Ask not what the government can do for Silicon Valley; ask what Silicon Valley can do for the government. He presented WIRED with six challenges he feels the tech industry needs to address--just a few earthshaking problems the country could use some help with, that's all. We reached out to six of the biggest names in the WIRED world, and we gave each of them a challenge from the president's list. Then we asked: To get this done, what's the industry's best play? Silicon Valley runs on stories. So does the economy in general. We create what we believe in. If we believe we can use technology to identify and solve big problems, then that's what we'll do.

Hitachi Appliances Inc., a unit of Hitachi Ltd., will launch a robotic vacuum cleaner that it says can clean hard-to-reach areas. At only 25 centimeters wide, the product named minimaru is smaller than rival models and can reach where others cannot, such as spaces between furniture and in the corners of a room, Hitachi Appliances said. It will hit stores on Nov. 19. Sales in Japan totaled some 200,000 units in fiscal 2015, a 2.3-fold expansion from five years earlier, the company said. It moves swiftly and has sensors that help it to avoid hitting the walls and furniture. It is easy to care for, as it has brushes that clean themselves automatically, Hitachi Appliances said.

Mizuho Securities will offer institutional investors a trading system guided by artificial intelligence that predicts the optimal time to execute stock transactions, a tool that could help shore up pension and mutual fund returns. The AI developed by the Mizuho Financial Group unit will look at such data as outstanding orders, trading volume and past price changes to forecast how a given stock will move in 30 minutes to an hour. The system will execute an order earlier if it expects the price to rise, or later if it predicts a drop. The service is slated to launch as early as late November. It will start out with the top 500 stocks by market capitalization on the first section of the Tokyo Stock Exchange, which are particularly heavily traded, and expand later.

"If you want to know where to make money over the next two decades, look for companies that are finding ways to automate jobs that are currently being done by humans...that you wouldn't have thought previously could be done by a machine. Truck drivers are one thing and Google as well as Tesla have a great head-start in disrupting that market, but lawyers, doctors, teachers, customer service and sales reps – there are companies that are turning these professions into lines of code, and they're going to make a lot of money." Customer Relationship Management (CRM) software is a roughly 25bn a year market today and Gartner projects that it will be the fastest growing enterprise SaaS segment over the next few years, reaching over 40bn in annual spend in 2019. The importance of this market is being underscored by the all-out war between tech titans Microsoft, Salesforce, and Oracle who have already spent close to 40bn in the past two months on CRM-related acquisitions including LinkedIn ( 26.2bn cash), NetSuite ( 9.3bn cash), and Demandware ( 2.8bn stock). Companies today are striving to leverage what is rapidly approaching the zettabyte scale data loads that customers are uploading to the cloud every year, and most CEOs understand that getting a better customer 360 will be a key driver of their firms' success.

Apple is making a visible push in the direction of AI today by hiring Carnegie Mellon University professor Ruslan Salakhutdinov for what appears to be a newly minted position: director of AI research. Salakhutdinov, who announced his new position on Twitter, works in the fields of deep learning and neural networks; he's published dozens of papers in the last couple of years alone. The topics he's worked on run the gamut, but the main thread is one of human-like understanding of various media: recognizing objects in images, actions in videos, and so on. We are looking for exceptional hands-on research scientists with a proven track record in a variety of machine learning methods; from the realms of deep learning, reinforcement learning, unsupervised learning, and computer perception. You will be joining a world-class, multidisciplinary team and will be participating in cutting-edge research in deep learning, machine intelligence, and artificial intelligence.

Outside a large warehouse in Pittsburgh, in an area along the Allegheny River that was once home to dozens of factories and foundries but now has shops and restaurants, I'm waiting for a different kind of technological revolution to arrive. I check my phone, look up, and notice it's already here. A white Ford Fusion, its roof bedazzled with futuristic--looking sensors, is idling nearby. Two people sit up front--one monitoring a computer, the other behind the wheel--but the car is in control. I hop in, press a button on a touch screen, and sit back as the self-driving Uber takes me for a ride. As we zip out onto the road toward downtown, the car stays neatly in its lane, threading deftly between an oncoming car and parked trucks that stick out into the street.

The branch of computer science which deals with study of developing machines which work and react as humans is known as Artificial Intelligence. The research on this field has become very essential for Technology Industry. Artificial Intelligence is not just a science fiction but it has become a reality today, though many of us are not aware of it, but in some or the other way we are using it which sounds surprising. We are already using it in video games from long, though the intensity and role of intelligence has been commendably increased in some years. The game characters learn player's behaviour and then respond to the gaming situation in a way you would have acted in real.

Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could try to learn from past inferences, so that future inferences run faster. This strategy is known as amortized inference; it has recently been applied to Bayesian networks and deep generative models. This paper proposes a system for amortized inference in PPLs. In our system, amortization comes in the form of a parameterized guide program. Guide programs have similar structure to the original program, but can have richer data flow, including neural network components. These networks can be optimized so that the guide approximately samples from the posterior distribution defined by the original program. We present a flexible interface for defining guide programs and a stochastic gradient-based scheme for optimizing guide parameters, as well as some preliminary results on automatically deriving guide programs. We explore in detail the common machine learning pattern in which a 'local' model is specified by 'global' random values and used to generate independent observed data points; this gives rise to amortized local inference supporting global model learning.

Dynamic text networks have been widely studied in recent years, primarily because the Internet stores textual data in a way that allows links between different documents. Articles on the Wikipedia (Hoffman et al., 2010), citation networks in journal articles (Moody, 2004), and linked blog posts (Latouche et al., 2011) are examples of dynamic text networks, or networks of documents that are generated over time. But each application has idiosyncratic features, such as the structure of the links and the nature of the time varying documents, so analysis typically requires bespoke models that directly address those aspects.

Driven by a declining population, a trend for developing robotic babies has emerged in Japan as a means of encouraging couples to become'parents'. The approaches taken vary widely and are driven by different philosophical approaches that also beg a number of questions, not least whether these robo-tots will achieve the aim of their creators. To understand all of this it is worth exploring the reasons behind the need to promote population growth in Japan. The new Kirobo Mini robot from Toyota (pictured) can't do much but chatter in a high-pitched voice, but supposedly has the smarts of a five-year-old Studies have found that humans and robots can share a'high degree of bonding,' especially with social robots that have a human-like appearance. But, humans may find certain realistic human qualities unappealing.