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Machine learning research

What AI gets wrong and what failure teaches us

What AI gets wrong and what failure teaches us

Jennifer Neville discusses her research into "surprising failures" in AI systems, where models, particularly LLMs, struggle with complexity in realistic user workflows. Her team focuses on practical evaluation for multi-turn conversations and agentic knowledge work, emphasizing the need for robust AI that understands context and user intent over time. She offers insights into bridging the gap between user expectations and AI capabilities, advocating for detailed user feedback and outlining the future of human-AI collaboration.

Replit CEO Amjad Masad on What Young People Should Learn in the AI Era

Replit CEO Amjad Masad on What Young People Should Learn in the AI Era

Replit founder Amjad Masad and HAA co-founder Gagan Biyani discuss the future of education in the AI era. They argue for fostering young people's natural inclination to question norms, advocating for project-based, curiosity-driven learning over traditional, prescriptive models. The conversation also explores the pitfalls of premature startup professionalization, the evolving nature of work with AI, and the critical importance of trust, discernment, and personal development in tech's high-stakes environment.

AI is Revolutionizing Scientific Discovery Featuring Nobel Laureate John Jumper

AI is Revolutionizing Scientific Discovery Featuring Nobel Laureate John Jumper

John Jumper, one of the creators of AlphaFold, discusses the journey of developing an AI system to solve the protein folding problem. He emphasizes that the breakthrough was driven more by novel research and a combination of "mid-scale ideas" than by raw data or compute scale alone. The talk covers the importance of blind-assessment benchmarks like CASP, the strategy of releasing a massive, accessible database to drive adoption and trust, and the unexpected ways the scientific community used the tool. He concludes by framing AI for science as a powerful amplifier for experimentalists, accelerating discovery by generating high-quality, testable hypotheses.