Jean-Philip Poulin was feeling "joyful" and "excited" when I interviewed him recently in Montreal. I know this because he showed me his real-time emotion metrics during our conversation, which were being parsed by a machine-learning algorithm that uses heart-rate data transmitted from his Microsoft Band 2 fitness tracker. Poulin is the COO of Sensaura, a Montreal-based software startup that proposes to bridge the gap between consumer wearables and affective computing. If its founders are as successful as they believe they will be, their product will hasten the inevitable future of emotionally intelligent machines: video games will know when you're bored, advertisers will know when you're swayed, and mental health professionals will know when you need a check-in. So far, progress in affective computing has depended on facial recognition software, which reads people's emotions pretty much the same way that people do: by looking at their faces for cues.

With Australia's rates of skin cancers one of the highest in the world, IBM Research and MoleMap -- one of the world's largest melanoma screening programs -- are partnering with the Melanoma Institute Australia to help further advances in the identification of melanoma. The partnership and the planned research builds on IBM's existing research agreement with MoleMap, which uses advanced visual analytics to analyse more than 40,000 datasets including images and text. Under the new partnership, IBM Research plans to analyse dermatological images of skin lesions to help identify specific clinical patterns in the early stages of melanoma. The Melanoma Institute cites current statistics indicating that two in three Australians will be diagnosed with skin cancer before the age of 70, yet 95 to 99% of all skin cancers are preventable – with early diagnosis of skin cancer critical for survival rates, notably for melanoma which is considered among the most life-threatening. And, according to national cancer statistics, someone in Australia dies from melanoma every six hours.

Google uses a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened. First is the Bloomberg story that broke the news about RankBrain (See also our write-up of it).

Mumbai: There is tremendous buzz around machine learning, broadly described as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. However, despite an exponential increase in power, computers have typically proved incompetent at things that are really simple to human beings--like recognizing the dog in a picture containing a dog, or understanding speech. The trend, however, is changing. Consider'Deep Learning', which describes a collection of techniques that allow computational tasks that were previously thought impossible. Facebook Inc, for instance, uses it to identify faces, and when Google Inc recently announced that their algorithms could not only'see' a dog but also identify it as a Pomeranian, they heralded the maturity of Deep Learning techniques.

A ROBOT with an algorithm-based persona is being used to help companies make data-driven decisions in real time. South Australian company Complexica has developed Larry, the Digital Analyst, which is basically a set of algorithms tuned to complex problems to quickly generate answers that would otherwise take people a very long time to work out. Big Data software algorithms are taking decision-making to a new level, delivering solutions and efficiencies like never before. The global Artificial Intelligence market is forecast to exceed USD 5 billion by 2020. Father and son team Matthew Michalewicz and Dr Zbigniew "Mike" Michalewicz, a former professor at the University of Adelaide's School of Computer Science and Artificial Intelligence pioneer, started the company in 2014 with software architect Constantin Chiriac.

Not all Twitter bots are racist -- some are genuinely creepy, (but in the best possible way). Take @smilevector, an algorithm created by New Zealand neural network researcher Tom White. If you submit a photo of your favorite celebrity in a glum or neutral pose, it'll turn it into a bizarre, "I just ate a child" kind of grin. The bot "uses a generative neural network to add or remove smiles from images it finds in the wild" or submitted to its follow list, according to its creator.

With business applications of artificial intelligence ranging from customer service, to hiring, to marketing, it's clear that AI is a tool that is critical for companies to embrace. But using AI has consequences for business structures, hierarchies, and budgets. Why Dick's Sporting Goods decided to play its own game in e commerce Dick's Sporting Goods has long partnered with eBay Enterprise on its e -commerce platform. Learn the benefits and risks of this multi -million dollar IT bet. To understand how this new technology will disrupt traditional business models, and to help prepare businesses integrate AI in their plans, TechRepublic spoke with Dave O'Flanagan, CEO and co-founder of Boxever, a data science company based in Ireland.

Robots probably won't kill people, but people could kill people with robots. That's the concern of an open letter signed by scientists and other interested parties -- including Elon Musk, Steve Wozniak and Stephen Hawking. If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow. See also: Team KAIST won the 2015 DARPA Robotics Challenge, so now what? The Future of Life Institute, the volunteer-backed research organization which posted the letter, aims to "maximize the future benefits of AI while avoiding pitfalls," according to its website.

Back in 1979, two scientists wrote a seminal textbook on computational complexity theory, describing how some problems are hard to solve. The known algorithms for handling them grow in complexity so fast that no computer can be guaranteed to solve even moderately sized problems in the lifetime of the universe. While most problems could be deemed either relatively easy or hard for a computer to solve, a few fell into a strange nether region where they could not be classified as either. The authors, Michael Garey and David S. Johnson, helpfully provided an appendix listing a dozen problems not known to fit into one category or the other. "The very first one that's listed is graph isomorphism," says Lance Fortnow, chair of computer science at the Georgia Institute of Technology.

That is what I said when I was asked whether we would ever see computer science taught in K–12. It was 2009, and I was addressing a gathering of attendees to a workshop on computational thinking (http://bit.ly/1NjmcRJ) It has been 10 years since I published my three-page "Computational Thinking" Viewpoint (http://bit.ly/1W73ekv) in the March 2006 issue of Communications. To celebrate its anniversary, let us consider how far we've come. Since the dotcom bust, there had been a steep and steady decline in undergraduate enrollments in computer science, with no end in sight.