Google DeepMind has published a cognitive taxonomy for evaluating progress toward AGI and paired it with a Kaggle hackathon to build new benchmarks. The framework maps AI systems against human baselines across 10 cognitive abilities instead of relying on a single headline score.
Google DeepMind said on X on March 12, 2026 that a new podcast for AlphaGo’s tenth anniversary explores how methods first sharpened in games now feed into scientific discovery. The post lines up with DeepMind’s March 10 essay arguing that AlphaGo’s search, planning, and reinforcement ideas now influence work in biology, mathematics, weather, and algorithms.
Google DeepMind published a new framework for evaluating progress toward AGI on March 17, 2026. The proposal tries to shift the discussion from single benchmark scores toward a structured map of human-like cognitive capabilities.
Google DeepMind said on March 17, 2026 that it has published a new cognitive-science framework for evaluating progress toward AGI and launched a Kaggle hackathon to turn that framework into practical benchmarks. The proposal defines 10 cognitive abilities, recommends comparison against human baselines, and puts $200,000 behind community-built evaluations.
Google DeepMind said on X that it is launching a Kaggle hackathon with $200,000 in prizes to build new cognitive evaluations for AI. The linked Google post says the effort is part of a broader framework for measuring AGI progress across 10 cognitive abilities rather than a single benchmark.
Google DeepMind's Aletheia AI research agent solved 6 out of 10 open research-level math problems in the FirstProof Challenge as judged by expert mathematicians. The system also generated a fully autonomous research paper and solved 4 open conjectures from Bloom's Erdős database.
OpenAI CEO Sam Altman has set a new date for AGI, claiming that by the end of 2028, most of humanity's intellectual capacity could reside inside data centers rather than human minds.
DeepMind CEO Demis Hassabis proposed a concrete AGI benchmark: train an AI with a knowledge cutoff of 1911, then see if it can independently derive general relativity as Einstein did in 1915. This test targets genuine scientific discovery rather than pattern matching.
DeepMind CEO Demis Hassabis proposed a concrete test for true AGI: train an AI with a 1911 knowledge cutoff, then see if it can independently derive general relativity — as Einstein did in 1915.