A renewed international effort is gearing up to design computers and software that smash language barriers and create a borderless global marketplace. A woman sits at a desk in Manhattan, talking to herself in French. The phrases she balances on each breath are musical to American ears. She has postcards of Montreal tacked up on the walls of her cubicle – pastel-painted houses in the snow – so as she sculpts the contours of each syllable, she can remind herself of the place where the sounds she's making are heard every day in the street. Her name is Guylaine Laperrière, and she came to New York City more than a decade ago to study musical theater. One day, a friend asked her if she wanted to make a little cash dubbing a French voice-over for a promotional short about insurance. She took the job, and was surprised how much she enjoyed bringing ideas from one language home into another. This article has been reproduced in a new format and may be missing content or contain faulty links.

Charles Platt reports on the latest battle to determine the most human computer, even as he worries that he may be the least human human. Robert Epstein is giving us all a pep talk. "You must work very hard to convince the judges that you're human," he tells us. "You shouldn't have any trouble doing that – because you are human." This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue. He wears Dr. Martens boots, black jeans, a black shirt, a Mickey Mouse tie, and an earring. His longish hair is brushed straight back and flips up over his collar. Five of us are listening to him in a beige conference room on the brand-new campus of California State University at San Marcos, near San Diego. Soon we will be put in front of computer terminals, where we will follow Epstein's instructions and, yes, do our best to seem human. Our purpose is to find out whether 10 judges can tell the difference between humans and artificial-intelligence programs, when they are online at the same time.

This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue. Seventeen of the world's most wired minds stake their names – and their cash – on the future. Pronouncements about the future come easy. Even when made with an air of authority, they're usually just cheap talk, rarely revisited. Only the tiny fraction that have proven correct tend to be remembered, when their authors want to take credit. The Long Bets Foundation, a new project masterminded by Well founder Stewart Brand and Wired editor at large Kevin Kelly, hopes to raise the quality of our collective foresight by incorporating money and accountability into the process of debate. If someone makes a grandiose claim, any skeptic can challenge it – "Would you bet on that?" – and the Long Bets Foundation will keep tabs on the wager, whether it takes five years or five decades to come to pass. If proven right, a predictor can relish the victory; if wrong, the challenger gets the glory. By preserving the terms of the wager in public view, Long Bets promises to be more than a service for confident prognosticators. Over time, it hopes to foster better understanding of how predictions in aggregate work out in reality – what kinds of truths are easiest (or hardest) to forecast, and what kinds of people are right (or wrong) most reliably. Following are the first-ever "long bets."

Mark Zuckerberg recently handpicked the longtime NYU professor to run Facebook's new artificial intelligence lab. The IEEE Computational Intelligence Society just gave him its prestigious Neural Network Pioneer Award, in honor of his work on deep learning, a form of artificial intelligence meant to more closely mimic the human brain. And, perhaps most of all, deep learning has suddenly spread across the commercial tech world, from Google to Microsoft to Baidu to Twitter, just a few years after most AI researchers openly scoffed at it. All of these tech companies are now exploring a particular type of deep learning called convolutional neural networks, aiming to build web services that can do things like automatically understand natural language and recognize images. At China's Baidu, they drive a new visual search engine.

Dr. Hodes was at the forefront of computer-related research at the Massachusetts Institute of Technology and the National Institutes of Health. While working toward his doctorate in mathematic logic at MIT from 1957 to 1962, he studied under two founders of theoretical computer science and artificial intelligence, Marvin Minsky and John McCarthy. Dr. Hodes was a member of the artificial intelligence group of the MIT Research Laboratory of Electronics and did pioneering work in the development of the computer programming language LISP, which was used in artificial intelligence research. He also is credited with being one of the first people to recognize that logic could be used as a programming language. In 1966, Dr. Hodes joined NIH and worked in the artificial intelligence laboratory before moving to the National Cancer Institute.

While working in his native Poland in the 1960s, Dr. Michalski devised an early computer system that could recognize handwriting. After coming to the United States in 1970, he expanded the field of machine learning, creating applications in which computers could execute a form of reasoning, drawing conclusions from information supplied to them. "He was a pioneer in this field," said James S. Trefil, a GMU physicist and writer. Dr. Michalski's specialty of machine learning is similar to but distinct from artificial intelligence. The underlying purpose of much of his work was to use computers to recognize patterns that could ease the decision-making process in seemingly unrelated systems.

Before Fortran, computers had to be meticulously "hand-coded" -- programmed in the raw strings of digits that triggered actions inside the machine. Fortran was a "high-level" programming language because it abstracted that work -- it let programmers enter commands in a more intuitive system, which the computer would translate into machine code on its own. The breakthrough earned Mr. Backus the 1977 Turing Award from the Association for Computing Machinery, one of the industry's highest accolades. The citation praised his "profound, influential, and lasting contributions." Mr. Backus also won a National Medal of Science in 1975 and the 1993 Charles Stark Draper Prize, the top honor from the National Academy of Engineering. "Much of my work has come from being lazy," Mr. Backus told Think, the IBM employee magazine, in 1979.

BARCELONA -- Ford's Motor's CEO stressed privacy and security when asked to weigh in on whether Apple should hack into a killer's iPhone, aligning with concerns big tech companies have raised on the issue. CEO Mark Fields' stance is fitting since one of the key issues driving his appearance at Mobile World Congress this week is Ford's continuing goal of being viewed as both automotive company and a tech-driven mobility company. "We're watching the (Apple) situation closely," Fields said during an interview with USA TODAY. "Our view as a company is we take the security and privacy of our customer data when they share it with us very carefully. We want to be trusting stewards for that data and we're committed to protecting it."

Mike Rhodin is senior vice president of IBM Watson. DETROIT -- IBM Watson initially won fame as the artificially intelligent computer system that won $1 million for whipping former Jeopardy! Since then, under the leadership of 1984 University of Michigan graduate Mike Rhodin, Watson has morphed into a muscular big business with lots of tentacles and more than 2,000 employees. Earlier this month in Ann Arbor, I interviewed Rhodin, the New York-based senior vice president of IBM Watson who was in town to speak with two groups of University of Michigan business students and budding entrepreneurs. Rhodin smiled when I asked the sci-fi question he hears often: When will machines turn on humans and take over the world?

Demis Hassabis has a modest demeanour and an unassuming countenance, but he is deadly serious when he tells me he is on a mission to "solve intelligence, and then use that to solve everything else". Coming from almost anyone else, the statement would be laughable; from him, not so much. Hassabis is the 39-year-old former chess master and video-games designer whose artificial intelligence research start-up, DeepMind, was bought by Google in 2014 for a reported $625 million. He is the son of immigrants, attended a state comprehensive in Finchley and holds degrees from Cambridge and UCL in computer science and cognitive neuroscience. A "visionary" manager, according to those who work with him, Hassabis also reckons he has found a way to "make science research efficient" and says he is leading an "Apollo programme for the 21st century". He's the sort of normal-looking bloke you wouldn't look twice at on the street, but Tim Berners-Lee once described him to me as one of the smartest human beings on the planet. Artificial intelligence is already all around us, of course, every time we interrogate Siri or get a recommendation on Android. And in the short term, Google products will surely benefit from Hassabis's research, even if improvements in personalisation, search, YouTube, and speech and facial recognition are not presented as "AI" as such. "It's just stuff that works.") In the longer term, though, the technology he is developing is about more than emotional robots and smarter phones.