Sending instantaneous messages across long distances, or quickly computing over ungodly amounts of data are just two possibilities that arise if we can design computers to exploit quantum uncertainty, entanglement, and measurement. In this SFI Community Lecture, scientist Christopher Monroe describes the architecture of a quantum computer based on individual atoms, suspended and isolated with electric fields, and individually addressed with laser beams.
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.
In a two-part lecture series September 24 and 25, SFI Professor Cristopher Moore looked at two sides of computation — the mathematical structures that make problems easy or hard, and the growing debate about fairness in algorithmic predictions. The videos are now available.
The fourth bi-annual Postdocs in Complexity Conference at the Santa Fe Institute provides networking opportunities for early career researchers working on complex systems science, as well as special sessions from SFI faculty and other prominent speakers. This three-day conference will build on the themes of the previous three Postdocs in Complexity meetings, refining the structure to allow additional time to build community and focus on collaborations.
The bane of the language-learner is a goldmine for linguists, cultural evolutionists, and computer scientists, a group of whom will meet at SFI Aug. 27–28, 2018. Given the messy state of linguistic affairs, they ask, is it possible to quantitatively encode “meaning” independent of any particular language?
An SFI Working Group examines the evidence of low-density Maya settlements and the challenge this poses to the idea that density increases with population.
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 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.
On July 17 at The Lensic Performing Arts Center, economists Samuel Bowles and Wendy Carlin discussed the social consequences of a failed economic model, then outlined a new economic paradigm as the basis for a more sustainable and just global future. Watch the lecture.
SFI hosts a working group July 10-13, 2018 to discuss climate projections for the next 50 years and what those projections may mean for the future human niche.
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.
The latest scientific understanding of time, and how time shapes our experience, were subjects of a June 19 panel discussion between physicist James Hartle, cosmologist Sean Carroll, evolutionary theorist David Krakauer, and science writer Jenniffer Ouellette. Watch the panel discussion.
Complexity scientists meet at SFI to examine how collective decisions get made in biological systems and to what degree those systems share a mechanism from one system to the next.
"Algorithmic Information Dynamics: From Networks to Cells," is a new online course that will introduce students to tools that allow them to explore causal relationships in complex datasets. Register online through Complexity Explorer.