Zurich Insurance is deploying artificial intelligence in deciding personal injury claims after trials cut the processing time from an hour to just seconds, its chairman said. "We recently introduced AI claims handling … and saved 40,000 work hours, while speeding up the claim processing time to five seconds," Tom de Swaan told Reuters, after the insurer started using machines in March to review paperwork, such as medical reports. "We absolutely plan to expand the use of this type of AI (artificial intelligence)," he said. Insurers are racing to hone the benefits of technological advancements such as big data and AI as tech-driven startups, like Lemonade Inc., enter the market. Lemonade promises renters and homeowners insurance in as little as 90 seconds and payment of claims in three minutes with the help of artificial intelligence bots that set up policies and process claims.

We're currently in the final phase of AI development, in which we've advanced past teaching computers to follow rules and evaluating the best solutions to problems. With a clearly defined goal and enough data to parse, AI can now learn how to arrive at a solution on its own. That's both exciting and terrifying all at once, says data scientist and YouTuber Siraj Raval on stage at TNW Conference in Amsterdam. According to him, the danger we now face in the 21st century is that governments and corporations can arm themselves with powerful AI to control societies, and we need a way to tackle that. What are we afraid of, specifically?

Summary: A machine learning algorithm shows that during sleep, the brain actively reprocesses information learned the previous day, strengthening the memory. University of Tübingen neuroscientists use machine learning algorithm to show that the brain actively reprocesses previously learned information during sleep, strengthening memory. Sleep helps us to retain the information that we have learned during the day. We know from animal experiments that new memories are reactivated during sleep. The brain replays previous experience while we sleep – and this replay strengthens memories overnight.

As voice assistants like Amazon's Alexa and Apple's Siri get more popular, a new study found what humans think about the technology -- and it sounds like the 2013 movie "Her." The study found people who use voice assistants regularly wish it were human, while others admitted to sexually fantasizing about their virtual assistant. The study, which focuses on voice technology implications for brands, was conducted by J. Walter Thompson Innovation Group London, a platform for research and analytics, and the media agency Mindshare Futures. More than 30,000 respondents in the U.K. took part in a two-week self-ethnography project from January - March 2017, jotting down their behaviors and attitudes related to voice technology. Researchers then analyzed two focus groups of 12 of the thousands of participants.

UK insurer Ageas has begun to use artificial intelligence (AI) technology to help manage its motor claims in partnership with AI specialist Tractable. In what it describes as "a first" for the UK market, Ageas said the technology is now being used to help Ageas engineers verify the performance of its UK-wide repair networks in managing customers' motor claims. Ageas completed a successful first-stage pilot at the end of 2016, which performed analysis of several thousand vehicle images involved in accidents or requiring repair. Ageas' motor engineers were able to verify the findings of Tractable's AI technology, called AI Approval. The pilot found "that repair efficiencies in a proportion of claims could be realized, enabling cost savings and engineers to focus on more complex matters," said Eastleigh, England-based Ageas, which in 2015 was the third largest motor insurer in the UK (according to statistics from the Association of British Insurers). "The results of our initial pilot are impressive, and I'm keen to progress to using this technology at scale," said Ageas' Transformation Director Rob Smale.

At the opening of the Leverhulme Centre for the Future of Intelligence in Cambridge last year, Professor Stephen Hawking told the crowd: "Success in creating AI could be the biggest event in the history of our civilisation. We do not yet know which." It is perhaps not coincidental that the centre, which brings together researchers to investigate the implications of AI, has been established in this country. Five of the world's biggest technology companies have bought UK AI businesses in recent years, including DeepMind, which was acquired by Google for a reported $400m in 2015, SwiftKey (bought by Microsoft for an estimated $250m) and Magic Pony Technology (acquired by Twitter for $150m). Analysis by MMC Ventures shows the number of AI companies founded in the UK doubled in 2014-16, compared with 2011-13.

Researchers have developed an artificial intelligence system that can put words right into people's mouths. It takes an image of a person as well as an audio clip, using them to create a video of a person speaking that audio. While the system is still rough and not realistic looking, the researchers claim the software could soon make fake videos that seem real. As an audio clip plays, the AI manipulates the mouth to look like the person is speaking. Left is the original still image.

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.

In this paper, we study and analyze the mini-batch version of StochAstic Recursive grAdient algoritHm (SARAH), a method employing the stochastic recursive gradient, for solving empirical loss minimization for the case of nonconvex losses. We provide a sublinear convergence rate (to stationary points) for general nonconvex functions and a linear convergence rate for gradient dominated functions, both of which have some advantages compared to other modern stochastic gradient algorithms for nonconvex losses.