An Android smartwatch from LG launched in 2014. It used to be the domain of science-fiction movies – gadgets that track and record your health data, surgeries performed remotely, and even diagnostics done by robotics. Everywhere we turn, it is apparent that technological advancements are fast changing the healthcare industry. "Technology diagnostics and predictive informatics are the new sciences propelling healthcare into the future," says Gordon Hewitt, chairman of the Global Advisory Board of SIXCAP Group. Today, it is getting increasingly common to see wrists donning a smart watch or health tracker that tells you the amount of calories you have burned, how fast your heartbeat is, and much more. They enable the capture of a continuous stream of data about physiology and kinesiology, empowering consumers with self-knowledge and allowing them to self-monitor their physical activities and prevent undesirable health conditions such as hypertension and stress.

This month Stanford launched a 100-year study of AI (AI100) with a report: Artificial Intelligence and Life in 2030. The 16 member study panel issuing the report sees increasingly useful applications of AI, with potentially profound positive impacts on our society and economy over the next decade. The study identifies eight domains where AI is already having or is projected to have the greatest impact: transportation, healthcare, education, low-resource communities, public safety and security, employment and workplace, home/service robots and entertainment. Check out this Pearson video (and our review of their report): Over the next few months, we'll be exploring developments in these eight categories and the implications for employment and education. This series, #AskAboutAI, will encourage parents, teachers, mentors and advisors to engage young people in a dialog about the emerging automation economy and the ethical and economic implications of artificial intelligence (AI).

Will the technology be coming for your job next? The question of whether marketing is more science or art has never seemed more relevant now that highly sophisticated cognitive learning technology is able to assume many of the tasks involved in marketing -- in some cases, even doing them better than a human could. But visions of a completely automated campaign may be premature, according to executives from IBM and other companies at the forefront of AI who weighed in on the technology's impact during a panel discussion at ad:tech New York last week. In good news for creative directors, the experts said cognitive technology has the ability to free up marketers to spend more time tackling bigger picture responsibilities, such as finding the inspiration for the right voice and vision to make an emotional connection with consumers. By laying the groundwork for significantly more sophisticated one-to-one marketing, AI could even create a need to beef up analytics, content and other areas for businesses that are able to gain a competitive edge through customer-centric marketing.

How to get people interested in art? How to expose permanent-collection works that sit in storage? These are questions art museums constantly ponder. Recently, Tate Britain asked another one: How can artificial intelligence help? It put the question to anyone who wanted to compete for the 2016 IK Prize, which promotes the use of digital technology in the exploration of art at Tate Britain or on the Tate website.

In 1997, a computer program codenamed Deep Blue, defeated Russian chess grandmaster, Garry Kasparov. In 2006, Deep Fritz dethroned the then World Champion Chess player, Vladamir Kramnik. In 2016, Google developed an artificially intelligent computing system named AlphaGo. It added another name on the "list of humans" defeated by a machine, the highly ranked South Korean Go player, Lee Sedol. Prior to the dethroning of Kasparov, Kramnik and Sedol, it was thought that artificial intelligence still had ways to go before it could outwit and outmatch gifted human players.

Tokio Marine & Nichido Fire Insurance Co. will extend automobile insurance coverage to accidents involving automated driving cars for all policyholders without costs from April 2017. The core unit of Tokio Marine Holdings Inc. will be the first to give such insurance coverage in Japan, officials said Tuesday. Tokio Marine will attach a special provision to cover self-driving car accidents to all contracts renewed or newly concluded in and after April 2017. The move is aimed at preventing victims of automated driving car accidents from being left without relief for a long time. In Japan, existing car insurance products do not cover self-driving car accidents unless the driver's fault is confirmed. This makes it necessary for accident victims to lodge damages claims against automakers and others on their own.

Quantum computer has an amazing potential of fast information processing. However, realisation of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a novel platform, quantum reservoir computing, to solve these issues successfully by exploiting natural quantum dynamics, which is ubiquitous in laboratories nowadays, for machine learning. In this framework, nonlinear dynamics including classical chaos can be universally emulated in quantum systems. A number of numerical experiments show that quantum systems consisting of at most seven qubits possess computational capabilities comparable to conventional recurrent neural networks of 500 nodes. This discovery opens up a new paradigm for information processing with artificial intelligence powered by quantum physics.

Generative adversarial networks (GANs) are successful deep generative models. GANs are based on a two-player minimax game. However, the objective function derived in the original motivation is changed to obtain stronger gradients when learning the generator. We propose a novel algorithm that repeats the density ratio estimation and f-divergence minimization. Our algorithm offers a new perspective toward the understanding of GANs and is able to make use of multiple viewpoints obtained in the research of density ratio estimation, e.g. what divergence is stable and relative density ratio is useful.

We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical model, and these assumptions are often misplaced in real-world settings. Correlated random measures address this limitation. They model correlation within the measure by using a Gaussian process in concert with the Poisson process. With correlated random measures, for example, we can develop a latent feature model for which we can infer both the properties of the latent features and their dependency pattern. We develop several other examples as well. We study a correlated random measure model of pairwise count data. We derive an efficient variational inference algorithm and show improved predictive performance on large data sets of documents, web clicks, and electronic health records.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems. Big companies do not value things like intuition, innovation, vision or a disruptive mindset (despite claiming the contrary), and for good reasons.