A report from KPMG suggests that insurers will need to take "drastic measures" in order to reinvent themselves. In Empowered for the Future: Insurance Reinvented, which was released earlier this week, KPMG argues that insurers need to make fundamental changes to their business and operating models. "Success in the insurance industry of the future will not come from simply tweaking the status quo, insurers will need to change virtually every part of their business if they hope to not just survive, but thrive," reads the report. KPMG surveyed more than 70 insurance executives and asked them about the difficulties they see ahead and how they plan to meet them. Asked about the greatest threats they face (respondents could give more than one answer), executives said they were most concerned by the prospect of regulatory changes (37%), the effect of organisations outside the insurance industry (37%), and disruptive technologies (36%).

Google is extending its push into artificial intelligence with a new European research centre dedicated to advancing the technology. Based in Zurich, the team will focus on three areas – machine learning, natural language understanding and computer perception. Emmanuel Mogenet, who will head the unit, said much of the research would be on teaching machines common sense. There was, he said, "no limit on how big I grow the team". "We are very ambitious in terms of growth. The only limiting factor will be talent," he told journalists gathered in Zurich to hear more about Google's AI plans.

Everyone who's anyone, you might think. Elon Musk, Bill Gates, and Stephen Hawking have all expressed concern about the "existential threat" of AI, just as "deep learning" neural networks are revolutionizing the AI field. Should we be scared for our jobs? Fear not, I have answers! They are, respectively, "maybe," and "don't be ridiculous."

You'll find the Future of Humanity Institute down a medieval backstreet in the centre of Oxford. It is beside St Ebbe's church, which has stood on this site since 1005, and above a Pure Gym, which opened in April. The institute, a research faculty of Oxford University, was established a decade ago to ask the very biggest questions on our behalf. Notably: what exactly are the "existential risks" that threaten the future of our species; how do we measure them; and what can we do to prevent them? Or to put it another way: in a world of multiple fears, what precisely should we be most terrified of? When I arrive to meet the director of the institute, Professor Nick Bostrom, a bed is being delivered to the second-floor office. Existential risk is a round-the-clock kind of operation; it sleeps fitfully, if at all. Bostrom, a 43-year-old Swedish-born philosopher, has lately acquired something of the status of prophet of doom among those currently doing most to shape our civilisation: the tech billionaires of Silicon Valley.

Google has launched a project to use artificial intelligence to create compelling art and music, offering a reminder of how technology is rapidly changing what it means to be a musician, and what makes us distinctly human. Google's Project Magenta, announced Wednesday, aims to push the state of the art in machine intelligence that's used to generate music and art. "We don't know what artists and musicians will do with these new tools, but we're excited to find out," said Douglas Eck, the project's leader in a blog post. Just as Louis Daguerre and George Eastman did not predict what Annie Leibovitz or Richard Avedon would do, "surely Rickenbacker and Gibson didn't have Jimi Hendrix or St. Vincent in mind." Google has already released a song demonstrating the technology. The song was created with a neural network -- a computer system loosely modeled on the human brain -- which was fed recordings of a lot of songs.

This video examines two of the main problems with machine learning, regression, and classification. Regression is a combination of multidimensional fitting and function interpolation. With the regression problem, you are trying to find function approximation with the minimal error deviation or cost function. That means that if you have multiple events characterized by input parameters which can be labeled differently, and you want your system to predict which label should be used – this is the classical classification problem.

Over the last few years the field of Artificial Intelligence (AI) and how it is shaping the marketing landscape has started to fascinate me. Most of us will remember Schwarzenegger in the world famous science fiction film, Terminator, as the cyborg assassin sent back in time to kill a woman whose son would one day become the saviour against these futuristic robotic machines. In fact, for some, when they think of Artificial Intelligence it probably begins by conjuring up images of robots taking over from humans in a post–apocalyptic future. The actual concept of Artificial Intelligence (AI) was in fact born in the 50s when computer scientist John McCarthy introduced the term Artificial Intelligence as'the science and engineering of making intelligent things'. It's definitely not a new concept and it wasn't just created for the box office, it is in fact already playing a pivotal role under our very own eyes across multiple industries, and starting to even shape the landscape for future marketers.

We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction. The pioneers of artificial intelligence were surely off on the timing, but they weren't wrong; AI is coming.

GE unveiled a machine data system for power plants it claims can increase a facility's efficiency of operation by 1.5%, reduce carbon dioxide emissions by 3%, and reduce coal consumption by 67,000 tons for each megawatt of electricity produced. GE's Digital Power Plant for Steam suite was introduced at the Minds Machines conference in Paris June 14, where GE executives also revealed the results of a hardware and software upgrade using the technology at the Chivasso power plant in Northern Italy. The plant, run by A2A Group, was restarted in November 2015 after a three-year shutdown. Digital Power Plant for Steam is one of the first application suites to sit atop GE's Predix machine data analytics platform and yield practical, industrial results. The reference to "steam" in the product's name reflects the fact that gas and coal-fired power plants produce steam to drive the turbines that generate electricity used in households and industry in most societies.

Every person who's any individual, you could feel. Elon Musk, Bill Gates, and Stephen Hawking have all expressed problem about the "existential threat" of AI, just as "deep learning" neural networks are revolutionizing the AI area. Really should we be fearful for our careers? Worry not, I have responses! They are, respectively, "maybe," and "don't be preposterous."