"When you are born, you know nothing." This is the kind of statement you expect to hear from a philosophy professor, not a Silicon Valley executive with a new company to pitch and money to make. A tall, rangy man who is almost implausibly cheerful, Hawkins created the Palm and Treo handhelds and cofounded Palm Computing and Handspring. His is the consummate high tech success story, the brilliant, driven engineer who beat the critics to make it big. Now he's about to unveil his entrepreneurial third act: a company called Numenta. But what Hawkins, 49, really wants to talk about -- in fact, what he has really wanted to talk about for the past 30 years -- isn't gadgets or source codes or market niches.

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."

From airport tarmacs to online job banks to medical labs, artificial intelligence is everywhere. In the 1960s, cyberneticians predicted that machines would be smarter than people within 20 years. Artificially intelligent computers would be building cities on Mars and solving diplomatic crises at home. But somewhere between promise and production, the fantasy got derailed. AI came to mean a computer that could beat the pants off most chess players, but not one that could vacuum the living room or understand why it's a bad idea to spread toothpaste on toast.

Sure, there are some morally and ethically questionable aspects of an unstoppable privatized security bot, but the armor and cyborgian capabilities are pretty freaking awesome. Why? Turns out we just don't have the battery power to operate a suit with that many moving parts for any length of time. Most iPhones barely last a full day on a charge, and a Tesla Model S can only make it about 300 miles before it needs to be plugged in--and that battery weighs more than 1,300 pounds (and is likely incapable of keep a human's organs running while being shot at). "That's one serious limitation that our technology is not approaching yet," says Charles Higgins, an electrical engineer and professor of neuroscience at the University of Arizona. "In order to do a real RoboCop like you see in the movie, you need to have a very compact power source that's going to power all those motors all day--it doesn't look like RoboCop has to plug in every hour."

A 6-foot-tall, one-armed robot named Stair 1.0 balances on a modified Segway platform in the doorway of a Stanford University conference room. It has an arm, cameras and laser scanners for eyes, and a tangle of electrical intestines stuffed into its base. From his seat at a polished table, roboticist Morgan Quigley sends the bot on a mission. "Stair, please fetch the stapler from the lab." After the third attempt, Stair responds in an inflectionless voice: "I will go fetch the stapler for you."

LOS ANGELES--Will.i.am sounds congested and subdued, his batteries running low after a high-octane appearance on The Ellen Show earlier that day. He's longing for 2025, when an artificial intelligence will be able to tell him to take it easy. "Will, you sound like you are stuffy," he says in the voice of this fictional AI. "What did you eat recently? Stay away from sweets because they are causing you more mucus. You should get some rest tonight. I'm going to cancel your appointments from 7 pm. I've already bought you some epsom salts from CVS. Go and pick them up."

In an age when technology companies routinely introduce new forms of everyday magic, one problem that remains seemingly unsolved is that of long-form transcription. Sure, voice dictation for documents has been conquered by Nuance's Dragon software. Our phones and smart home devices can understand fairly complex commands, thanks to self-teaching recurrent neural nets and other 21st century wonders. However, the task of providing accurate transcriptions of long blocks of actual human conversation remains beyond the abilities of even today's most advanced software. When solved on a broad scale, it is a problem that might unlock vast archives of oral histories, make podcasts easier to consume for speed-readers (tl;dl), and be a world-changing boon for journalists everywhere, liberating precious hours of sweet life.