Self-improvement is a new frontier in AI

Scaling laws, which demonstrate a predictable relationship between AI's performance and the amount of training data, compute, and model size, have driven much of the progress in AI in the past few years. In this talk, we present self-improvement as a new frontier for AI. We have entered a new era where models themselves are powerful sources of intelligence that can indefinitely synthesize new experiences and strategies through thinking, reasoning, and interacting with external environments. Two important enablers of model self-improvement are test-time compute scaling and deep reinforcement learning. Building on these two paradigms, we will discuss our recent work to generate more powerful AI capabilities, design better self-verification techniques, process long context lengths, and train models that can iteratively improve upon their own learned experiences. Finally, we will explore the future of self-improvement and why it represents a significant and largely untapped frontier for general artificial intelligence. The talk will conclude with a discussion of the future of self-improvement, highlighting its role as a significant and largely untapped frontier for general artificial intelligence.

Bio

Azalia Mirhoseini is an Assistant Professor of Computer Science and founder of Scaling Intelligence Lab at Stanford University. Her lab develops scalable and self-improving AI systems and methodologies towards the goal of advancing artificial general intelligence. She also spends time at Google DeepMind as a Senior Staff Scientist. Prior to Stanford, she spent several years in industry AI labs, including Google Brain and Anthropic. Her past work includes Mixture-of-Experts (MoE) neural architectures, now commonly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; and research on inference-time scaling laws. Her research has been recognized through the MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, The Times, ZDNet, VentureBeat, and WIRED.