The smartphones that are now ubiquitous were just gaining popularity when Anthony Wagner became interested in the research of his Stanford colleague, Clifford Nass, on the effects of media multitasking and attention. Though Wagner, a professor of psychology at Stanford University and director of the Stanford Memory Laboratory, wasn’t convinced by the early data, he recommended some cognitive tests for Nass to use in subsequent experiments. More than 11 years later, Wagner was intrigued enough to write a review on past research findings, published in Proceedings of the National Academy of Sciences, and contribute some of his own.
Investigators may have compared a serial killer’s DNA with that of one million unwitting genealogy enthusiasts as part of an investigation that led to the arrest earlier this week of a man accused of being California’s elusive “Golden State Killer.”
BrainNet allows collaborative problem-solving using direct brain-to-brain communication.
In recent years, physicists and neuroscientists have developed an armory of tools that can sense certain kinds of thoughts and transmit information about them into other brains. That has made brain-to-brain communication a reality.
China’s spiraling space station, Tiangong-1, looks like it will plummet to Earth and burn up in the atmosphere sometime on April 1st — though where it will fall is still up for debate. So if you want to keep an eye on Tiangong-1’s whereabouts over the weekend, there are numerous space agencies and websites to follow.
Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research).