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.

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.

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.

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.

Twisted rebar, concrete, and splintered furniture lay scattered across the floor of this room. Our view through a jagged hole in the wall looks out on the city, showing steady civilian traffic crossing a bridge over a river below. 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. An Army major beside me, Paul Tyrrell, scans the high-rises on the other side of the river through his laser rangefinder. He is the frontline eyes of the coalition, responsible for calling in air strikes. A platoon sergeant named Donald Prado tells Tyrrell that an office tower half a mile to the west is an enemy stronghold. Prado radios in for the Air Force to drop a smoke screen for cover.

ROBONAUT Not all NASA robots drive around poking at rocks. Robonaut is the same size and shape as a person in a space suit, so it can handle tasks typically performed by humans – its hands are even better articulated than an astronaut's gloved digits. The fact that it looks like Boba Fett? 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.

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."

As if the debate over immigration and guest worker programs wasn't complicated enough, now a couple of robots are rolling into the middle of it. 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. Vision Robotics, a San Diego company, is working on a pair of robots that would trundle through orchards plucking oranges, apples or other fruit from the trees. In a few years, troops of these machines could perform the tedious and labor-intensive task of fruit picking that currently employs thousands of migrant workers each season.

The Pentagon's blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves -- while making it easier for ordinary schlubs like us to build them, too. That path fell out of favor among computer scientists years ago as a means of creating artificial intelligence; we'd have to understand our own brains first before building a working artificial version of one. But the agency thinks we can build machines that learn and evolve, using algorithms -- "probabilistic programming" -- to parse through vast amounts of data and select the best of it. After that, the machine learns to repeat the process and do it better. But building such machines remains really, really hard: The agency calls it "Herculean."