Lifeboat Foundation

This scientist made an algorithm to predict which artists succeed–without even looking at their art.

Why do some people live lawful lives, while others gravitate toward repeated criminal behavior? Do people choose to be moral or immoral, or is morality simply a genetically inherited function of the brain? Research suggests that psychopathy as a biological condition explained by defective neural circuits that mediate empathy, but what does that mean when neuroscience is used as evidence in criminal court? How can understanding neuroscience give us an insight into the actions and behaviors of our political leaders?

Forensic psychiatrist Dr. Octavio Choi https://med.stanford.edu/profiles/ochoi will explore how emerging neuroscience challenges long-held assumptions underlying the basis—and punishment—of criminal behavior.

Continue reading “The Neuroscience of Real Life Monsters: Psychopaths, CEOs, & Politicians (Science on Tap Livestream)” »

In a new study, scientists have observed long-lived excitons in a topological material, opening intriguing new research directions for optoelectronics and quantum computing.

Excitons are charge-neutral quasiparticles created when light is absorbed by a semiconductor. Consisting of an excited electron coupled to a lower-energy electron vacancy or hole, an exciton is typically short-lived, surviving only until the electron and hole recombine, which limits its usefulness in applications.

“If we want to make progress in quantum computing and create more sustainable electronics, we need longer exciton lifetimes and new ways of transferring information that don’t rely on the charge of electrons,” said Alessandra Lanzara, who led the study. Lanzara is a senior faculty scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and a UC Berkeley physics professor. “Here we’re leveraging topological material properties to make an exciton that is long lived and very robust to disorder.”

New research offers a path toward transplants that can fix damage from a heart attack without causing life-threatening arrhythmias.

A book talk by:

Robert Stickgold, PhD

Continue reading “When Brains Dream: Exploring the Science and Mystery of Sleep” »

Researchers should avoid the lure of proprietary models and develop transparent large language models to ensure reproducibility.

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Never listen to anyone who says the big questions have already been answered. We still have plenty to learn about extending our lifespans.

Large language models are drafting screenplays and writing code and cracking jokes. Image generators, such as Midjourney and DALL-E 2, are winning art prizes and democratizing interior design and producing dangerously convincing fabrications. They feel like magic. Meanwhile, the world’s most advanced robots are still struggling to open different kinds of doors. As in actual, physical doors. Chatbots, in the proper context, can be—and have been—mistaken for actual human beings; the most advanced robots still look more like mechanical arms appended to rolling tables. For now, at least, our dystopian near future looks a lot more like Her than M3GAN.

The counterintuitive notion that it’s harder to build artificial bodies than artificial minds is not a new one. In 1988, the computer scientist Hans Moravec observed that computers already excelled at tasks that humans tended to think of as complicated or difficult (math, chess, IQ tests) but were unable to match “the skills of a one-year-old when it comes to perception and mobility.” Six years later, the cognitive psychologist Steven Pinker offered a pithier formulation: “The main lesson of thirty-five years of AI research,” he wrote, “is that the hard problems are easy and the easy problems are hard.” This lesson is now known as “Moravec’s paradox.”