Analyzing queries made to Google, Bing, and other search engines can reveal the potentially dangerous consequences of mixing prescriptions before they are known to the Food and Drug Administration (FDA), according to a new study. Such data mining could even expose medical risks that slip through clinical trials undetected. Pharmaceuticals often have side effects that go unnoticed until they're already available to the public. This is especially true of side effects that emerge when two drugs interact, largely because drug trials try to pinpoint the effects of one drug at a time. Physicians have a few ways to hunt for these hidden risks, such as reports to FDA from doctors, nurses, and patients.

Rosen, a researcher at the Stanford Research Institute in Menlo Park, California, envisioned a roboperson driven by neural networks, algorithms that mimic the human brain. Much like its biological counterparts, it would have the power to see and sense its environment. As Rosen and his team wrote in a memo (.pdf) to DARPA, the Defense Department's research arm, describing the project, it would "perform reconnaissance missions" that would normally require human intelligence. DARPA eventually "got kind of excited about it," recalls Nils Nilsson, one of the leaders of the project, and the agency granted the researchers $750,000 -- more than $5 million in today's money -- to make it happen. The project didn't include true neutral networks -- in the 1960s, the technology just wasn't up to the sort of visual analysis, planning, and navigation Rosen and team wanted to explore -- but the automaton did indeed happen.

There's a theory that human intelligence stems from a single algorithm. The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is -- at its core -- a general-purpose machine that can be tuned to specific tasks. About seven years ago, Stanford computer science professor Andrew Ng stumbled across this theory, and it changed the course of his career, reigniting a passion for artificial intelligence, or AI. "For the first time in my life," Ng says, "it made me feel like it might be possible to make some progress on a small part of the AI dream within our lifetime."

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.

With a souped-up reproducing piano and some ingenious learning machines, AI maestro Gerhard Widmer is discovering how performers unlock the art in Mozart. A gray-blue dusk is settling over the Gothic cathedrals, palatial opera houses, and labyrinthine streets of Vienna's First District. Here in the Austrian capital, music is an almost elemental force. It's a place where very old and very new musical traditions collide and intermingle – the perfect setting for a computer scientist obsessed with examining the blips and fault lines, deviations and inventions, that transform music into something more than code and just slightly less than magic. 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. It's Friday evening, and most of his fellow teachers at the University of Vienna have already gone home, but associate professor Gerhard Widmer is bounding up the stairs of a peach-colored Baroque building and into the offices of the Austrian Research Institute for Artificial Intelligence. He waves hello to his team scientists – Simon Dixon, Emilios Cambouropoulos, and Werner Goebl – and makes for a computer monitor marked EUROPA, which is jacked into an electric piano. Widmer eagerly begins to trigger several audio files.

Compiled by Carl Zimmer (zimmer@panix.com) Natural Language Laboratory, School of Computing Science, Simon Fraser University Burnaby, British Columbia Researchers are devising "relaxed grammars" to extract the sense of transcribed speech through a method known as "partial parsing," which deciphers chunks of language rather than breaking down the structure of entire sentences. Their work is being integrated into closed-captioning technology for real-time TV translations. 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.

As the US war machine develops a digital air force of "unmanned aerial vehicles," it's only a matter of time before fighter planes without fighter jocks joust in some robot dogfight in the sky. "Tumbleweeds cleared" reads the sign by the side of the road, a fair indication of the nature of local enterprise. This is the desert east of Palmdale, California, center of the US high-tech aerospace industry. To the north is Edwards Air Force Base and nearby, Air Force Plant 42, where Northrop hatched the B-2 bomber. A few hundred yards away is Lockheed Skunk Works, birthplace of the F-117 stealth fighter. 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. But now I'm driving past old ranches with corrals jury-rigged from wire and discarded doors, beneath a sky as wide as the plain, looking for a very different kind of airplane.

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. The outcome is a halt in federal funding for machine translation R&D. Darpa launches its Spoken Language Systems (SLS) program to develop apps for voice-activated human-machine interaction. Researchers focus on portable systems for face-to-face English-language business negotiations in German and Japanese.

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.

Running software that took four years and millions of dollars to develop, Carbonell's machine – or rather, the server farm it's connected to a few miles away – is attempting a task that has bedeviled computer scien tists for half a century. The message isn't encrypted or scrambled or hidden among thousands of documents. I brought along the text, taken from a Spanish newspaper transcript of a 2004 al Qaeda video claiming responsibility for the Madrid train bombings, to test Meaningful Machines' automated translation software. The brainchild of a quirky former used-car salesman named Eli Abir, the company has been designing the system in secret since just after 9/11. Now the application is ready for public scrutiny, on the heels of a research paper that Carbonell – who is also a professor of computer science at Carnegie Mellon University and head of the school's Language Technologies Institute – presented at a conference this summer. In it, he asserts that the company's software represents not only the most accurate Spanish-to-English translation system ever created but also a major advance in the field of machine translation. This article has been reproduced in a new format and may be missing content or contain faulty links.