Between cat videos and online banking, the web runs the gamut of wasteful and wonderful when it comes to getting things done. And if you were to weigh the Internet's time wasters against its time-saving tools, it would be like an elephant sitting across a fulcrum from a field mouse. But through all the advances that have come with cloud computing, the Internet is quickly turning into a giant productivity machine. Shopping is great when you're soaking in some retail therapy, but when you're doing it because you have to, it can feel like a major chore. Mezi, a free iOS app, works like a personal assistant that you can text message. Just tell the service what you want--airfare, clothing, dinner reservations, personal electronics--and Mezi's agents scour the web for the best deals, even using coupons if possible to get you a good deal.

Once a secret project, Google's self-driving cars are now out in the open, quite literally, with the company test-driving them on public roads and, on one occasion, even inviting people to ride inside one of the robot vehicles as it raced around a closed course. Google's fleet of robotic Toyota Priuses has now logged more than 190,000 miles (about 300,000 kilometers), driving in city traffic, busy highways, and mountainous roads with only occasional human intervention. The project is still far from becoming commercially viable, but Google has set up a demonstration system on its campus, using driverless golf carts, which points to how the technology could change transportation even in the near future. Stanford University professor Sebastian Thrun, who guides the project, and Google engineer Chris Urmson discussed these and other details in a keynote at the IEEE International Conference on Intelligent Robots and Systems in San Francisco last month. Thrun and Urmson explained how the self-driving car works and showed videos of the road tests, including footage of what the on-board computer "sees" [image below] and how it detects other vehicles, pedestrians, and traffic lights.

No matter how hard they try, brain scientists and cognitive psychologists will never find a copy of Beethoven's 5th Symphony in the brain – or copies of words, pictures, grammatical rules or any other kinds of environmental stimuli. But it does not contain most of the things people think it does – not even simple things such as'memories'. Our shoddy thinking about the brain has deep historical roots, but the invention of computers in the 1940s got us especially confused. For more than half a century now, psychologists, linguists, neuroscientists and other experts on human behaviour have been asserting that the human brain works like a computer. To see how vacuous this idea is, consider the brains of babies. Thanks to evolution, human neonates, like the newborns of all other mammalian species, enter the world prepared to interact with it effectively. A baby's vision is blurry, but it pays special attention to faces, and is quickly able to identify its mother's.

It is an early morning of the just-arrived winter. People I can see on the street from my window wear heavy coats, but it's unclear how cold it is. I can open the window and let my natural skin sensors grab an approximate measurement, but I realize a much more accurate value can be obtained by pressing a button. I ask, with a Brazilian accent that most Americans think is Russian. It is 32 degrees outside," answers the piece of rectangular glass I hold.

The output of the face detector is scaled to 90x90 and fed directly to the facial expression analysis system (see Figure 1). The system is essentially the same as the one used for Automatic FACS coding. First the face image is passed through a bank of Gabor filters at 8 orientations and 9 scales (2-32 pixels/cycle at 0.5 octave steps). The filterbank representations are then channeled to a classifier to code the image in terms of a set of expression dimensions. We have found support vector machines to be very effective for classifying facial expressions (Littlewort et al., in press, Bartlett et al., 2003).

A fascinating account, written in real time, of the unfolding of a scientific discovery: the first detection of gravitational waves. Why our brains aren't built for media multitasking, and how we can learn to live with technology in a more balanced way. An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. A more powerful innovation, which seeks to discover not how things work but why we need things. The new edition of a textbook that combines economic concepts with empirical evidence, updated with material on the Affordable Care Act and other developments.

VERY soon, you may be able to keep a close watch on your home while you------re miles away. And this time, it------s not just about physical changes, say when a person falls or leaves the gas on. This is about technology that will discern and react to the non-physical aspects, say loneliness and fear, in conjunction with the physical aspects. The ultimate goal is to detect changes in behaviour, such as sudden mood swings or a lessening of the ability to comprehend. They represent telltale signs of potential problems, and early detection may allow the problem to be corrected before it spins out of control.

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Whether it's a new employment contract, a rental contract, or sale contract, it needs to be checked before signing. Everyone knows the struggle of working through the dreaded small print, searching for pitfalls hidden in the tiniest details, and trying to make sense out of the bizarre language of law. In fairness to the layman, contract review is also a hustle for lawyers themselves. In 2014, commercial lawyer Noory Bechor got sick of the fact that 80 percent of his work was spent reviewing contracts. He figured the service could be done much cheaper, faster, and more accurately by a computer.

The following graphs highlight venture investing trends into the Artificial Intelligence (AI) sector. The graphics include data through October 2016. The above graph compares the total venture funding in each AI category to the number of companies in the category. The Machine Learning Applications category leads the sector in both metrics, with around $3.5B in total funding and 425 startups. The Smart Robots category leads the sector with around $22M in average funding per company.