As cheap cloud computing services erode IBM's traditional hardware business with alarming speed, the company finds itself facing an uncertain future. If only there were some clever machine it could turn to for advice. Appropriately enough that's what a large part of IBM's research division is trying to create, by building on the research effort that led to Watson, the computer that won in the game show Jeopardy! in 2011. The hope is that this effort will lead to software and hardware that can answer complex questions by looking through vast amounts of information containing subtle and disparate clues. "We're betting billons of dollars, and a third of this division now is working on it," John Kelly, director of IBM Research, said of cognitive computing, a term the company uses to refer to artificial intelligence techniques related to Watson.

Demis Hassabis started playing chess at age four and soon blossomed into a child prodigy. At age eight, success on the chessboard led him to ponder two questions that have obsessed him ever since: first, how does the brain learn to master complex tasks; and second, could computers ever do the same? Now 38, Hassabis puzzles over those questions for Google, having sold his little-known London-based startup, DeepMind, to the search company earlier this year for a reported 400 million pounds ($650 million at the time). Google snapped up DeepMind shortly after it demonstrated software capable of teaching itself to play classic video games to a super-human level (see "Is Google Cornering the Market on Deep Learning?"). At the TED conference in Vancouver this year, Google CEO Larry Page gushed about Hassabis and called his company's technology "one of the most exciting things I've seen in a long time."

Would you buy a self-driving car that couldn't drive itself in 99 percent of the country? Or that knew nearly nothing about parking, couldn't be taken out in snow or heavy rain, and would drive straight over a gaping pothole? If your answer is yes, then check out the Google Self-Driving Car, model year 2014. Of course, Google isn't yet selling its now-famous robotic vehicle and has said that its technology will be thoroughly tested before it ever does. But the car clearly isn't ready yet, as evidenced by the list of things it can't currently do--volunteered by Chris Urmson, director of the Google car team. Google's cars have safely driven more than 700,000 miles.

After catching the world and the auto industry by surprise with its progress with self-driving cars, Google has begun the latest, most difficult phase of its project – making the vehicles smart enough to handle the chaos of city streets. But while the company describes its work with its typical tight-lipped optimism, academic experts in robotics are cautious about the prospects of fully autonomous vehicles. They estimate it will be decades until they can perform as well as human drivers in all situations – if they ever do at all. Google's cars make extensive use of detailed maps that describe not only roads and restrictions such as speed limits, but the 3-D location of stop lights and curbstones to within inches. The company is now working to make its vehicles capable of seeing and understanding the kind of unexpected obstacles that don't appear on those maps and are particularly common in urban areas, said Chris Urmson, the director of the project, last week.

To create a computer as powerful as the human brain, perhaps we first need to build one that works more like a brain. Today, at the International Joint Conference on Neural Networks in Dallas, IBM researchers will unveil a radically new computer architecture designed to bring that goal within reach. Using simulations of enormous complexity, they show that the architecture, named TrueNorth, could lead to a new generation of machines that function more like biological brains. The announcement builds on IBM's ongoing projects in cognitive computing. In 2011, the research team released computer chips that use a network of "neurosynaptic cores" to manage information in a way that resembles the functioning of neurons in a brain (see "IBM's New Chips Compute More Like We Do").

The retina is an enormously powerful tool. It sorts through massive amounts of data while operating on only a fraction of the power that a conventional digital camera and computer would require to do the same task. Now, engineers at a company called iniLabs in Switzerland are applying lessons from biology in an effort to build a more efficient digital camera inspired by the human retina. Like the individual neurons in our eyes, the new camera--named the Dynamic Vision Sensor (DVS)--responds only to changes in a given scene. This approach eliminates large swaths of redundant data and could be useful for many fields, including surveillance, robotics, and microscopy.

A woman who is completely paralyzed below the neck has regained the ability to reach out and interact with the world around her thanks to the most advanced brain-computer interface for operating a robotic arm so far. Each chip has 96 electrodes and is wired through the skull to a computer that translates her thoughts into signals for the robotic arm. The work, performed by researchers from the University of Pittsburgh, is reported in the latest issue of The Lancet. The work is the latest advance to show how brain-controlled interface technology can restore some movement to quadriplegics. In May of this year, researchers at Brown University described how a paralyzed patient could use a robotic limb to perform basic tasks, including giving herself a drink of coffee (see "Brain Chip Helps Quadriplegics Move Robotic Arms with Their Thoughts").

One night in 1982, John Mumford was working on an avalanche patrol on an icy Colorado mountain pass when the van carrying him and two other men slid off the road and plunged over a cliff. The other guys were able to walk away, but Mumford had broken his neck. The lower half of his body was paralyzed, and though he could bend his arms at the elbows, he could no longer grasp things in his hands. Fifteen years later, however, he received a technological wonder that reactivated his left hand. It was known as the Freehand System. A surgeon placed a sensor on Mumford's right shoulder, implanted a pacemaker-size device known as a stimulator just below the skin on his upper chest, and threaded wires into the muscles of his left arm.

The benefits of any truly transformative technology are at first exaggerated, but their long-term effects surprise everyone. At the moment, mesh networks are experiencing such misvaluation. Their promoters (and they are many) now describe them with hyperbolic enthusiasm; but in the end they will be the mechanism by which machine intelligence becomes like electricity – that is, invisible and ubiquitous. Mesh networks are not so very new: their conceptual lineage dates back to packet radio, a kind of digital data transmission used by amateur radio hackers in the 1970s. But investments in more reliable and intelligent networks made during the 1990s by the U.S. Department of Defense renewed interest in meshes; and within the last five years, academic institutions like MIT's Media Lab and startups like Aeria, BelAir Networks, Ember, MeshNetworks (now owned by Motorola), and Tropos Networks have rapidly advanced the technology.

A pug-size robot named pioneer slowly rolls up to the Captain America action figure on the carpet. They're facing off inside a rough model of a child's bedroom that the wireless-chip maker Qualcomm has set up in a trailer. The robot pauses, almost as if it is evaluating the situation, and then corrals the figure with a snowplow-like implement mounted in front, turns around, and pushes it toward three squat pillars representing toy bins. Qualcomm senior engineer Ilwoo Chang sweeps both arms toward the pillar where the toy should be deposited. Then it rolls back and spies another action figure, Spider-Man. This time Pioneer beelines for the toy, ignoring a chessboard nearby, and delivers it to the same pillar with no human guidance.