Tony Tether has headed up the Pentagon's way-out research arm, Darpa, since 2001. That makes him the longest-serving director in the agency's nearly 50-year history. He sat down with me for an interview in his office, on the top floor of a blandly menacing Northern Virginia office building, last December. For my story in the March issue of Wired (online next Tuesday), Tether and I talked about everything from bio-terrorists to zombie rodents to thinking machines to the golf courses in Iraq. Let's start with the big picture and talk a little bit about 9/11 -- it'?s been five years now -?-? and how, obviously, that has affected defense research hugely. What do you see as Darpa's big contributions to the war on terror? What do you think has been contributed so far, and what do you think is on the horizon that might be the most valuable? Tony Tether: We have several efforts in use in Iraq and Afghanistan today. There's been somewhat of a misunderstanding that when 9/11 unfolded that Darpa suddenly turned totally toward supplying things for the war. Now, of course, the war made us a great deal more interested in trying to find out what the issues and problems were over there so that we could develop programs along that line. Those programs are long-range and, for the most part, they'?re things that won't really come to fruition for several years. On the other hand, Darpa had started many things in the '90s, because we've been looking at this global terrorist war since probably 1994. At that time we called it the transnational threat –?? you know, a threat without a country. At the same time, there was a great push to look at the way our forces were developed and move them from huge divisions, force on force, to small units of action, back to the squad… As it turned out, 2001 came and we went into war in both Afghanistan and Iraq, and, after the major conflict in Iraq, really small units became the way we were orchestrating that war. And it was probably that way from the very beginning in Afghanistan. So the technologies that we have been developing for four or five years, some of them were already ready to go. TT: One of the major things we knew a small unit would need, especially in a city, was situational awareness. So we developed â?? we already had been developing -- a small platform that we call Wasp.

Do you think AI will decrease human labor? So if foreseeable technologies materialize, then then the need for human labor could decrease. Technology always puts existing jobs under strain. This doesn't immediately mean that human labor as a whole is under threat. Generally, other professions grow to fill the loss, often creating more jobs than the ones that are lost.

In April 2008 I wrote "Where Have All the Great Programmers Gone?" . In trying to answer the question, I contrasted the contemporary introduction to programming with the way it was learned in the 1950s. The first was that one of the prerequisites for being a promising oddball was that of having a nontrivial college degree. Examples were philosophy (J. A. Robinson), English literature (Mark Halpern), Classics (C. A. R. Hoare), Physics (E.W. Dijkstra). Being thrown into the deep end in this way was educative, something that cannot be said of the typical first-year programming text, dumbed down in the way that only Educators have the secret of. A Programmer's Place (APP) has yet to snag Hoare or Halpern, but was fortunate to find J.A. Robinson available for an interview.

The Xerox Palo Alto Research Center, which most people refer to as Xerox PARC, is one of the most fabled institutions in Silicon Valley. PARC is also where Steve Jobs and early Apple engineers took inspiration for aspects of the Macintosh computer. Today, Xerox PARC remains an active operation with a host of commercial clients, focused in areas that include printed electronics, data and analytics, cleantech, and contextual intelligence. During the conversation, we talked about innovation, managing brilliant researchers, and the critical importance of user experience. One of the fascinating parts of our discussion was a window Steve provided into PARC's work on the Internet of Things.

Even by the standards of the TED conference, Henry Markram's 2009 TEDGlobal talk was a mind-bender. He took the stage of the Oxford Playhouse, clad in the requisite dress shirt and blue jeans, and announced a plan that--if it panned out--would deliver a fully sentient hologram within a decade. He dedicated himself to wiping out all mental disorders and creating a self-aware artificial intelligence. And the South African–born neuroscientist pronounced that he would accomplish all this through an insanely ambitious attempt to build a complete model of a human brain--from synapses to hemispheres--and simulate it on a supercomputer. Markram was proposing a project that has bedeviled AI researchers for decades, that most had presumed was impossible. He wanted to build a working mind from the ground up. In the four years since Markram's speech, he hasn't backed off a nanometer. The self-assured scientist claims that the only thing preventing scientists from understanding the human brain in its entirety--from the molecular level all the way to the mystery of consciousness--is a lack of ambition. If only neuroscience would follow his lead, he insists, his Human Brain Project could simulate the functions of all 86 billion neurons in the human brain, and the 100 trillion connections that link them.

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.