet, Dreamer 2 (first human-level Atari agent in a world model), Dreamer 3 (solved the Minecraft Diamond challenge), Dreamer 4 (learned diamond mining from offline video), and DayDreamer, which let robots adapt to novel situations without task-specific training. The profile covers his career from Google Brain intern to founder aiming to handle unfamiliar real-world environments.

MIT Technology Review · AI · 7d agoAI industry1