Vyacheslav is a PhD candidate at the Oxford Internet Institute. His research uses social psychology and machine learning to understand networks of people and networks of ideas. Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past.

"If Trump loses, it will defy the data trend for the first time in the last 12 years since internet engagement began in full earnest." According to a report in the International Business Times (IBT), the artificial intelligence (AI) system that accurately predicted the outcomes of the last three U.S. presidential elections has put Trump ahead of Hillary Clinton in the race for the White House. The AI system called MogIA was created in 2004 by Sanjiv Rai, founder of Indian IT company Genic.ai. The system works by processing 20 million data points from public platforms including Google, Facebook, Twitter, and YouTube, the IBT explained. It then analyzes the information to generate predictions, taking into account data such as engagement with tweets and viewership of Facebook Live videos.

This article was written by Stephanie Kim. Stephanie has a professional experience with data mining and processing including natural language processing along with a small amount of machine learning and script automation. Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let's find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets. This walkthrough will provide a basic introduction to help developers of all background and abilities get started with the NLP microservices available on Algorithmia.

The White House released its much anticipated document last May, entitled "Preparing for the Future of Artificial Intelligence." Sent from the Office of the President and the National Science and Technology Council Committee on Technology (or NSTC), the report is 58 pages of research, documentation, and recommendations on how the United States government plans to respond to artificial intelligence (AI) moving forward. The report was developed by the NSTC's Subcommittee on Machine Learning and Artificial Intelligence "which was chartered in May 2016 to foster interagency coordination, to provide technical and policy advice on topics related to AI, and to monitor the development of AI technologies across industry, the research community, and the Federal Government," according to the report. The NSTC hosted five public workshops, as well as putting out a public Request for Information. The information drawn from those six sources informed the eventual recommendations of the committee.

Comcast Corp. CEO Brian Roberts said Friday that he believes one of the biggest business trends will be "artificial intelligence," in which computers do tasks once done by people, leading to smart cities and smart cars. He didn't say how he thought artificial intelligence could transform Comcast, but he noted that "there's always a dark side to that kind of change." In a question-and-answer format, Roberts spoke conversationally to about 1,200 executives, lawyers, city and state officials, and others at the annual Greater Philadelphia Chamber of Commerce breakfast . Before Roberts' remarks, Drexel University president John Fry officially took over as the chamber's board chairman, replacing Exelon Corp.'s Dennis O'Brien. Fry said he believed that Philadelphia could be one of the world's 25 top-tier cities, but that civic leaders couldn't be complacent because "as we sit down here for breakfast, [competing cities] are preparing to eat our lunch."

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning. Deep learning's powerful capabilities rely on algorithms called convolutional neural networks that consist of layers of nodes (also known as neurons). Such neural networks can filter huge amounts of data through their "deep" layers to become better at, say, automatically recognizing individual human faces or understanding different languages. These are the types of capabilities that already empower online services offered by the likes of Google, Facebook, Amazon, and Microsoft.

The topic of "artificial intelligence" has recently brought a confluence of nationally significant announcements. In September, Stanford University released its One Hundred Year Study on Artificial Intelligence, which was quickly followed by the announcement in early October that five firms -- Amazon, DeepMind of Google, Facebook, IBM, and Microsoft -- have formed a nonprofit named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). A week after the Partnership on AI announced its formation, the National Science and Technology Council (NSTC), which is overseen by the Executive Office of the President, released Preparing for the Future of Artificial Intelligence. For the release of the Stanford and the NSTC reports, perhaps, but the formation of the Partnership on AI is no coincidence. The members of the Partnership on AI realize the marketplace is at an important "tipping point" when it comes to the increasing utilization of AI in the U.S. AI is already used in automobiles to enable enhanced driving safety features and GPS services, in smartphone apps, and in wearable medical device -- to name just a few examples.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The 2016 U.S. Robotics Roadmap was released this week; it's a massive document authored by 150 roboticists that's intended to help frame and guide research and policy decisions with the goal of solving societal problems in the United States. We'll be taking a closer look at it, but here's a 30-minute summary from lead editor Henrik Christensen: The legged robot ANYmal can support disaster relief teams with safer search and rescue operations. With its advanced locomotion capabilities, ANYmal can operate in rough outdoor environments, crawl through pipes, and access buildings over steps and stairs.

Former actress Saya Takagi was arrested in Okinawa on Oct. 25 for possession of marijuana, three months after she unsuccessfully ran for a seat in the Upper House election on a platform to legalize pot for medical purposes. She insists the contraband was not hers. Though Takagi, whose real name is Ikue Masudo, is retired from showbiz, reruns of dramas she appeared in are still shown on TV. When a celebrity is involved in a scandal, broadcasters scour their lineups for any ties to the disgraced person. TV Asahi quickly scrubbed from its afternoon schedule old episodes of the popular detective series "Aibo" that featured Takagi.

The All-Party Parliamentary Group on Data Analytics launches today in Parliament. The group aims to explore the opportunities and challenges presented by'big data', a term which refers to the growth of large, complex data that can be analysed to provide valuable new insights. It's a complicated and controversial area, one that is only going to become more significant as time goes on for those making and influencing policy. Coming from an IT background, I've witnessed a drastic shift in technology over the decades and the speed of that shift is increasing. It can perhaps be best encapsulated by a simple fact: around 90 per cent of global data was created in just the last two years - and that amount is predicted to grow year on year for the next decade.