The legal case for using drone strikes outside of armed conflict needs "urgent clarification" from ministers, a cross-party parliamentary committee has said. The government insists it does not have a "targeted killing" policy, but the UK was clearly willing to use lethal force overseas for counter-terrorism, the Joint Committee on Human Rights said. It follows the killing of a UK citizen in Syria last year by an RAF drone. The government says it takes "lawful action" over direct threats to the UK. Reyaad Khan, a British member of the so-called Islamic State group, was killed by an RAF drone in Syria last August.

In the intellectual property plan, the government said it will consider a new registration system to protect rights for AI-created works. The system's coverage would be limited to creations with certain levels of marketability. The plan also called for the establishment of a group of municipal, school and company representatives to support intellectual property education. "We will work to enable everyone to create and use intellectual property," Prime Minister Shinzo Abe said at a meeting of the Intellectual Property Strategy Headquarters.

If your mind instantly went to Skynet, I can put your mind at ease; it's not Skynet. That's not, however, to say that this problem isn't just as scary, only without the cool special effects. While Sci-Fi has made the risks of robot takeover well-known, the more immediate concerns are the subtle decisions being made by (sometimes) poorly coded, or designed, algorithms that can drastically alter each of our lives. Our biggest ever edition of TNW Conference is fast approaching! The Obama administration published a report this week that examines problems associated with the shift to an increasingly automated world.

We introduce a model-free relax-and-round algorithm for k-means clustering based on a semidefinite relaxation due to Peng and Wei. The algorithm interprets the SDP output as a denoised version of the original data and then rounds this output to a hard clustering. We provide a generic method for proving performance guarantees for this algorithm, and we analyze the algorithm in the context of subgaussian mixture models. We also study the fundamental limits of estimating Gaussian centers by k-means clustering in order to compare our approximation guarantee to the theoretically optimal k-means clustering solution.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

"Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning. Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

The White House Office of Science and Technology Policy announced a decision to create a new subcommittee on Artificial Intelligence to look for ways to use the technology as American citizens interact with the federal government. "The Federal Government also is working to leverage AI for public good and toward a more effective government," Deputy U.S. Chief Technology Officer Ed Felten in a statement. The new subcommittee plans to work with the private sector to help implement AI in government activities such as welfare, crime, urban development, and the environment. "[T]here is tremendous potential in AI-driven improvements to programs and delivery of services that help make everyday life better for Americans in areas related to urban systems and smart cities, mental and physical health, social welfare, criminal justice, the environment, and much more," Felten said. The group has announced a series of public workshops to discuss the controversial aspects of AI, particularly the legal implications and using artificial intelligence for the "social good" as well as controlling the groundbreaking technology.

"Your space agency is on a journey to Mars. We need kids to create new tech that we are missing," Bolden said. "We can't get to Mars based on what we have today. We can go back to the moon and we will, but we have to go to Mars." Interactive projects like working on robots or partnering with NASA to send projects to the International Space Station will be particularly useful to show female and minority students "that science and math and technology is not only is fun but that it is available to them," he said. "Quit telling girls they can't learn math and science," he said.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

Jeet Raut's mom was told she no longer had breast cancer. But it turned out to be a false diagnosis and she had to undergo further treatment. She's okay now, but that medical mistake could have cost her life and it gave Raut the idea to build a better way to catch medical abnormalities in the body. He and his co-founder Peter Wakahiu Njenga created Behold.ai to speed up the process of finding cancers and minimize human error. "The idea behind Behold.ai is to increase efficiency," Raut told TechCrunch.