New concerns have been raised about the political influence of Google after research found at least 80 "revolving door" moves in the past decade – instances where the online giant took on government employees and European governments employed Google staff. The research was carried out by the Google Transparency Project, an initiative run by the Campaign for Accountability (CfA), a US organisation that scrutinises corporations and politicians. The CfA has suggested that the moves are a result of Google seeking to boost its influence in Europe as the company seeks to head off antitrust action and moves to tighten up on online privacy. In the UK, Google has hired people from Downing Street, the Home Office, the Treasury, the Department for Education and the Department for Transport. Overall, the company has hired at least 28 British public officials since 2005.

At ICML last year and the year before the amount of capacity that needed to fit everyone on any single day was about 1500. My advice was to expect 2000 and have capacity for 2500 because "New York" and "Machine Learning". I was not involved in the venue negotiations, but my understanding is that they were difficult, with liabilities over 1M for IMLS the nonprofit which oversees ICML year to year. The result was a conference plan with a maximum capacity of 1800 for the main conference, a bit less for workshops, and perhaps 1000 for tutorials. Then the NIPS registration numbers came in: 3900 last winter.

Kevin Kelly knows technology can't be stopped. Of course, artificial intelligence will wipe out or rework whole industries, and entire career paths will vanish. And if you think the world is addicted to its phones now, just wait until there's a screen everywhere, and you never really escape the grid. We will be tracked like never before. While this may sound like the recipe for a Tom Cruise dystopia sci-fi movie, it doesn't have to be. In fact, in his new book, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future, Kelly goes to great pains to make it all seem like a net positive.

While much of the media attention around autonomous vehicle technology has been focused on fully self-driving cars, consumers shouldn't expect cars to act like chauffeurs any time soon. The vast majority of mainstream vehicles adopting autonomous driving features will be controlled by advanced driver assistance systems (ADAS) or "guardian angels" that learn over time, Gil Pratt, CEO Toyota Research Institute, told reporters and analysts last week. Speaking at the New England Motor Press Association Technology Conference at MIT, Pratt said that 30,000 motor vehicle fatalities occur in the U.S. each year. That number may seem high, but as a whole, U.S. drivers are excellent at avoiding crashes. So, instead of taking the wheel from drivers' hands, as a fully autonomous vehicle would do, auto makers are more focused on assisting drivers for years to come.

Perhaps the Terminator/Skynet concept of world domination isn't as far-fetched as one might assume. Today, robots are doing everything from service industry jobs to writing and almost winning literary prizes. The formidable advancements in artificial intelligence (AI) have prompted not just experts, but the public, to start wondering how we can protect ourselves from robots taking over. Wouldn't robots, with their ever-advancing technology that moves closer and closer to near-human intelligence and consciousness, soon need protection from humans too? This is a question that mathematician Marcus du Sautoy, from the University of Oxford, is asking. As we move closer to a reality of robots equipped with advanced AI, shouldn't they be given moral and legal protection that has, until now, been granted freely to humans?

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

In 2014, the government brought in a new curriculum, which included coding lessons for children. But Prof Susskind said that the development of new, "self-coding" systems meant that such lessons were obsolete. He added: "I belong to the school of thought who don't believe it's a particularly great use of people's time and energy to code. Our thesis is that the next generation of systems will be writing themselves. Automatic code generation is already very common. "Low-level code generation is actually a great intellectual exercise, it's a bit like studying logic, but I don't believe that people learning to code in school will find in seven or eight years that they'll be employable for that reason alone.

Animals have evolved sophisticated ways of processing sensory data to make sense of their surroundings. Now, robotics researchers are drawing inspiration from biological processes to improve the way machines handle information, perceive their surroundings, and react to stimuli. While the human brain is often seen as the ultimate model for robotic intelligence, scientists are also learning plenty from the neurobiological structures and processes of more humble creatures, from fruit flies to rodents. Take the fruit fly – or rather, the maggot that grows up to be a fruit fly. Drosophila fruit fly larvae have fewer than 10 000 neurons – compared to about 100 billion in the human brain.

Last month, the Washington Post reported on a surprising new job in Silicon Valley: bot-writer. "Increasingly, there are poets, comedians, fiction writers, and other artistic types charged with engineering the personalities for a fast-growing crop of artificial intelligence tools," the Post's Elizabeth Dwoskin wrote. These personalities, Dwoskin reported, will soon be joined by more specialized bots developed by other companies, among them Sophie and Molly, "nurse avatars" that talk to patients about their medical conditions. There's even a "guru avatar" in development, designed to teach meditation. These products are exciting and futuristic--just a decade ago, the possibility of conversing with a computer program seemed like science fiction.

Machines are becoming more intelligent every year thanks to advances being made by companies like Google, Facebook, Microsoft, and many others. AI agents, as they're sometimes known, can already beat us at complex board games like Go, and they're becoming more competent in a range of other areas. Now a London artificial-intelligence research lab owned by Google has carried out a study to make sure that we can pull the plug on self-learning machines when we want to. DeepMind, bought by Google for a reported 400 million pounds -- about 580 million -- in 2014, teamed up with scientists at the University of Oxford to find a way to make sure that AI agents don't learn to prevent, or seek to prevent, humans from taking control. The paper -- "Safely Interruptible Agents PDF," published on the website of the Machine Intelligence Research Institute (MIRI) -- was written by Laurent Orseau, a research scientist at Google DeepMind, Stuart Armstrong at Oxford University's Future of Humanity Institute, and several others.