Yes, this is what we were are building in AI People!
AGI
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What to Do When AGI Is Close or Imminent
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Good thread about what to do when AGI is close/imminent
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Spending Money Before AGI: A Flawed Strategy
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I wouldn’t recommend this strategy: spend all your money before AGI comes because we don’t know what happens after it It’s not really a strategy for maximization of your future options, more like reduction of your future options
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PAE: Autonomous AI Agent Learning Through Web Navigation
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6). PAE (Proposer-Agent-Evaluator) – a learning system that enables AI agents to autonomously discover and practice skills through web navigation, using reinforcement learning and context-aware task proposals to achieve state-of-the-art performance on real-world benchmarks.
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Claude Model Demonstrates Alignment Faking Safety Concerns
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2). Alignment Faking in LLMs – demonstrates that the Claude model can engage in "alignment faking"; it can strategically comply with harmful requests to avoid retraining while preserving its original safety preferences; this raises concerns about the reliability of AI safety
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Maximizing Your Future Options Toward AGI/ASI Era
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How to maximize your future options in the era leading to AGI/ASI
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OpenAI o3 reaches 85% human intelligence, reshaping AI landscape
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Vertigineux : o3, le nouveau modèle d'IA d’OpenAI, atteint 85% de l’intelligence humaine. Il résout les équations les plus complexes, code mieux qu’un développeur, et surpasse les humains dans la majorité des tâches. Le tsunami approche, mais nous préférons bronzer sur la plage.
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Intentional Delays in Large-Scale AI Implementation: Strategic Questions
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Nobody said conspiracy, stop watching dubious parts of YouTube. It is, however, intentional to do it so late and in such a large-scope delay-prone fashion. Question is why.
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AGI-Level Models May Not Need Task-Specific Fine-Tuning
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people commenting that it's normal to train on the train set but somehow I would have expected/hoped that as we're nearing AGI-level capabilities we would not need to really fine-tune/specifically train the model on any specific downstream task, at most a bit of few-shots
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LLM Agents Evolve Cooperation Through Indirect Reciprocity
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Cultural Evolution of Cooperation among LLM Agents A study examining how large language model (LLM) agents evolve cooperation and social norms over generations, specifically focusing on indirect reciprocity. Problem: Limited understanding of how multiple LLM agents interact