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55 articles
This article explores the limitations of hybrid thinking in AI models and why researchers like Junyang Lin are now advocating for agentic thinking as a more robust and scalable approach.
This explainer explores agentic finance, a cutting-edge field where AI agents autonomously manage financial tasks. Learn how reinforcement learning, deep learning, and transformer models enable these systems to make intelligent financial decisions.
DeepReinforce has released Ornith-1.0, an open-source coding model that learns its own reinforcement learning scaffolding during training. The 397B parameter flagship model achieved a score of 82.4 on SWE-Bench Verified.
Learn to build a digital world environment for AI agent testing using Python, reinforcement learning, and PyTorch. This tutorial demonstrates how to create a simulated environment where AI agents navigate obstacles and learn optimal behaviors.
Prime Intellect releases prime-rl 0.6.0, an open framework for training trillion-parameter Mixture-of-Experts models using asynchronous reinforcement learning.
This explainer explores the concept of 'looping AI' - a revolutionary approach where multiple AI agents continuously interact in feedback cycles, enabling autonomous self-improvement and adaptation in dynamic environments.
OpenAI researchers show that training AI models on small doses of beneficial traits like truthfulness and corrigibility improves safety and performance across domains.
This explainer explores the advanced AI concepts behind modern robot vacuums, including SLAM algorithms, sensor fusion, and reinforcement learning techniques that enable autonomous navigation and adaptive cleaning.
This article explains how AI-powered dynamic pricing systems work and why they're transforming consumer technology markets. Learn about the machine learning algorithms behind real-time price optimization.
This explainer explores how artificial intelligence can optimize the cost of living by systematically reducing expenses in areas like housing, food, and wireless services through advanced optimization algorithms and machine learning techniques.
Learn to build a stateful search harness system inspired by Harness-1, a 20B parameter retrieval subagent trained with reinforcement learning. This tutorial teaches you to implement candidate pooling, evidence graph maintenance, and reinforcement learning-based decision making.
This article explains how autonomous AI agents using reinforcement learning and causal inference are transforming industrial maintenance from reactive to predictive, as demonstrated by Shell's deployment of C3 AI technology.
