AI vs Cancer - How AI Can, and Can't, Cure Cancer (by Emilia Javorsky)
Emilia Javorsky explores how AI can realistically aid cancer research, where current hype exceeds evidence, and what changes researchers, policymakers, and funders must make to turn AI advances into real clinical impact.
Tech executives have promised that AI will cure cancer. The reality is more complicated — and more hopeful. This essay examines where AI genuinely accelerates cancer research, where the promises fall short, and what researchers, policymakers, and funders need to do next.
Peter Wildeford discusses methods for forecasting AI progress and why he sees AI as neither a bubble nor a normal technology, covering economic effects, national security, cyber capabilities, robotics, export controls, and prediction markets.
Inria researcher Carina Prunkl discusses why AI evaluation struggles to keep pace with general-purpose systems, including jagged capabilities, missed real-world behavior, misuse risks, de-skilling, red teaming, and layered safeguards.
Li-Lian Ang from Blue Dot Impact discusses how to build a workforce to defend against AI-driven risks, including engineered pandemics, cyber attacks, job disempowerment, and concentrated power, using a defense-in-depth framework for uncertain AI progress.
