Through a novel combination of machine learning and atomic force microscopy, researchers in China have unveiled the molecular surface structure of “premelted” ice, resolving a long-standing mystery surrounding the liquid-like layer which forms on icy surfaces.
Detailed in a study in Physical Review X, the approach could also be applied more widely to reveal surface features that are too challenging for existing microscopy techniques to resolve.
Money is supposed to be the reward for effort. Elon Musk thinks it eventually becomes unnecessary paperwork.
While talking with Indian entrepreneur and investor Nikhil Kamath on the “People by WTF” podcast last month, the Tesla and SpaceX CEO and richest man in the world returned to a theme he’s raised before. It’s one he treats less like a theory and more like an inevitability. As artificial intelligence and robotics accelerate, Musk believes society moves past jobs, past income debates, and straight into something stranger.
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A new artificial neural-network architecture opens a window into the workings of a tool previously regarded as a black box.
Thanks to the extremely large datasets and computing power that have become available in recent years, a new paradigm in scientific discovery has emerged. This new approach is purely data driven, using large amounts of data to train machine-learning models―typically neural networks―to predict the behavior of the natural world [1]. The most prominent achievement of this new methodology has arguably been the AlphaFold model for predicting protein folding (see Research News: Chemistry Nobel Awarded for an AI System That Predicts Protein Structures) [2]. But despite such successes, these data-driven approaches suffer a major drawback in that they are generally “black boxes” that offer no human-accessible understanding of how they make their predictions. This shortcoming also extends to the models’ inputs: It is often desirable to build known domain knowledge into these models, but the data-driven approach excludes that option.
Feelings of guilt and shame can lead us to behave in a variety of different ways, including trying to make amends or save face, cooperating more with others or avoiding people altogether. Now, researchers have shed light on how the two emotions emerge from cognitive processes and in turn guide how we respond to them.
Their study is published in eLife. The editors say it provides compelling behavioral, computational and neural evidence to explain the cognitive link between emotions and compensatory actions. They add that the findings have broad theoretical and practical implications across a range of disciplines concerned with human behavior, including psychology, neuroscience, public policy and psychiatry.
“Self-driving” or “autonomous” labs are an emerging technology in which artificial intelligence guides the discovery process, helping design experiments or perfecting decision strategies.
While these labs have generated heated debate about whether humans or machines should lead scientific research, a new paper from Argonne National Laboratory and the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) has proposed a novel answer: Both.
In the paper published in Nature Chemical Engineering, the team led by UChicago PME Asst. Prof. Jie Xu, who has a joint appointment at Argonne, outlined an “AI advisor” model that helps humans and machines share the driver’s seat in self-driving labs.
This week’s ThreatsDay Bulletin tracks how attackers keep reshaping old tools and finding new angles in familiar systems. Small changes in tactics are stacking up fast, and each one hints at where the next big breach could come from.
From shifting infrastructures to clever social hooks, the week’s activity shows just how fluid the threat landscape has become.
Here’s the full rundown of what moved in the cyber world this week.
An automated campaign is targeting multiple VPN platforms, with credential-based attacks being observed on Palo Alto Networks GlobalProtect and Cisco SSL VPN.
On December 11, threat monitoring platform GreyNoise observed the number of login attempts aimed at GlobalProtect portals peaked at 1.7 million during a period of 16 hours.
Collected data showed that the attacks originated from more than 10,000 unique IP addresses and were aimed at infrastructure located in the United States, Mexico, and Pakistan.