October 13-16, graduate students can meet with leading scientists to learn about modeling and evaluating the future of human populations and their environments. Free tuition for accepted students. Apply before July 11, 2018.
An SFI workshop examines the key impediments to building machines that understand meaning, and how much understanding is necessary for artificially intelligent machines to approach human-level abilities in language, perception, and reasoning.
The autumn Applied Complexity Network meeting “Risk: Retrospective Lessons and Prospective Strategies,” explores what we have learned since the financial crisis of 2008.
The notion that an attractive person is “out of your league” doesn’t often dissuade dating hopefuls – at least online. In fact, the majority of online daters seek out partners who are more desirable than themselves, suggests a new large-scale analysis published in Science Advances.
In this SFI Community Lecture, science writer Sabine Hossenfelder explains what physicists mean when they say a theory is beautiful, what went wrong with their reliance on it, and how the field can move on. Watch her talk.
A-list for ALIFE: Steen Rasmussen receives Lifetime Achievement Award from International Society for Artificial Life
A small cadre of scientists and entrepreneurs convened a two-week long SFI working group to address the growing gap between our physical and social technologies.
Neuroscientists and complexity scientists meet to develop new tools for studying the brain as a complex network. Their working group, titled “Cognitive Regime Shift: When the Brain Breaks,” is part of SFI’s Aging, Adaptation, and the Arrow of Time research theme, funded by the James S. McDonnell Foundation.
Parakeet pecking orders, basketball match-ups, and the tenure-track: How analyzing winners and losers can reveal rank within networks
In a paper published in Science Advances, researchers from the Santa Fe Institute describe a new algorithm called SpringRank that uses wins and losses to quickly find rankings lurking in large networks. When tested on a wide range of synthetic and real-world datasets, ranging from teams in an NCAA college basketball tournament to the social behavior of animals, SpringRank outperformed other ranking algorithms in predicting outcomes and in efficiency.
This question of how the collective influences individual performance is central to the work of SFI’s investigation into the limits of human performance. In a workshop that takes place June 25-27, experts from a range of disciplines, including physiology, organizational behavior, sports analytics and applied mathematics, explore how the collective affects the individual.