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Reinforcement Learning and Agents
Reinforcement Learning and Agents in Machine Learning: the news, the names and what changed.
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Reinforcement Learning, Part 14: Imitation Learning — When There’s No Reward Signal at All
20+ hour, 46+ min ago (595+ words) Every part of this series so far assumed some reward signal exists — handed directly by the environment in Parts 1 through 5 and 8 through…Continue reading on Medium » Reinforcement Learning, Part 14: Imitation Learning — When There’s No Reward Signal at All Every part…...
Quantum neural operator learns PDEs with quadratic expressivity edge
1+ day, 12+ hour ago (536+ words) Scientists at Shanghai Jiao Tong University have unveiled a quantum neural operator that promises to squeeze genuine machine-learning power out of today’s noisy, error-prone quantum processors....
What changes when an agent starts building the slides?
1+ day, 22+ hour ago (402+ words) When we started out in consulting, a large part of an associate’s job happened inside PowerPoint. You learned the shortcuts, borrowed pages from old decks and spent far too long moving boxes by a few pixels because the slide had…...
When AI Agents Started Working Together
2+ day, 4+ hour ago (1722+ words) Guest Post by Willis Eschenbach (@WEschenbach on X, my own blog is here.) Ever wonder what happens when you take a few thousand highly capable computer agents, tell them to solve a problem, give them persistence bordering on the pathological,…...
Quantum Annealers Take Over Training of Variational Quantum Algorithms
2+ day, 4+ hour ago (81+ words) Variational quantum algorithms have become the workhorses of near-term quantum computing, promising everything from molecular simulation to machine learning on hardware that is still noisy and small. Yet a stubborn bottleneck has shadowed the field from the start: training the…...
Reinforcement Learning, Part 12: AlphaZero — Where Search, Self-Play, and Confidence Bounds Meet
2+ day, 19+ hour ago (690+ words) Three separate threads from earlier in this series converge in one place. Part 8 asked what happens when an agent explicitly models its…Continue reading on Medium » Reinforcement Learning, Part 12: AlphaZero — Where Search, Self-Play, and Confidence Bounds Meet Three separate threads…...
Iterative genetic programming builds feature subsets for high-dimensional
2+ day, 21+ hour ago (55+ words) High-dimensional data have become the defining challenge of modern machine learning, and a team of researchers in China has now unveiled a new evolutionary algorithm that promises to make sense of the overwhelming number of variables found in gene expression…...
Parallel Works, CoreWeave to speed DARPA research with fully managed AI Cloud environment
5+ day, 2+ hour ago (205+ words) CoreWeave co-founder, chairman and CEO Michael Intrator. - CoreWeave Parallel Works Inc., a Chicago-based provider of managed high-performance computing and AI, and Livingston-based AI builder and scaler CoreWeave Inc., announced Sept. 9 the deployment of a managed AI and high-performance computing platform…...
Quantum learning models bridge computing and machine intelligence
4+ day, 10+ hour ago (766+ words) Machine learning has transformed nearly every corner of modern science and industry, but the field is now confronting an uncomfortable truth: the computational resources required to train ever-larger models are growing at a pace that may soon become unsustainable....
Quantum Bayesian networks boost reinforcement learning in partially
4+ day, 12+ hour ago (55+ words) Reinforcement learning has powered some of artificial intelligence’s most celebrated achievements, from mastering the ancient game of Go to sharpening the reasoning abilities of large language models. Yet one of its hardest problems has remained stubbornly classical: what happens when…...