How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, a computer scientist, former president of the Association for the Advancement of Artificial Intelligence, and managing director of Microsoft Research's main Redmond lab. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

As Fei-Fei Li sees it, this is a historical moment for civilization fueled by an artificial intelligence revolution. "I call everything leading up to the second decade of the twenty-first century AI in-vitro," the Stanford computer science professor told the audience at last week's White House Frontiers Conference. Heretofore, the technology was being fundamentally understood, formulated, and tested in labs. "At this point we're going AI in-vivo," she said. "AI is going to be deployed in society on every aspect of industrial and personal needs."

While IoT remains wildly insecure, thanks to the integrated solution operated by the WISeKey Vertical Platform, IoT devices would be able to organize themselves into trusted networks based on mutual authentication, identity and integrity. This trusted cybersecurity platform will only enable IoT devices which can provide a recognized identity and a valid integrity report to communicate with peer devices that are part of the trusted community. Adding AI at the chip level together with the WISeKey RoT it will allow the IoT to provide authenticated sensor data. Current IoT data do not provide value for their manufacturers unless it can be converted into actionable, contextualized information generated by IoT sensors fully identifiable. The majority of IoT devices are not built with embedded secured systems thus are very vulnerable.

Apple has made a firm commitment to futuristic machine learning by appointing its first director of AI research. Ruslan Salakhutdinov comes from the same US university that developed an AI bot programmed to kill humans in a computer game. Mr Salakhutdinov announced his new appointment with the Cupertino tech firm on Twitter this week and stated that he is looking to recruit more experts for his team. Ruslan Salakhutdinov, Apple's new director of AI comes from the same US university that developed an AI bot programmed to kill humans in a computer game. Pictured is a still from the film I, Robot.

The mussels you see on the shoreline might not just be soaking up some rays... they could be key to studying climate change. An research team has published data from robotic mussels whose sensors have been collecting temperatures for the past 18 years in a bid to monitor climates around the world. By recording the body heat of the real mussels around them every 10 to 15 minutes, the artificial sea life can track and predict global warming in situations where it's most likely to have a pronounced effect. Mussels are highly dependent on air temperature and sunlight for warmth, so they're more reliable gauges of an ecosystem's health than many other techniques. If they're too hot, something is likely wrong.

Earlier this month, the National Science and Technology Council released a report entitled: Preparing for the Future of Artificial Intelligence. While it is promising that the Obama Administration recognizes the importance in beginning to seriously contemplate AI and its implications for human endeavors in the 21st century, the report seems intent on betting that the warnings advocated by Musk and others are not something worth worrying about today. In discussing the associated risks of potentially harmful AI, the report states: "The policies the Federal Government should adopt in the near-to-medium term if these fears are justified are almost exactly the same policies the Federal Government should adopt if they are not justified." The report goes on to assert that the best path forward is to focus on the "less extreme" near term risks inherent in AI development such as security and privacy. The issue with the government's stance here is not in its logic but rather in its assumption that the long-term concerns of AI really are long-term, seemingly declaring the alarm-sounding by industry experts as little more than science fiction overindulgence.

The Internet connected us all--but what good is that if we can't understand each other? Skype's artificial-intelligence-based Translator is our digital Tower of Babel. It lets us talk to anyone, anywhere, regardless of mother tongue. Made available on Windows in late 2015, Translator uses layers of machine-learning algorithms. When a user speaks, the A.I., drawing on millions of speech examples, analyzes the words and transcribes them into text.

Google's survey highlights demographic inequalities in K-12 computer-science education. Research from Google and Gallup has found that black students are less likely to have access to computer-science classes at school and are less likely to use a computer at home or school, despite being more interested in learning about the subject than their white counterparts. The findings, based on a survey of 16,000 students, parents, teachers, principals and superintendents, highlight demographic inequalities in K-12 computer-science education. Just 47 percent of black students say they have dedicated computer-science classes, compared with 58 percent of white students and 59 percent of Hispanic students. White students also have greater access to computers than Hispanic and black students.

I've been trying to think of a way to describe how big Machine Learning is, and I think I finally have a decent one: Because Statistics is the primary mechanism we've had for decades for learning about the world. Machine Learning is similar, except its method of doing it is far more powerful. Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Most importantly, Machine Learning can…well, learn. With traditional Statistics you can potentially extract additional insights with more (and better) data, but the model for doing the analysis itself doesn't improve.

Contact centers across Latin America are looking to transform their customer service solutions by implementing new channels, such as chat, social media, email, video, or updated voice services, which are still hugely relevant in the region. Within that scope, speech recognition technology is often seen as a means to cut costs, improve customer satisfaction, and increase productivity in the call center. But are these solutions worth it? Speech recognition is performed by a machine or program that can identify words or phrases in spoken language and convert them into a machine-readable format. Beyond interactive voice response (IVR) systems, some of the more popular call center speech recognition applications are call routing, speech-to-text, voice dialing, and voice search.