Predicting human life
If we had extremely detailed data about every person in an entire country… what could we really know? Could we predict when someone will die? And what happens when we include not just individual data, but entire social networks? Family, friends, colleagues? How predictable are human lives, actually?
A few years ago, speaker Sune Lehmann had a striking realization: Large Language Models aren’t really about language. They’re about sequences. Language just happens to be one of the most refined systems of sequences we have: words unfolding according to grammar, context, and social rules. But what if we apply that same logic to something else?
Human lives can also be understood as sequences. You are born, assigned a birth weight, move to a certain address, start school, meet people, change jobs. Step by step, a life unfolds. Using Denmark’s uniquely detailed registry data, Sune Lehmann and his team trained a new kind of model; one that treats life itself as data. The result is a system that can detect patterns in human lives with remarkable precision.
During this edition, Sune Lehmann takes us inside this radical approach to understanding human lives and the questions it raises about prediction, privacy, and what it means to be human.
This event is an initiative by the Dutch Institute for Emergent Phenomena (DIEP) with the support of the University of Amsterdam. Science & Cocktails Amsterdam is presented in cooperation with Paradiso Amsterdam. This special event is integrated in the Institute for Advanced Study (IAS) Festival.
Talk by
Sune Lehmann
Sune Lehmann is professor of complexity and network science at the Technical University of Denmark and a professor of data science at the Center for Social Data Science (SODAS) at the University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, his research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. He works on large-scale behavioural data and while his primary focus is on modelling complex networks, his research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behaviour on social media. He is a member of the Royal Danish Academy of Sciences and Letters and a Chief Scientist in the Danish National Center for AI in Society (CAISA).
