Google DeepMind took a leap forward last year when its artificial intelligence agent mastered 49 Atari 2600 games. The learning system, or "deep Q-network" (DQN), that DeepMind designed achieved this mastery through general experience, rather than specific programming for each game. This milestone is just one step along a grander path toward the general-purpose "smart machine": an AI that can master any task with minimal input. DeepMind's work in this field is groundbreaking, and it's helping advance the field in ways you might not expect. Wojciech Jaśkowski is an assistant professor at the Institute of Computing Science (ICS) at Poznan University of Technology, Poland.

Google argues that driverless cars improve highway safety while enabling lower federal spending on roads, buses, and trains. The government should support driverless cars because they will allow it to spend less on roads, public transport, and parking, Google will tell US law makers on Tuesday. A month after Google recorded its first own-fault accident in a self-driving car, Chris Urmson, head of the project at Google, will urge senators to allow the US Department of Transport to clear a path for the vehicles because they're safer and will ultimately cost the government less. Exactly what impact driverless cars will have on roads is still being debated. Some believe they will cut congestion due to the potential for car-sharing.

Dan Goldin, who led the National Aeronautics and Space Administration (NASA) in the 1990s, officially unveiled a company with more than $100 million in private investment to focus on neural computing. The company KnuEdge was founded in 2005, but its two divisions -- one focused on voice biometrics and the other on data center neural computing -- worked in stealth mode to bring its first two products to maturity. KnuEdge boasts that its voice recognition and authentication product, called KnuVerse, is military-grade technology that spent five years in production in "mission-critical battlefield conditions." It's now available for enterprises interested in building human-voice interfaces that can function in noisy, real-world environments. It can be used to build authentication systems into computers, web or mobile apps and IoT devices, relying on just a few words in any language.

Though ultimately a problem for philosophers and machine-learning specialists, we can answer this question using NASA's Curiosity rover with a resounding "Yes." Since landing on the Red Planet a year ago, our silicon-based envoy has been exploring, experimenting, and, for the most part, photographing Mars. It has produced pictures at an incredible rate, often nearly 1,000 per day, and its raw image gallery is currently overflowing with more than 71,000 photos. Curiosity is armed with an impressive array of cameras and the continual snaps it sends back to mission control help scientists and engineers decide where the rover should go next and what rocks to prod. But for the rest of us watching the mission closely, the pictures provide a portal that goes straight to Mars.

President Obama officially announced a new brain research initiative in a press conference at the White House this morning, something he first hinted at in his State of the Union address in February. In its first year, the project would devote roughly $100 million in public funding and a similar amount from private foundations, to develop new tools for mapping neural circuits. "The human brain is at the present time the most complicated organ in the known universe," Francis Collins, the director of the National Institutes of Health, told reporters on a conference call this morning. Understanding how circuits of neurons contribute to the complex properties of the brain and how they break down in disease is one of the biggest scientific challenges of our time, Collins said. "We aim through this very ambitious project, some might even call it audacious, to begin to unravel those mysteries."

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."

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.

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.