The Weight of Innovation: MIT’s SEAL Framework Takes AI Model Updates to the Next Level
MIT’s SEAL framework equips AI models to compute their own gradients and tweak weights in real time. Learn how self-editing autogradients make AI more agile.
The Future is Now: MIT’s Plan for Self-Adapting LLMs
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.
Training AI Models on Limited Data: The Few-Shot Learning Phenomenon
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.
Databricks’ Agent Bricks
Databricks' Agent Bricks is a new platform that simplifies the development, deployment, and management of AI agents.