From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
Paper: https://
arxiv.org/pdf/2505.17117
v1.pdf
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AGI
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LLMs and Humans Trade Compression for Meaning Analysis
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Herding Algorithms and Human Intelligence in AI Systems
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One more argument: We could easily replace the sampling of the multinomial by a dynamical system, eg by herding https://
icml.cc/Conferences/20
09/papers/447.pdf
… I’m not sure randomness is needed. More on the opinion side of things: I don’t think humans are very far from being parrots. We may yet -
Language as Tool for Communication and Self-Reflection
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Digesting these arguments with great interest. Thanks everyone. Here are some quick sketchy reflections to expand our debate: 1. Language is a tool for communication but it’s more than that. We turned the tool on ourselves to self reflect and think. Of course we still need
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Large-scale coordination problems in AI governance and society
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Any problem that requires large scale coordination of many human individuals and/or institutions is pretty much toast.
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AGI Timeline Implications on Problem-Solving Strategy
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TBF, if you *really* think that AGI is just around the corner, then kicking the can down the road is probably the optimal strategy for 99.99% of the problems we are facing.
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Consciousness as Pattern: Implications for AGI Development
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I think that consciousness is not an object or individuated process but a pattern. Our experience differs in its contents but the experience of experiencing itself is the same
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AlphaEvolve AI Seen as Closest System to AGI
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Une de mes meilleures et plus importantes vidéo YouTube à mon Avis. Alpha Evolve, pour moi une IA qui va absolument tout changer. C'est la chose la plus proche que j'ai vu de ce que l'on appelle l'AGI : https://
youtu.be/wVN0glSVheM?si
=6YXF4TJDBkzcrCnk
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ARC-AGI-2: New Benchmark Pushes AI Reasoning Boundaries
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9. ARC-AGI-2 ARC-AGI-2 is a new benchmark designed to push the boundaries of AI reasoning beyond the original ARC-AGI.
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LLM Reasoning in Dynamic Environments Beyond Static Benchmarks
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6. Towards a Deeper Understanding of Reasoning in LLMs This paper investigates whether LLMs can adapt and reason in dynamic environments, moving beyond static benchmarks.
