Topic: scientific discovery

Armin Tavakoli discusses the growing ability of commercial AI models to contribute to scientific discovery with limited human involvement. The talk surveys current capabilities and examines their consequences for academia, organizing the discussion around grants and recruitment, scientific practice itself, and university education. It proposes possible responses and invites a wider discussion of how institutions and researchers should address these challenges. Hybrid seminar in the Rydberg Lecture Hall, Department of Physics, Professorsgatan 1B, Lund, Sweden, with public Zoom attendance. Coff

Supports agentic AI research and open-source tools, including multi-agent collaboration, scientific discovery, autonomous trajectory verification, and customization through post-training or optimization of prompts, tools and agent scaffolding. Research may address safe agent behavior, data integration, enterprise operation and human–AI collaboration. Submit a scientific proposal using the ARA template, encouraged to be no more than four pages excluding appendices. Decisions are expected in February 2027. Applications close on 4 November 2026 at 11:59pm Pacific Time.

Max Welling discusses how ideas from physics may inform the next generation of artificial intelligence. The conversation examines physical structure, generative modelling and the relationship between learning systems and scientific discovery.

Anjan Chatterjee discusses how neuroscience can investigate beauty and aesthetic experience, including what neurological changes and responses to faces reveal about perception and judgement. Shankar Vedantam then speaks with physicist Frank Wilczek about the role of beauty in scientific discovery. The episode connects empirical questions about the human response to beauty with the use of aesthetic intuitions in developing physical theories.

Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.

Concerns about the impact of social media use on adolescent well-being and mental health are common. While the amount of research in this area has increased rapidly over the last 5 years, most outputs are still marred by a multitude of limitations. These shortcomings have left our understanding of social media effects severely limited, holding back both scientific discovery and policy interventions. This talk discusses how developmental, cognitive and neuroscientific approaches might provide a new and improved way of studying social media effects. It will detail new studies in support of this