An international team of scientists including Kieron Burke, UCI professor of chemistry, has created a machine-learning algorithm that predicts molecular behavior. The breakthrough may aid in the development of pharmaceuticals and materials to enhance the performance of batteries, solar cells and digital displays. In a study published in Nature Communications, the researchers describe how their algorithm gathers knowledge about atomic interactions in a molecule and then uses that information to anticipate new actions. Complex atomic interactions are prescribed by quantum mechanical calculations. The research team, which also included scientists from New York University and the Technical University of Berlin, found a way to simulate chemical behavior within a molecule without having to perform quantum-level number crunching.

Earlier this month a self-driving shuttle in Las Vegas patiently waited as a delivery truck backed up, then backed up some more, then backed right into it. Inconveniently for the roboshuttle's developer Navya, this happened within hours of the shuttle's inauguration ceremony. The real problem is that the shuttle can't learn from the incident the way a human would: immediately and without forgetting how to do everything else in the process. The U.S. Defense Advanced Research Projects Agency (DARPA) is looking to change the way AI works through a program it calls L2M, or Lifelong Learning Machines. The agency is looking for systems that learn continuously, adapt to new tasks, and know what to learn and when.

In its race to embrace driverless vehicles, Washington has cleared away regulatory hurdles for auto companies and brushed aside consumer warnings about the risk of crashes and hacking. But at a recent hearing, lawmakers absorbed an economic argument that illustrated how the driverless revolution they are encouraging could backfire politically, particularly in Trump country. It was the tale of a successful, long-distance beer run. A robotic truck coasted driverless 120 miles down Interstate 25 in Colorado on its way to deliver 51,744 cans of Budweiser. Not everyone at the hearing was impressed by the milestone, particularly the secretary-treasurer of the Teamsters, whose nearly 600,000 unionized drivers played no small roll in President Trump's victory last year.

Intelligence agencies have a limited number of trained human analysts looking for undeclared nuclear facilities, or secret military sites, hidden among terabytes of satellite images. But the same sort of deep learning artificial intelligence that enables Google and Facebook to automatically filter images of human faces and cats could also prove invaluable in the world of spy versus spy. An early example: US researchers have trained deep learning algorithms to identify Chinese surface-to-air missile sites--hundreds of times faster than their human counterparts. The deep learning algorithms proved capable of helping people with no prior imagery analysis experience find surface-to-air missile sites scattered across nearly 90,000 square kilometers of southeastern China. Such AI based on neural networks--layers of artificial neuron capable of filtering and learning from huge amounts of data--matched the overall 90 percent accuracy of expert human imagery analysts in locating the missile sites.

This Tuesday, we're testing concrete speakers, blissful Dreamcast games reimagined in VR, and a robot that will copy your moves. The FCC's plan to undo net neutrality is about to be revealed According to reports, Ajit Pai will unveil the details of his plan to roll back Title II net neutrality protections later today. Timed during a short holiday week, the plan is expected to follow what we heard in April -- with rules preventing ISPs from blocking, slowing down or charging extra for different kinds of content removed, and responsibility for managing disputes pushed to the FTC. Concrete speakers are heavy on the wallet. Master & Dynamic's concrete speaker is equal parts sound and spectacle If you're a fan of well-designed headphones that have a unique aesthetic, Master & Dynamic should be at the top of your list.

Spy agencies are to use computer algorithms to keep tabs on 20,000 former terror suspects because they don't have the manpower to physically watch all of them at once. MI5 and MI6 will use the complex system to sift through massive amounts of data to alert intelligence agents to worrying behaviour, under potential plans. A review is expected to recommend security agencies widen their searches when examining the online footprint and movement of these former jihadis. Britain's counter-terrorism officials currently actively monitor around 3,000 people. But following a wave of terror attacks in the UK, the Government admitted there were as many as 20,000 former subjects of interest no longer classed as posing serious danger.

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form --- where form could be a tree, ring, chain, grid, etc. [Kemp & Tenenbaum (2008). The discovery of structural form. PNAS, 105(3), 10687-10692]. While this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist.

For the first time, artificial intelligence (AI) may have eclipsed the science that brought it to "life": we now have AI that can converse in a human-like manner, robots designed to look like us, and deep learning machines built specifically to learn, think, and act the way we do. Experts believe that by 2030, machine intelligence will be on par with humans and that by 2045, the capabilities of AI will actually surpass human intelligence. Given the rate of advancement the field has seen and the resources being dedicated towards its continued development, robotics researchers believe we're closer to "thinking" machines than ever before. "It's getting to a point where we might be able to say this thing has a sense of itself, and maybe there is a threshold moment where suddenly this consciousness emerges," mathematician Marcus du Sautoy from the University of Oxford said. "And if we understand these things are having a level of consciousness, we might well have to introduce rights.

Hundreds of millions of dollars can go into the school bus-sized satellites that blast into orbit above Earth and provide services including broadband internet, broadcasting or military surveillance. But if a part breaks or a satellite runs out of fuel, there's no way to send help. Commercial industry and government agencies believe they're getting to close having an answer: robot repairs. The idea is to extend the lives of satellites through on-orbit satellite servicing, in which robotic spacecraft essentially act as the AAA roadside service trucks of space, traveling from satellite to satellite to refuel them and fix problems. On a spring day earlier this year in Greenbelt, Md., 30 companies gathered at NASA's Goddard Space Flight Center to learn about the technology and view hardware for on-orbit satellite servicing. They ranged from spacecraft makers to purveyors of robot arms and even insurance brokers.

No machine learning algorithm is perfect. Whilst the margin of error might be tiny, any computer which uses such algorithms sometimes makes mistakes. Earlier this month, research conducted by a team of students from MIT showed that Google's neural network could be tricked into misidentifying a 3D printed turtle as a gun. The group used a hacking technique known as an adversarial attack, altering the image that the software received. In other words, an adversarial attack is a smokescreen for computers.