AGI roadmaps, cognitive frameworks, and limits of current learning

This cluster explores how close today’s AI systems are to genuine learning and cognition, and how we might rigorously track progress toward AGI. DeepMind proposes a cognitive framework and public benchmarks to standardize AGI evaluation, while other work argues current systems still lack autonomous, human-like learning despite impressive performance. Complementary research examines whether large language models truly capture meaning, whether AI can internalize nuanced concepts like “scientific taste,” and how brain-style computation differs from processors as Moore’s law slows. Together, these pieces probe both the theoretical foundations and hardware constraints shaping the future trajectory of general intelligence.