The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

Garry Kasparov, one of the greatest chess players of all time, is famous for his pair of faceoffs against the IBM supercomputer Deep Blue. Kasparov won the first match against the computer, 4-2, in 1996, but lost in the rematch, 3½-2½, in 1997. He recently published a book, "Deep Thinking," about the experience. Business Insider recently spoke with Kasparov about Deep Blue, his thoughts on AI, and machine advancements over the past 20 years -- and how he sees the interplay between machine intelligence and humanity. This interview has been edited for clarity and length. Garry Kasparov: AI as a concept is surrounded by mythology. Most of the things we mention we understand. You know, if we say "white," we all see it's white. If we talk about elements of computer science or some general items, we are in agreement.

Big data and machine learning will play increasingly critical roles in improving information security, predicts Will Cappelli, a vice president of research at Gartner. Even today, the demand for the technologies is strong, he points out. "In terms of market size, Gartner estimates that in 2016 the world spent approximately $800 million on the application of big data and machine learning technologies to security use cases," he says in an interview with Information Security Media Group. "About 80 percent of that was big data; about 20 percent was machine learning." Enterprises are looking at these technologies as two components of a single architecture, he says.

Joy Buolamwini is a graduate researcher at the MIT Media Lab and founder of the Algorithmic Justice League – an organisation that aims to challenge the biases in decision-making software. She grew up in Mississippi, gained a Rhodes scholarship, and she is also a Fulbright fellow, an Astronaut scholar and a Google Anita Borg scholar. Earlier this year she won a $50,000 scholarship funded by the makers of the film Hidden Figures for her work fighting coded discrimination. How did you become interested in that area? When I was a computer science undergraduate I was working on social robotics – the robots use computer vision to detect the humans they socialise with.

With more than 40% market share in mobile games, billion monthly active users and 2.6 billion unique devices, Unity plays a profoundly important role in the booming gaming and VR markets. Introducing machine learning and AI into game development will curb one of the greatest expenses in time and money that go into content creation, but will also change the role of programmers. With immense access to data and computing power, developers will use historical data to model extensive virtual realities of games, taking programming out of the equation. I spoke with Dr Danny Lange, VP of AI and machine learning at Unity about this, the present and future of game development, machine learning, and how developers and startups can get most out of it. Dr Danny Lange is VP of AI and machine learning at Unity Technologies.

A disembodied voice sounded over a loudspeaker. Take cover," it warned to anyone within earshot. Then, the sirens began to wail. Erin Delaney assumed it was a drill. She peeked down the hallway to see how other people were responding. Then she hit the deck. The NATO base in Kabul where Delaney had been working for weeks was being attacked. Delaney, 24, had never had any military training. She grew up in San Diego, traveled up the coast for college at UC Berkeley, and spent the next two years nestled in the safe, Tesla-filled San Francisco bubble, working in the compliance department at Dropbox. Now, with her nose to the ground, she was getting a taste--however brief--of life in a war zone. She flipped over the visitor's badge she'd received when she first arrived at the base. In case of attack, it said, she should stay on the ground for two minutes. Assuming nothing dire happened, she was to shelter in place until the shelling stopped. So, for about an hour, that's what she did.

With more than 40% market share in mobile games, billion monthly active users and 2.6 billion unique devices, Unity plays a profoundly important role in the booming gaming and VR markets. Introducing machine learning and AI into game development will curb one of the greatest expenses in time and money that go into content creation, but will also change the role of programmers. With immense access to data and computing power, developers will use historical data to model extensive virtual realities of games, taking programming out of the equation. I spoke with Dr. Danny Lange, VP of AI and machine learning at Unity about this, the present and future of game development, machine learning, and how developers and startups can get most out of it. Dr. Danny Lange is VP of AI and machine learning at Unity Technologies.

The world's No.1 weiqi (Go) player Ke Jie lost the contest against his artificial intelligence (AI) rival, AlphaGo, in the third and also final match of the summit on Saturday. This match began at 10:30 BJT in Wuzhen, east China's Zhejiang Province, with AlphaGo playing the black and Ke white. Ke showed his brilliant weiqi skills as he said he will "fight till the end," though he lost his previous two matches against AlphaGo on Tuesday and Thursday. AlphaGo made the first "impolite" move as it did on Thursday – to put the black stone on the bottom-right corner of the weiqi board. It is a Chinese tradition that the first stone is usually placed around the top-right corner and this is what weiqi coaches always teach beginners.

Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.

Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.