MIT’s self-adapting LLM system lets models generate their own training examples, use feedback signals, and tweak themselves in real time for better accuracy and adaptability.
AI is reshaping diagnostics by combining imaging, ECGs, wearables and genetics to catch disease earlier and tailor treatments. Learn how smart tools are changing medicine.
MIT researchers are using generative AI for robotics to train machines in virtual environments. Robots learn to jump, land and even dive without manual reprogramming.
AI agents excel at data analysis but lack empathy and context. This article explores why combining AI power with human intuition leads to smarter decisions.
AI models are being shrunk down to size for edge devices, making them more efficient and powerful. Learn more about the techniques being used and the benefits of smaller AI models.
China is investing heavily to mass-produce non-binary AI chips that hold multiple states per memory cell. These analog modules aim to speed up AI, cut energy and power next-gen devices.
AlphaGenome uses a hybrid convolutional and transformer architecture plus RAG integration to boost genomics research, from precision medicine to gene therapy.
Google’s Gemini 2.0 experimental series marks the start of the agentic era—AI that breaks goals into plans and acts on them. Explore Project Astra, Mariner and Jules.
Salesforce Agentforce3 offers a live dashboard for AI agent visibility—track performance, spot issues, and optimize bots with real data.
Few-shot learning trains AI on minimal examples, blending meta-learning and transfer learning. Read about real use cases, challenges, and why small-data AI matters.