<p>**Abstract:** This paper introduces a novel framework for the quantitative assessment and prediction of viewer engagement within YouTube content focusing on ...
freederia.com·2h
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Watts and bots: the energy implications of AI adoption
iopscience.iop.org·2d
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New software toolbox enables brain-like models to learn directly from data
phys.org·8h
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The Future Of Digital Engineering In The Age Of AI
semiengineering.com·20h
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Why Language Models Are “Lost in the Middle”
pub.towardsai.net·3d
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Google’s SIMA 2 agent uses Gemini to reason and act in virtual world
techcrunch.com·12h
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Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning
arxiv.org·23h
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Inside LinkedIn’s generative AI cookbook: How it scaled people search to 1.3 billion users
venturebeat.com·12h
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Evaluation Avoidance: How Humans and AIs Hack Reward by Disabling Evaluation Instead of Gaming Metrics
lesswrong.com·3h
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RESTL: Reinforcement Learning Guided by Multi-Aspect Rewards for Signal Temporal Logic Transformation
arxiv.org·1d
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When Light Became the Brain: How Scientists Just Cracked AI’s Biggest Energy Problem
pub.towardsai.net·11h
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Are AI time horizons inherently superexponential?
lesswrong.com·1d
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Plug-and-Play Clarifier: A Zero-Shot Multimodal Framework for Egocentric Intent Disambiguation
arxiv.org·23h
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Bayesian Mixture of Experts For Large Language Models
arxiv.org·23h
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