HAI Weekly Seminar with Andrew Ng
From improving medical diagnosis to optimizing supply chains, AI holds the promise of transforming every industry. However, many companies and teams--particularly ones outside the consumer internet industry--are still struggling to take research breakthroughs or promising proof-of-concept demonstrations and turn them into practical production deployments. How can researchers and AI professionals help bridge this gap? In this talk, I’ll describe some key challenges facing AI deployments, and also discuss some solutions, ranging from techniques for working with small data, to improving algorithms’ robustness and generalizability, to systematically planning out the full-cycle of machine learning projects.
Watch Event Recording
Related Events
Joel Becker | Reconciling Impressive AI Benchmark Performance with Limited Developer Productivity Impacts
AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
.png&w=1920&q=100)
AI coding agents now complete multi-hour coding benchmarks with roughly 50% reliability, yet a randomized trial found experienced open-source developers took about 19% longer when allowed frontier AI tools than when tools were disallowed...
Dan Iancu & Antonio Skillicorn | Interpretable Machine Learning and Mixed Datasets for Predicting Child Labor in Ghana’s Cocoa Sector
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
Wolfgang Lehrach | Code World Models for General Game Playing
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.