The 18th of March, 2018, was the day tech insiders had been dreading. That night, a new moon added almost no light to a poorly lit four-lane road in Tempe, Arizona, as a specially adapted Uber Volvo XC90 detected an object ahead. Part of the modern gold rush to develop self-driving vehicles, the SUV had been driving autonomously, with no input from its human backup driver, for 19 minutes. An array of radar and light-emitting lidar sensors allowed onboard algorithms to calculate that, given their host vehicle's steady speed of 43mph, the object was six seconds away – assuming it remained stationary. But objects in roads seldom remain stationary, so more algorithms crawled a database of recognizable mechanical and biological entities, searching for a fit from which this one's likely behavior could be inferred. At first the computer drew a blank; seconds later, it decided it was dealing with another car, expecting it to drive away and require no special action. Only at the last second was a clear identification found – a woman with a bike, shopping bags hanging confusingly from handlebars, doubtless assuming the Volvo would route around her as any ordinary vehicle would. Barred from taking evasive action on its own, the computer abruptly handed control back to its human master, but the master wasn't paying attention. Elaine Herzberg, aged 49, was struck and killed, leaving more reflective members of the tech community with two uncomfortable questions: was this algorithmic tragedy inevitable? And how used to such incidents would we, should we, be prepared to get? "In some ways we've lost agency. When programs pass into code and code passes into algorithms and then algorithms start to create new algorithms, it gets farther and farther from human agency. Software is released into a code universe which no one can fully understand."

"In two years, China will be ahead of the United States in AI (artificial intelligence)," states Denis Barrier, CEO of global venture firm Cathay Innovation. If so, China will largely determine how this technology transforms the world. Today's contest is more than a race for dominance in a new technology -- it's one between authoritarianism and democracy. "AI is the world's next big inflection point," says Ajeet Singh, CEO of ThoughtSpot in Palo Alto. Artificial intelligence is machine learning, which self-learns programmed tasks, using data, and the more it gets, the more learned it becomes.

Next, they want to try to do the same thing with a real (not virtual) celestial body: Mars. They think they have enough satellite images to make it happen. If they're right, the first people to walk on the Red Planet could find that all they have to do to navigate around the Martian surface is just take a picture.

Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.

Donald Trump has intensified his attacks on Google, with a fresh complaint about its home page. The president claimed that the site promoted the State of the Union address while Obama was president, by telling everyone who visited its home page to watch through YouTube. But as soon as Mr Trump became president those promotions stopped. He suggested that the change proves his claim that the search giant is biased against him. He had previously complained the site was failing to show positive enough results when he searched for his name.

Humans have been pondering the potential of artificial intelligence for thousands of years. Ancient Greeks believed, for example, that a bronze automaton named Talos protected the island of Crete from maritime adversaries. But AI only moved from the mythical realm to the real world in the last half-century, beginning with legendary computer scientist Alan Turing's foundational 1950 essay asked and provided a framework for answering the provocative question, "Can machines think?" At that time, the United States was in the midst of the Cold War. Congressional representatives decided to invest heavily in artificial intelligence as part of a larger security strategy.

Last Thursday, Senator Jeff Flake of Arizona introduced the following amendment to the U.S. Department of Defense appropriations bill currently in Congress: None of the amounts appropriated or otherwise made available by this Act may be obligated or expended for the development of a beerbot or other robot bartender. This sounds like a joke, but it's not: Legislation prohibiting Department of Defense funding of robot bartenders is on its way to becoming law. The reason why Senator Flake wants this to become law is based, at best, on a misunderstanding of how basic robotics research works. At worst, it's a deliberate decision to misrepresent the research for political gain. In 2015, MIT researchers presented a paper at the Robotics: Science and Systems (RSS) conference on "Policy Search for Multi-Robot Coordination under Uncertainty" [PDF].

A Justice Ministry statement said Wednesday that after an almost yearlong investigation the State Attorney's office summoned top Aeronautics Ltd. officials, including its chief executive, for a hearing pending indictment.

There are fears that tend to come up when people talk about futuristic artificial intelligence -- say, one that could teach itself to learn and become more advanced than anything we humans might be able to comprehend. In the wrong hands, perhaps even on its own, such an advanced algorithm might dominate the world's governments and militaries, impart Orwellian levels of surveillance, manipulation, and social control over societies, and perhaps even control entire battlefields of autonomous lethal weapons such as military drones. But some artificial intelligence experts don't think those fears are well-founded. In fact, highly-advanced artificial intelligence could be better at managing the world than humans have been. These fears themselves are the real danger, because they may hold us back from making that potential a reality. "Maybe not achieving AI is the danger for humanity," Tomas Mikolov, a research scientist for Facebook AI, said at The Joint Multi-Conference on Human-Level Artificial Intelligence in Prague on Saturday.

Based on feedback from Soldiers at an Army training course, ARL researcher Dr. Chad Kessens began to develop software to analyze whether any given robot could get itself "back on its feet" from any overturned orientation. "One Soldier told me that he valued his robot so much, he got out of his vehicle to rescue the robot when he couldn't get it turned back over," Kessens said. "That is a story I never want to hear again." Researchers from Navy PMS-408 (Expeditionary Missions) and its technical arm, the Indian Head Explosive Ordnance Disposal Technology Division, agree. They teamed up with JHU/APL and the prime contractor, Northrop Grumman Remotec, to develop the Advanced Explosive Ordnance Disposal Robotic System, or AEODRS, a new family of EOD robotic systems featuring a modular opens systems architecture.