For decades Hollywood has treated computers as magic boxes from which endless plot points could be conjured, in denial of all common sense. TV and movies depicted data centers accessible only through undersea intake valves, cryptography that can be cracked through a universal key, and e-mails whose text arrives one letter at a time, all in caps. "Hollywood hacker bullshit," as a character named Romero says in an early episode of Mr. Robot, now in its second season on the USA Network. "I've been in this game 27 years. Not once have I come across an animated singing virus."

Not long ago, a startup founder in San Francisco was trying to organise a meeting with someone visiting from Europe, and setting a time required dozens of e-mails back and forth. The European arrived with a bottle of wine for the founder's personal assistant, Clara, as a gesture of thanks for putting up with the scheduling hassle. But the assistant could not accept the gift. Clara is a software service from a startup of the same name that helps schedule meetings via e-mail. It is powered by artificial intelligence (AI), with some human supervision.

While running a digital marketing agency, Neerav Parekh regularly updated his clients on their campaign performance with reports and charts that were carefully put together. However, the clients were quickly snowed under the blizzard of data, and inevitably demanded that account managers personally visit them and take them through these reports. This was a laborious process and, rather than plod through it repeatedly, Parekh, a serial entrepreneur, turned to artificial intelligence (AI), the science of trying to make computers think and act like humans, for a solution. His product, Phrazor, is aimed at automating the process of interpreting data and communicating insights. Having used Phrazor for his agency, Parekh has now sought to extend the reach of his product.

Is your child's favorite toy a spy in disguise? Complaints filed by consumer watchdogs in the United States and Europe claim that so-called smart toys are in violation of privacy and data protection laws. Toys including My Friend Cayla, i-QUE Intelligent Robot and Hello Barbie are the target of the watchdogs, who claim the internet-connected devices targeted to kids are recording and collecting audio without limitations. The issues being raised come from the European Consumer Organization BEUC and U.S. groups including the Electronic Privacy Information Center (EPIC), which have filed their complaints to authorities in France and across Europe as well as with the U.S. Federal Trade Commission. "The toys subject young children to ongoing surveillance and are deployed in homes across the United States without any meaningful data protection standards," the complaint from EPIC and other U.S. watchdogs said.

Apple, the most the secretive member of the technology industry, is starting to break down its walls. Russ Salakhutdinov, the company's director of artificial intelligence research, says Apple will start letting its AI researchers publish papers and engage with the greater AI community, according to a report from Business Insider. Salakhutdinov reportedly made the announcement earlier today at the NIPS conference in Barcelona, Spain. The goal, according to Salakhutdinov, is to help attract and retain top talent in the industry. Unlike some areas in tech Apple can afford to keep behind closed doors, AI thrives on open collaboration. Many of the top AI researchers routinely attend industry conferences, publish papers, and contribute to open-source projects with mission statements geared toward the safe and careful development of machine intelligence.

Uber is creating a new AI research lab dedicated to exploring the frontiers of machine learning and applying key advances to its business. The lab will be based in Silicon Valley and will be led by Gary Marcus, a professor at NYU and the CEO of Geometric Intelligence, a company Uber is acquiring for an undisclosed sum. The Uber AI lab will also employ another big-name AI researcher, Zoubin Ghahramani, who will retain a part-time post as a professor at the University of Cambridge in the U.K. The company's other cofounders are Ken Stanley, an associate professor at the University of Central Florida, and Doug Bemis, a recent NYU graduate with a PhD in neurolinguistics. The new lab will have 15 founding members, and it will explore a range of fundamental challenges, including developing forms of machine learning that need less data; training AI systems using not only data but also explicit rules; and designing machine-learning systems that explain their decisions. Advances in these areas could be vital to self-driving cars but might also help improve Uber's existing business by, for instance, helping route cars or match customers in an Uber pool more efficiently.

Traditionally, optimization of task allocation problems considered only the costs involved in the allocation. However, there has been in recent years more attention to cases where cost should not always be the sole consideration (Campbell et al. 2008). There are circumstances when other criteria need to be taken into account as well during the decision making process. Fairness has been considered as one of the important additional criteria in many application domains (Ogryczak et al. 2005, Gopinathan and Li 2011, Bertsimas et al. 2012). Although there is no common definition for the term, there are two fairness criteria that are often used in the literature: the Nash bargaining criterion and the Rawlsian maximin criterion. The former is based on Nash's four axioms of pareto optimality, independency of irrelevant alternatives, symmetry, and invariance to affine transformations or equivalent utility representations (Nash 1950). The latter is based on Rawls' two principles of justice (Rawls 1971). Rawls' maximin criterion maximizes the welfare level of the worst-off group member and has therefore been used in allocation problems (Jaffe 1981, Kumar and Kleinberg 2000).

Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on weight decay and DropOut, result in impressive performance when a very large training set is available. However, they tend to have large problems overfitting to small data sets. Indeed, the available regularization methods deal with the complexity of the network function only indirectly. In this paper, we study the feasibility of directly using the $L_2$ function norm for regularization. Two methods to integrate this new regularization in the stochastic backpropagation are proposed. Moreover, the convergence of these new algorithms is studied. We finally show that they outperform the state-of-the-art methods in the low sample regime on benchmark datasets (MNIST and CIFAR10). The obtained results demonstrate very clear improvement, especially in the context of small sample regimes with data laying in a low dimensional manifold. Source code of the method can be found at \url{https://github.com/AmalRT/DNN_Reg}.

Microsoft to turn millions of PCs and tablets into smart'home hubs' to take on Amazon's Echo and Google's Home Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Both can do everything from control lights to answer questions Microsoft plans to take them on with a new Home Hub app for Windows PCs Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold The app will turn the millions of Windows PCs, laptops and tablets already in homes into a'home hub' that can do everything from control lights to answer questions. Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen...

I don't normally post about politics (I'm not particularly savvy about polling, which is where data science has had the largest impact on politics). But this weekend I saw a hypothesis about Donald Trump's twitter account that simply begged to be investigated with data: Every non-hyperbolic tweet is from iPhone (his staff). Every hyperbolic tweet is from Android (from him). When Trump wishes the Olympic team good luck, he's tweeting from his iPhone. Is this an artifact showing which tweets are Trump's own and which are by some handler?