While the hype of artificial intelligence (AI) and its potential role as a driver of transformational change to businesses and industries is pervasive, there are limited insights into what companies are actually doing to reap its benefits. The Artificial Intelligence Report, conducted by Ernst & Young, aims at getting a deeper understanding of how companies currently manage their AI activities, and how they address the current challenges and opportunities ahead. To get to the heart of the AI agenda, we received input from AI leaders in 277 companies, across 7 sectors and 15 countries in Europe, via surveys and/or interviews. Below is a brief summary of what they had to say. This is significantly higher than on the non-managerial / employee level where AI is only considered an important topic in 28% of the companies.

Customer experience is often the deciding factor when it comes to banking. Today's customers want personalized interactions, simplified banking and access to their accounts through technology. Banks that can innovate and meet customers' needs have a huge competitive advantage. Here are 10 examples of innovative banking customer experiences. Bank of America recently launched its AI-powered chatbot, Erica, and saw more than 1 million users in the first three months.

What human tasks can artificial technology not replace? Does the physical manifestation of AI change the way we interact with it, and how might we be manipulated by this? Do we put too much faith in AI? And what happens when AI goes wrong? These are just some of the questions Guardian supporters asked host and executive editor for Membership Lee Glendinning to put to our panel in this edition of We Need to Talk About.

The Artificial Intelligence we believe to be global, is far from it. Within restricted geographies and people. As the pressure of containment builds and the kettle begins to whistle, the call sounds. How do we make global AI truly global? This call will be made in different ways.

Artificial intelligence research has a lot to learn from nature. My work links biology with computation every day, but recently the rest of the world was reminded of the connection: The 2018 Nobel Prize in Chemistry went to Frances Arnold together with George Smith and Gregory Winter for developing major breakthroughs that are collectively called "directed evolution." One of its uses is to improve protein functions, making them better catalysts in biofuel production. Another use is entirely outside chemistry – outside even the traditional life sciences. That might sound surprising, but many research findings have very broad implications.

Pepper has become the first robot to appear at a UK parliamentary meeting, talking to MPs about the future of artificial intelligence in education. The robot is currently based at Middlesex University, working with students and appearing at events. MPs smiled when the robot was wheeled in, although both questions and answers were pre-arranged. Tory MP Lucy Allan joked that Pepper was "better than some of the ministers we have had before us". The Education Committee is looking at the impact technologies such as AI and robotics will have on education.

Boston Dynamics continues to crank out viral-worthy videos of its robots busting strange and unexpected moves. The robotics company formerly owned by Google released on Tuesday a new video of its dog-like SpotMini robot dancing to a cover of Mark Ronson's "Uptown Funk." It even pulls off the robot-dog equivalent to a moonwalk. Previously SpotMini has gone viral for opening doors for its robot comrades. Boston Dynamics released no information to accompany the video, so it's entirely possible that Spotmini has been pre-programmed to dance.

LONDON – SoftBank Group Corp.'s Pepper humanoid robot testified on Tuesday at the Education Committee of Britain's House of Commons. It was the first time in the long history of the British Parliament, which has served as the world's standard-setter of the parliamentary system, for a nonhuman to present an opinion, people familiar with the matter said. Pepper answered questions during a committee session to discuss the fourth industrial revolution, which uses robots and other new technologies, and the development of education brought on by artificial intelligence. "Good morning, chair," Pepper started off by saying. "Thank you for inviting me to give evidence today," it proceeded.

Chatbot Eugene Goostman supposedly passed the legendary Turing Test on Sunday, tricking 33% of a panel of judges into believing he was a real boy during the course of a five-minute chat conversation. The milestone conveniently occurred 60 years to the day after Alan Turing passed away; Turing bet that by the year 2000, computers would be intelligent enough to trick humans into thinking they were real 30% of the time. As you may or may not notice below, passing the Turing Test is less about building machines intelligent enough to convince humans they're real and more about building programs that can anticipate certain questions from humans in order to pre-form and return semi-intelligible answers. In that spirit, Eugene Goostman -- the fake 13-year-old from Odessa, Ukraine who doesn't speak English all that well – makes for a semi-convincing chatbot. His answers are at times enthusiastic and unintelligible like those from any normal 13-year-old would be; add in a shaky grasp of English, and there you go.

The complete part of the earthquake frequency-magnitude distribution (FMD), above completeness magnitude mc, is well described by the Gutenberg-Richter law. The parameter mc however varies in space due to the seismic network configuration, yielding a convoluted FMD shape below max(mc). This paper investigates the shape of the generalized FMD (GFMD), which may be described as a mixture of elemental FMDs (eFMDs) defined as asymmetric Laplace distributions of mode mc [Mignan, 2012, https://doi.org/10.1029/2012JB009347]. An asymmetric Laplace mixture model (GFMD- ALMM) is thus proposed with its parameters (detection parameter kappa, Gutenberg-Richter beta-value, mc distribution, as well as number K and weight w of eFMD components) estimated using a semi-supervised hard expectation maximization approach including BIC penalties for model complexity. The performance of the proposed method is analysed, with encouraging results obtained: kappa, beta, and the mc distribution range are retrieved for different GFMD shapes in simulations, as well as in regional catalogues (southern and northern California, Nevada, Taiwan, France), in a global catalogue, and in an aftershock sequence (Christchurch, New Zealand). We find max(mc) to be conservative compared to other methods, kappa = k/log(10) = 3 in most catalogues (compared to beta = b/log(10) = 1), but also that biases in kappa and beta may occur when rounding errors are present below completeness. The GFMD-ALMM, by modelling different FMD shapes in an autonomous manner, opens the door to new statistical analyses in the realm of incomplete seismicity data, which could in theory improve earthquake forecasting by considering c. ten times more events.