Continuous Learning FOR AI Agents: NOT YET

Discover AI · Beginner ·📄 Research Papers Explained ·1mo ago

Key Takeaways

Explores the concept of continuous learning for AI agents using in-context learning and evaluating frontier AI systems in real-world stateful environments

Original Description

The Problem w/ AI Was Never Intelligence. It Was Learning. Continual Learning on the job. Frontier AI fails in new benchmark on real world tasks. Continuous Learning FOR AI Agents: NOT YET In-Context Learning ICL Beats Claude Code AI Is Smart. So Why Doesn’t It Learn? All rights w/ authors: CONTINUAL LEARNING BENCH: Evaluating Frontier AI Systems in Real-World Stateful Environments Parth Asawa∗ UC Berkeley Christopher M. Glaze Snorkel AI Gabriel Orlanski University of Wisconsin-Madison Ramya Ramakrishnan Snorkel AI Benji Xu UC Berkeley Asim Biswal UC Berkeley Vincent Sunn Chen Snorkel AI Frederic Sala University of Wisconsin-Madison, Snorkel AI Matei Zaharia UC Berkeley Joseph E. Gonzalez UC Berkeley #airesearch #aiexplained #aicode #aiagents
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

Up next
Welcome to the Next Temperamental Era
Charles Schwab
Watch →