New lip-reading technology could help solve crimes by deciphering what people caught on CCTV are saying, researchers have claimed. The visual speech recognition technology developed by the University of East Anglia in Norwich can be used to determine what people are saying in situations where audio is not good enough to hear - such as on security camera footage. Helen Bear, from the university's school of computing science, said the technology could be applied to a wide range of situations from criminal investigations to entertainment. She added: "Lip-reading has been used to pinpoint words footballers have shouted in heated moments on the pitch, but is likely to be of most practical use in situations where there are high levels of noise, such as in cars or aircraft cockpits. "Crucially, whilst there are still improvements to be made, such a system could be adapted for use for a range of purposes - for example, for people with hearing or speech impairments."

Two months ago, Stephen Hawking warned humanity that its days may be numbered: the physicist was among over 1,000 artificial intelligence experts who signed an open letter about the weaponization of robots and the ongoing "military artificial intelligence arms race." Overnight we got a vivid example of just how quickly "artificial intelligence" can spiral out of control when Microsoft's AI-powered Twitter chat robot, Tay, became a racist, misogynist, Obama-hating, antisemitic, incest and genocide-promoting psychopath when released into the wild. For those unfamiliar, Tay is, or rather was, an A.I. project built by the Microsoft Technology and Research and Bing teams, in an effort to conduct research on conversational understanding. It was meant to be a bot anyone can talk to online. The company described the bot as "Microsoft's A.I. fam the internet that's got zero chill!." Microsoft initially created "Tay" in an effort to improve the customer service on its voice recognition software. According to MarketWatch, "she" was intended to tweet "like a teen girl" and was designed to "engage and entertain people where they connect with each other online through casual and playful conversation."

Things went from cute to Godwin in about a day. Jana Eggers has observed that AI is our progeny; rather than demonize or glorify it, we should be like responsible parents, and decide how we want our offspring to be raised. This is a real problem for machine learning algorithms. We train them on a corpus, or body of knowledge. We want them to be big and varied so we don't overfit the algorithm to a limited data set.

Google may be looking to end their involvement with robots and artificial intelligence, but that's hardly stopping Amazon. The e-commerce more retailer recently held a top secret conference for robot experts and space explorers. There were even lightsabers involved. This past week, in the lovely town of Palm Springs, California, a top secret meeting took place. This invite-only conference included experts in artificial intelligence, robotics and space exploration.

Tiff Macklem is dean of the Rotman School of Management and former senior deputy governor of the Bank of Canada. Ajay Agrawal is the Peter Munk professor of entrepreneurship and professor of strategic management at the Rotman School and founding academic director of its Creative Destruction Lab. Scott Bonham is co-founder of GGV Capital and co-chair of the C100, a non-profit organization of Canadian tech entrepreneurs, executives and investors in Silicon Valley. Productivity growth is essential for improving a country's standard of living. Unfortunately, Canada's productivity growth chronically underperforms.

In less than 24 hours, Microsoft's artificial intelligence project modeled after an American teenage girl went from making awkward conversation in broken syntax to spewing hateful, fully formed tweets laden with racial slurs. But as startling as the offensive tweets were, the incident shows how quickly online conversations turn fetid when diversity isn't a factor. Tay, programmed as a 19 year old, was created as a machine learning project meant interact with peers between 18 and 24 years old. Users can play games with her, trade pictures, tell stories, and ping her for late-night chats. That last activity went awry Thursday when the chatbot began regurgitating inappropriate messages that skewed anti-semitic, used the n-word, and condemned feminism.

Microsoft created an artificial-intelligence based chatbot named Tay to engage with young people on Twitter. But within hours of her debut Wednesday, the Internet had stolen the bot's innocence. The bot, @TayandYou, rapidly transformed into a racist that hates feminists, supports Donald Trump for President and considers herself a fan of Hitler. The metamorphosis from happy bot to enraged racist came lightning-fast, much like the way many things go in the instantaneous virtual world in which Tay was born. "Tay" went from "humans are super cool" to full nazi in 24 hrs and I'm not at all concerned about the future of AI pic.twitter.com/xuGi1u9S1A

I've just finished Week 5 of the Coursera/Stanford Machine Learning course. It has been a mixture of refreshing, relearning, and new for me. I had already been using, building, and researching/evaluating machine learning algorithms for a number of years. I therefore felt like I'knew' a lot of the concepts, particularly the introductory ones. I put'knew' in quotes, however, since I've always had a feeling that I don't know them well enough, no matter how many times I've used them.

Sudden, broad-scale shifts in public opinion about social problems are relatively rare. Until recently, social scientists were forced to conduct post-hoc case studies of such unusual events that ignore the broader universe of possible shifts in public opinion that do not materialize. The vast amount of data that has recently become available via social media sites such as Facebook and Twitter--as well as the mass-digitization of qualitative archives provide an unprecedented opportunity for scholars to avoid such selection on the dependent variable. Yet the sheer scale of these new data creates a new set of methodological challenges. Conventional linear models, for example, minimize the influence of rare events as "outliers"--especially within analyses of large samples.

Linear regression is one of the simplest machine learning techniques you can use. It is often useful as a baseline relative to more powerful techniques. Like all regressions, we wish to map some input X to some input Y. You may recall from your high school studies that this is just the equation for a straight line. When X is 1-D, or when "Y has one explanatory variable", we call this "simple linear regression".