A toxic workplace can leave employers with $190 billion in excess costs. You might not think so, but AI analytics could prove you wrong. Receptiviti is a platform designed to help finance firms and banks measure workplace culture, happiness and burnout. This is done via artificial intelligence (AI) which analyzes how employees use language in the company's messaging systems (email, Slack, Skype, etc.). Aside from making assumptions on stress and happiness, the platform can also analyze if an office has a "toxic" culture.

Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence. "But who knows what's going to happen in a few years from now," he said in an interview. "The main issue is that if you're defending a system you have to be good 100 per cent of the time, but when you're attacking the system you only have to be successful once to get in." Decary-Hetu said a growing list of corporate and government officials who say infiltrations are their top worry, including Bank of Canada governor Stephen Poloz, have a very good reason to fear. The Bank of Canada warned in its semi-annual review released this month that the high degree of interconnectedness among Canadian financial institutions means any successful cyberattack could spread widely throughout the financial system.

Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

MONTREAL – Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

MONTREAL - Technological advances in artificial intelligence are fuelling a new race between hackers and those toiling to protect cybersecurity networks. Cybersecurity is always a race between offence and defence but new tools are giving companies that employ them a leg up on those trying to steal their data. Whereas past responses to cybercrimes often looked for known hacking methods long after they occurred, AI techniques using machine learning scan huge volumes of data to detect patterns of abnormal behaviour that are imperceptible to humans. Experts expect machines will become so sophisticated that they'll develop answers to questions that humans won't clearly understand. David Decary-Hetu, assistant professor of criminology at the University of Montreal, says defenders have an edge right now in using artificial intelligence.

Richard Zemel, an AI consultant for the Toronto Raptors, tells Axios that among things that could transform a season are forecasting an opposing team's next play, or signaling whether, if you swap out one player for another, "we go on to win the championship." How it would work: For a picture of what we're talking about, I went to Rob Schumaker, a computer science professor at the University of Texas at Tyler and co-author of Sports Data Mining. That is all theoretical, though, because, as of now, sports AI is in its infancy: Mark Silver, president of Synergy Sports Technology, a data and analytics firm for basketball teams, says that at the moment, live, in-game forecasting is something from a "fantasy world." Not only has no one figured out how to predict plays or the future performance of players, but they also don't know how they would apply the knowledge if they obtained it. "You couldn't communicate the information fast enough" to players on the court, he said.

Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online.[/i][/i] When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors? In 2016, this kind of data-led decision-making will come ever closer.

This article was taken from The WIRED World in 2016 -- our fourth annual trends report, a standalone magazine in which our network of expert writers and influencers predicts what's coming next. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. When assessing a patient, medics look at snapshots of physiological data that are manually taken by doctors or nurses, and make decisions against patient history, family background and test results, as well as their own knowledge and experience. But what if this data was constantly being taken, every second of every day? And what if a system was clever enough to compare these readings to thousands of patients worldwide with a similar history and disorder, as well as all the current clinical guidelines and studies, and make clinical suggestions to doctors?