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  "text": "🚨 DeepMind AGI/ASI paper: a Field Lost in Hype and Metaphors\n\nSolid scaling. Useful pathways. Overdue bottlenecks.\n\nBut it keeps treating AI like a subject with a mind. It’s not.\n\nWhy serious researchers treat massive compressed files running matrix multiplication as social “agents” with psychology, beliefs & motives is beyond me.\n\nLLMs don’t think. They compute.\n\n“Step-by-step thinking”? Repeating training corpus patterns that look like problem-solving.\n\n“Understanding”? Pattern matching. Not comprehension.\n\nThe 4 paths to ASI are real but lead to better pattern matching, efficient data compression, not actual intelligence.\n\nFix the language: \n• Intelligence → data coverage \n• Reasoning → structured sampling \n• Goals → constraints \n• Creativity → interpolation\n\nHumanised AI narratives make us overestimate what these systems can do, underestimate failures & build things that look smart but aren’t safe or reliable.\n\nPaper’s a great start. Describe AI as it actually is: a sophisticated pattern engine, not as we wish it were.\n\nAnthropomorphism in analysis wastes resources and confuses policymakers with false narratives.\n\nWe have a long way to go to overcome hype and misdirected incentives.",
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