New AI-based facial recognition techniques are reducing false positive and false negative matches.
Thousands of small earthquakes, detected for the first time by a machine-learning process, reveal the distinct, razor-sharp edge of the Yakutat microplate as it subducts beneath the North American plate.
The Yakutat oceanic plateau is caught in the middle of a tectonic traffic jam with the Pacific plate as it subducts beneath the North American plate. The position and structure of the plates in this congested zone play a significant role in the earthquake and volcanic landscape of south-central Alaska.
The research published by Meghan Miller of Australian National University and her colleagues in The Seismic Record now shows the edge and extent of the Yakutat plate in astonishing detail.
As a philosopher and philosophical counselor, I research the connection between virtue and happiness. In particular, I’ve noticed a connection between sophrosyne and eudaimonia, the Greek philosophical concept for happiness, or living well.
Harmony of the soul
For the Greeks, sophrosyne represented excellence of character, moderation and self-control. It was connected to phronesis, or practical wisdom, and stood in marked contrast with hubris: excessive pride, dangerous overconfidence and lack of self-insight. Heraclitus, a philosopher who lived around 500 B.C.E., taught that sophrosyne was the most important virtue of all.
Studying physics can be very useful—even when it comes to machine learning. A digital “super-brain” with built-in knowledge of the fundamental laws of nature can speed up the development of optical components for everything from quantum computers to eyeglasses or camera lenses, according to a new study from Chalmers University of Technology in Sweden.
“When we fed the super-brain information about the laws of physics, it immediately got much smarter. Our calculations now take one tenth of the time previously required,” says Philippe Tassin, professor at the Department of Physics and Astronomy, Chalmers University of Technology.
The research team led by Tassin designs optical components in a field called nanophotonics. On a small scale—less than one wavelength—light can be controlled and manipulated in a completely different way than on larger scales. But there are also limitations on how light can be controlled in advanced ways in natural optical materials.
In 2026, the hype for artificial intelligence agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain environments which LMs struggle with.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Harvard University’s School of Engineering and Applied Sciences (SEAS) peered deeper into LMs to understand their main issues in high-stakes settings. Their test: Battleship, a classic guessing game that’s helped cognitive scientists study how humans seek information.
CSAIL and SEAS scholars added a twist by reframing the game around asking and answering natural language questions. In their “Collaborative Battleship” game, one participant is a “captain” who inquires about where hidden ships are, while their teammate plays the “spotter” by responding to those questions in real time.
Because the method does not require a needle, it could offer an alternative for people who are uncomfortable with injections. Researchers also believe it may make large scale vaccination campaigns easier and faster, particularly in settings where traditional injections are more difficult to administer.
Before human testing began, animal studies showed the vaccine could generate strong immune responses against multiple coronaviruses.
Non-invasive eye scans allow doctors a zoomed-in, three-dimensional look beneath the eye’s surface without causing discomfort or pain to the patient. Used routinely in clinics worldwide, the scans produce detailed views of individual layers of the eye’s interior to help diagnose conditions that threaten vision. But with that level of precision comes a flood of data—hundreds of images per scan that physicians have to review manually, a time-consuming process that is vulnerable to human error.
Now, researchers at Washington University School of Medicine in St. Louis, in collaboration with colleagues at the University of Washington in Seattle and Genentech, Inc., have developed an experimental artificial intelligence (AI) system that can speed the scan review process and help doctors spot subtle signs of eye disease sooner. The technology, called OCTCube-M, includes a family of three AI models that are designed to read and interpret 3D images of the eye’s retina as well as other types of eye scans.
In a new study, the researchers found that, compared with older models, the new AI system more accurately identified eight different retinal diseases, including age-related macular degeneration, a common disease that damages the retina and is the leading cause of blindness in people over 50. It also was more accurate in its predictions of how fast a severe form of this condition, called geographic atrophy, would progress.