But lately, it has been aiming much more directly at building HAL, or what's sometimes called the Google Brain. As I wrote in a recent article, a fast-emerging branch of artificial intelligence called deep learning is helping Google and other companies and researchers produce significant advances in machines that at least approach the way we think. It won't be long--for better or worse--before their work also has a profound impact on marketing and advertising as well. Deep-learning software tries to emulate, albeit in a still primitive way, the activity in layers of neurons in the neocortex, the part of the human brain where thinking occurs. The software, part of artificial neural networks that simulate the neocortex's large array of neurons, learns quite literally to recognize patterns in digital representations of sounds, images, and other data.

Self-driving cars (SDCs) have been getting a lot of buzz recently. Last month, Nissan showcased their autonomous car at the Detroit Auto Show, re-asserting their plan to make SDCs available to the general public by 2020. Meanwhile, the Ford Motor Company announced the formation of a partnership with researchers from M.I.T. and Stanford University. Together they will continue the company's effort in developing systems that will allow SDCs to not only react to obstacles but better anticipate where objects (vehicles, pedestrians, animals, etc.) will move next. "Our goal is to provide the vehicle with common sense," said Greg Stevens, Ford's global manager for driver assistance.

When I arrived at a Stanford University auditorium Tuesday night for what I thought would be a pretty nerdy panel on deep learning, a fast-growing branch of artificial intelligence, I figured I must be in the wrong place--maybe a different event for all the new Stanford students and their parents visiting the campus. Despite the highly technical nature of deep learning, some 600 people had shown up for the sold-out AI event, presented by VLAB, a Stanford-based chapter of the MIT Enterprise Forum. The turnout was a stark sign of the rising popularity of deep learning, an approach to AI that tries to mimic the activity of the brain in so-called neural networks. In just the last couple of years, deep learning software from giants like, Facebook, and China's Baidu as well as a raft of startups, has led to big advances in image and speech recognition, medical diagnostics, stock trading, and more. "There's quite a bit of excitement in this area," panel moderator Steve Jurvetson, a partner with the venture firm DFJ, said with uncustomary understatement.

A computer that taught itself to play almost 50 video games including Space Invaders and Pong is being hailed as the pinnacle of artificial intelligence. But it is unlikely to spark the Terminator-like Armageddon predicted in recent months by technology entrepreneur Elon Musk (who provided early funding for the project) and physicist Stephen Hawking. Despite mastering more than half the classic Atari 2600 games, the program – deep Q-network (DQN), developed by DeepMind Technologies – struggled with more difficult challenges, such as, well, Pac-Man. "On the face of it, it looks trivial in the sense that these are games from the '80s and you can write solutions to them quite easily," said Dr Demis Hassabis, the vice-president of engineering at DeepMind, a British company acquired by a year ago for a reported £400m (US$650m). Never before has a computer taught itself how to do a range of complex operations, said Dr Hassabis, one of the company's co-founders.

Cognitive Code CEO, Leslie Spring, is seeing his world explode, in a good way. Since the emergence of Siri, the world has awoken to the realities of how artificial intelligence will take on a major role in the way we search and organize our lives. Spring, who's been building his company to provide the tools for an AI-powered space, is now seeing his work in voice/data recognition and computation taken seriously--very seriously. According to the company, SILVIA can interpret speech, text or other input, interact with appropriate applications or operating systems, all the while interacting with the user. What sets SILVIA apart from other AI, speech recognition or other products that she competes with is that the product is not necessarily server-based, operating from the cloud, as an app (yes, running natively on mobile devices too), as part of a network or, if you really want, from a server.

"My vision is to give people immediate gratification in healthcare," says Ron Gutman the high-energy and affable founder and CEO of HealthTap, a mobile health platform that connects customers with trusted health information and doctors in near real-time at any given time of day or day of the week from a network of over 50,000 U.S.-licensed doctors. "We call people'patients' but they are anything but patient when they are in pain or desperate to talk to a doctor during the day or in the middle of the night," continues Gutman. In creating a customer–focused health information service, Gutman sees the opportunity to build the world's first global mobile health brand. There is nothing small about Ron's ambitions or business opportunity. HealthTap looks to disrupt the model and bring high-quality information directly to the customer, instantly through their mobile devices for free or very modest costs.

If you want a notion of how easy it would be for terrorists to slip a dirty nuke, a canister of nerve gas or a bundt cake of C4 through a container port, consider this statistic from the Brookings Institution in Washington: If the newly renamed U.S. Customs & Border Protection agency were to inspect all incoming containers, it would need to spend $50 billion a year. The Customs inspection budget is $2.3 billion. Currently, American authorities physically search only 2% of them. There's no reason to think things are too different elsewhere. "You ought to be damn afraid," says Thomas Sheets, chairman of the National Cargo Security Council trade group in the U.S. and director of corporate security services at Palo Alto, California-based CNF, a logistics company.

While consumers are marveling at this year's new tablets and smartphones, researchers are hard at work developing the next wave of computer technologies that will change our lives. For starters, parallel computing is yielding extraordinary results. The once-experimental technology that allows computers to process multiple problems simultaneously is being used to create new breakthroughs in graphics rendering, language translation and even facial recognition. Berkeley Par Lab research professor Kurt Keutzer predicts that parallel computing will foster enormous advances in speed and power for every kind of electronics, from videogame consoles to handheld devices. In addition to producing more powerful machines, new research is making it easier to work with computers.

The robots are on the move–leaping, scrambling, rolling, flying, climbing. They are figuring out how to get here on their own. They come to help us, protect us, amuse us–and some even do floors. Since Czech playwright Karel Capek popularized the term ("robota" means "forced labor" in Czech) in 1921, we have imagined what robots could do. But reality fell short of our plans: Honda Motor trotted out its Asimo in 2000, but for now it's been relegated to temping as a receptionist at Honda and doing eight shows a week at Disneyland.

Yoshiyuki Sankai's Hybrid Assisted Limb (yes, that's a nod to the haywire computer in 2001: A Space Odyssey) is suit that gives its wearer superpowers: the ability to lift an extra 88 pounds or walk long distances without tiring. The catch–the suit's battery needs to be charged every five hours. Sankai's Cyberdyne plans to rent HAL suits for $1,000 a month. Irobot founders Colin Angle and Helen Greiner wanted to make robots that made money. The Roomba, their best-known offering, has done that, moving 2 million floor-cleaning units since 2002.