AlphaGo

Headlines · 6

1. 12h
   A Framework for Human-AI Coexistence in the “Dual Intelligence” Era

   A Tencent Research Institute essay republished by 36Kr argues that humanity is entering a dual-intelligence era in which human and AI cognition coexist as distinct but interacting systems. It cites research on AI-assisted Go play and examples of AI-assisted scientific discovery to examine how AI may expand human problem-solving while introducing new governance questions. The essay proposes “human-centered intelligence,” a coexistence framework preserving human rights to set goals, assess developments, and stop or correct AI systems.

   36氪 人工智能 · 🔥 7
2. 3d
   Reinforcement Learning Map: Model-Based vs. Model-Free

   A beginner-oriented tutorial organizes reinforcement-learning methods by whether they use an environment model. Using a corridor-robot example, it distinguishes planning with a known or learned model from learning directly from experience. The article introduces dynamic programming, model predictive control, Monte Carlo tree search, learned models, value-based methods, policy-based methods, and actor-critic methods. It also clarifies that “model” here means environment dynamics and rewards, rather than a large language model or the use of a neural network.

   掘金 · 人工智能 · 🔥 0
3. 5d
   Sim-to-Real Gaps May Help Explain Uneven AI Progress

   An analysis argues that the sim-to-real gap may help explain why AI has made rapid progress in programming and mathematics while its real-world impact in many professions remains modest. Programming and mathematical reasoning can be replicated and verified in digital environments, whereas many white-collar professions involve people and physical environments that are difficult to simulate. The analysis uses robotics examples to illustrate the challenge. It also cites GDPval, where the best score reportedly rose from 47.6% for Claude Opus 4.1 when the benchmark was released to more than 85% for current leading models.

   Understanding AI · 🔥 0
4. Oct 20
   Ex-DeepMind researcher launches London reasoning lab Metis Reasoning

   Thore Graepel, who helped build Google DeepMind’s AlphaGo, has launched Metis Reasoning in London. The lab says its approach combines explicit knowledge, evidence-based judgments and AlphaGo-inspired search, with the goal of making reasoning more auditable than standard LLM outputs. Its white paper “The Age of Reasoning” presents no test results or released model, and co-founder Piers Ingram said only that early results on frontier reasoning problems were “encouraging.”

   The Decoder · 🔥 0
5. Oct 12
   Ex-DeepMind scientist David Silver's startup Ineffable bets on RL 'super learner'

   In a Financial Times interview with Madhumita Murgia, former Google DeepMind scientist and AlphaGo architect David Silver discussed his startup Ineffable. According to the report, Ineffable is pursuing an AI "super learner" built on reinforcement learning as an alternative path to large language models. Silver also spoke about building machines that can teach themselves, and said he feels a moral obligation to advance the technology.

   Techmeme · 🔥 0
6. Oct 10
   Yuandong Tian: co-writing a paper with GPT-5 made me fear job loss within 5 years

   Yuandong Tian, a former Meta FAIR researcher and co-founder of Recursive Superintelligence, said on The Information Bottleneck podcast that he treated his final Meta paper — on the neural-network grokking phenomenon — as a collaboration with GPT-5, using the model to prove and check theorems and brainstorm, and estimates the workflow was roughly 6–10x faster. That experience, he said, led him to conclude his own job could be replaced within about five years, and to start a company instead: "rather than passively lose my job, I'd rather start a company heading in that direction." He was also skeptical of full autonomy, saying models asked to generate ideas on their own produce mediocre ones and that humans still frame the questions and validate results. He argued that restricting recursive self-improvement is hard because the boundary is blurry, and said Recursive will open-source its research. Claims such as the company's reported $4.65 billion valuation and over $650 million raise, and OpenAI's unreleased internal model producing 722 math manuscripts, come from the article and interview accounts.

   36氪 人工智能 · 🔥 0

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