That would be a Mobile Virtual Player, a remote-controlled dummy that can run drills with football players, allowing them to practice against an opponent that never gets tired. The MVP was developed by Dartmouth Football head coach Buddy Teevens as a way for players to practice basic skills like tackling, without risking as much injury as they would if they were slamming into their teammates. The dummies are each individually controlled by remote control from the sidelines. In addition to the Steelers, the MVP's are being used in beta testing by an assortment of universities for football and rugby practice, and by the United States' men's and women's rugby Olympic teams. The MVP company is working on an autonomous version of the dummy as well.

Move over Janet Yellen, automation in the workplace is about to get personal. Instead of relying on the Federal Reserve chair, imagine using a computer to transform mountains of raw economic data into reliable predictions for unemployment, inflation and gross domestic product. "The capability is here," says Andrew Lo, director of the Laboratory for Financial Engineering at the Massachusetts Institute of Technology, near Boston. "The biggest hurdle is the cultural barrier. You've got a lot of central bankers who are not as open to technology."

One of Google's latest projects is the Magenta project, an effort to generate music, video and images using machine intelligence. The big question Project Magenta tries to answer is, "Can machines make music and art?" If this is possible, the Google team wants to learn how, and if it cannot, they want to know why. Douglas Eck, one of the research scientists working on Magenta, wrote that before Magenta, he worked on music search and recommendation for Google Play Music. His goal in this area was to "use machine learning and an audio signal processing to help listeners find the music they want when they want it."

Learning machines are capable of working ever more autonomously on ever more complex tasks. In this blog, Muz Janoowalla explores whether it would be smart for humankind to develop an'intelligent gun'. There are an estimated 875 million civilian, law-enforcement, and military firearms in the world, of which 650 million are in the hands of civilians, either legally or illegally. Given the plethora of high-profile gun attacks in recent months and years – particularly in the US, but also in France, Norway, Pakistan and Tunisia, to name but a few – it is disturbingly easy to imagine gunmen on the loose in a school or at a public event, shooting indiscriminately and leaving casualties in their wake. Imagine how different things could be if a gun had artificial intelligence built into it, turning it into an intelligent gun.

WASHINGTON – The death of the Taliban's leader in a U.S. drone strike has scrambled discussions between the U.S. military and the White House over whether to let U.S. forces once again conduct offensive operations against the insurgent group in Afghanistan. The American military wants presidential permission to use airpower to blunt the group's threatened advances this summer, according to several U.S. officials. The White House first wants to see what effect the death of Mullah Akhtar Mansour in Pakistan over the weekend will have on the Taliban, senior administration officials said. President Barack Obama confirmed Mansour's death on Monday. The death came amid indications of an impending Taliban offensive.

Imagine you're sitting in a coffee shop. Out of the corner of your eye, you see a stranger pointing his phone in your direction. The next day, you get an email from someone claiming to have seen you at the coffee shop. You have no idea how he got your contact information, let alone how he identified you. The power to identify total strangers on the street is the advertising pitch for a new wave of startups hoping to capitalize on rapidly advancing facial recognition technology.

Congratulations are in order for the folks at Google Deepmind (https://deepmind.com) who have mastered Go (https://deepmind.com/alpha-go.html). However, some of the discussion around this seems like giddy overstatement. Wired says, "machines have conquered the last games" (http://bit.ly/200O5zG) The truth is nowhere close. For Go itself, it has been well known for a decade that Monte Carlo tree search (MCTS, http://bit.ly/1YbLm4M; that is, valuation by assuming randomized playout) is unusually effective in Go.

There is a rhythm in the affairs of the Association for Computing Machinery and June marks our annual celebration of award recipients and the biennial election of new officers. I will end my final year as past president, Alex Wolf will begin his first year in that role, and a new president and other officers will take their places in the leadership. June also marks Bobby Schnabel's first appearance at our annual awards event in his role as CEO of ACM. I am especially pleased that two former Stanford colleagues, Martin Hellman and Whitfield Diffie, are receiving the ACM A.M. Turing Award this year. Nearly four decades have passed since their seminal description of what has become known as public key cryptography and in that time the technology has evolved and suffused into much of our online and offline lives.

Ever since gerard salton of Cornell University developed the first computerized search engine (Salton's Magical Automatic Retriever of Text, or SMART) in the 1960s, search developers have spent decades essentially refining Salton's idea: take a query string, match it against a collection of documents, then calculate a set of relevant results and display them in a list. All of today's major Internet search engines--including Google, Amazon, and Bing--continue to follow Salton's basic blueprint. Yet as the Web has evolved from a loose-knit collection of academic papers to an ever-expanding digital universe of apps, catalogs, videos, and cat GIFs, users' expectations of search results have shifted. Today, many of us have less interest in sifting through a collection of documents than in getting something done: booking a flight, finding a job, buying a house, making an investment, or any number of other highly focused tasks. Meanwhile, the Web continues to expand at a dizzying pace.

It was unusual for Martin Hellman, a professor of electrical engineering at Stanford University, to present two papers on cryptography at the International Symposium on Information Theory in October 1977. Under normal circumstances, Steve Pohlig or Ralph Merkle, the doctoral students who also had worked on the papers, would have given the talks, but on the advice of Stanford's general counsel, it was Hellman who spoke. The reason for the caution was that an employee of the U.S. National Security Agency, J.A. Meyer, had claimed publicly discussing their new approach to encryption would violate U.S. law prohibiting the export of weapons to other countries. Stanford's lawyer did not agree with that interpretation of the law, but told Hellman it would be easier for him to defend a Stanford employee than it would be to defend graduate students, so he recommended Hellman give the talk instead. Whitfield Diffie, another student of Hellman's who says he was a hippie with "much more anti-societal views then," had not been scheduled to present a paper at the conference, but came up with one specifically to thumb his nose at the government's claims.