Abstract:
In the past half-century, advances in computation have accelerated & scientific progress and innovation in diverse fields at all scales, from particle physics to socioeconomics to cosmology. However, recent works point to stagnating innovation and diminishing returns in science (Cowen & Southwood ’19). Can advancements in artificial intelligence (AI) and machine learning (ML) help push science, engineering, and other fields through existing bottlenecks (be they physical, computational, or otherwise)? Is AI-driven science necessary for humankind to decode and solve its greatest challenges, such as nuclear fusion and neurodegenerative disease? The application of AI in scientific discovery presents very different challenges relative to popular environments such as game-playing and machine translation. In general, scientific discoveries require hypothesis and solution spaces that are orders of magnitude larger than existing AI environments, a far more elaborate verification process, and non-trivial integration with scientific materials and machines. In this talk, leading AI researcher Alexander Lavin will discuss the challenges and opportunities in AI-driven science, and further propose a “Nobel-Turing Challenge”: AI systems capable of making Nobel-caliber discoveries in science. Lavin will present several key areas of AI/ML, simulation, and computing to advance towards this goal, recent progress in areas such as chemistry and climate, and critical operational aspects like human-machine teaming and systems engineering in AI.

What You’ll Learn:
The aim is to share the opportunities and challenges at the intersection of AI and science, particularly in very timely and relevant sectors such as energy, biology, and climate.
Researchers and engineers in AI/ML and scientific fields will take away an understanding of key technologies for advancing scientific AI/ML, as well specific methods or concepts to apply in their own work. Leaders and entrepreneurs will take away inspiration and practical directions to align their initiatives (companies, teams, etc.) with the pursuit of AI/ML for science. Overall, the presentation of a “Nobel-Turing Challenge” will intrigue and motivate audiences broadly.

Alexander Lavin is a world-leading AI researcher and software engineer, specializing in probabilistic machine learning, scientific computing, and human-centric AI systems. He is the Founder and Chief Technologist of the Institute for Simulation Intelligence, a public-benefit “focused research organization” aiming to reshape the scientific method for the machine age, building novel technologies to synergise AI and simulation in areas such as climate, synbio, nuclear energy, and more. Lavin has explored AI and probabilistic computation via several perspectives: theoretical neuroscience and online learning with Numenta, general intelligence in robotics and computer vision with Vicarious AI, predictive medicines and causality as the founder of Latent Sciences (acquired), Earth systems and climate with NASA as an AI Advisor, and autonomous systems with Astrobotic. Lavin earned his Masters in Mechanical Engineering at Carnegie Mellon, a Masters in Engineering Management with Duke University, and Bachelors in Mech & Aero Engineering at Cornell University. He has won several awards for work in rocket science and space robotics, published in top journals and conferences across AI/ML and neuroscience, and was an honoree for the Forbes 30 Under 30 List in Science and the Patrick J. McGovern Tech for Humanity Prize. In his free time, Lavin enjoys running, yoga, live music, and reading sci-fi and theoretical physics books.