The Science of Intelligent Exploration: Why We Need Exploration in AI
richardcsuwandi.github.io·2h·
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LLMs are NOT Turing Complete (at train time), we need "train time recurrence"
fchaubard.github.io·1d·
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AGI Is an Engineering Problem
vincirufus.com·8h·
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Show HN: A short story on developing a long-context World-Model with no money
francesco215.github.io·1d·
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LLMs Are Biased Here's Why Enterprises Can't Afford to Just Plug and Pray
viveksgag.substack.com·1d·
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Walter
strangeloopcanon.com·8h·
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AI Is at the PageRank Moment
twkrr.com·18h·
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A Systematic Study of Post-Training Quantization for Diffusion LLMs
arxiv.org·3d·
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LLMs and the Russellian Inversion
philosophicalhacker.com·5h·
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V-JEPA 2: Scaling V-JEPA
gonzoml.substack.com·1d·
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Making Sense of AI Job Titles
dbreunig.com·2d·
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AI built from 1800s texts surprises creator by mentioning real 1834 London protests
arstechnica.com·1d·
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AI Agents: Why the Hype Feels Wrong to an Old Programmer
nimishg.substack.com·1d·
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Amazon is betting on agents to win the AI race
theverge.com·2d
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Building AI Products in the Probabilistic Era
giansegato.com·2d·
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The Cost of Winning:How RL Training on Poker Leads to Evil LLMs
tobysimonds.com·1d·
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How to model the world? Introduction to Laplace Neuron
abibulic.github.io·4d·
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💻operating systems
Facts, Arguments, Theses: Building AI Knowledge Retrieval on Meaning, Not Slices
nsavage.substack.com·21h·
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Using information theory to solve Mastermind
goranssongaspar.com·5h·
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Reproducing prospect theory with 'differentiable decision theories'
science.org·2d·
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