PinnedPublished inTDS ArchiveAdvancing AI Reasoning: Meta-CoT and System 2 ThinkingHow Meta-CoT enhances system 2 reasoning for complex AI challengesJan 20A response icon2Jan 20A response icon2
PinnedPublished inGenerative AICOCONUT: Redefining Reasoning in Large Language ModelsRevolutionizing reasoning in large language models through latent space.Dec 17, 2024A response icon8Dec 17, 2024A response icon8
PinnedPublished inAI AdvancesNeural Fictitious Self-Play (NFSP) for Imperfect-Information GamesHow AI Advances Decision-Making in Complex Games Without Domain ExpertiseDec 26, 2024A response icon6Dec 26, 2024A response icon6
PinnedPublished inAI AdvancesLearning by Doing: An Introduction to Reinforcement LearningHow AI Agents Acquire Knowledge Through Trial and ErrorDec 14, 2024A response icon8Dec 14, 2024A response icon8
Published inHooked on BooksMy Journey Through ‘Life 3.0’: How AI’s Future Shapes My WorkPersonal reflections on Max Tegmark’s vision and its impact on my approach to technology and researchMay 25May 25
Published inGenerative AIGoogle ADK: Simplifying the Complex World of Agent-Based AIFrom basic question-answering to multi-agent orchestrationApr 21Apr 21
Published inData Science CollectiveFrom Pixels to Skill: Understanding AI through Mario’s Reinforcement Learning AdventuresReaders who are not Medium members are welcome to read this article via this link.Mar 31Mar 31
Published inGenerative AIPixels, Prompts, and PonyoThe technology behind the viral Ghibli trendMar 30Mar 30
Published inTowards AIHow I Built an Adaptive Concept Explainer Using Hugging Face ModelsDemystifying Complex Ideas Through Multi-Level ExplanationsMar 29A response icon1Mar 29A response icon1
Mastering the Reward Loop: A Hands-On Guide to Reinforcement Learning with PythonFrom zero to agent: building intelligent systems that learn through experienceMar 3Mar 3