Machine learning can zero in on abnormal behaviour not detectable by existing programs. 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.
Billy Beane, the analytics-driven general manager of the budget-strapped Oakland A's, shook up sports and corporate boardrooms by melding overlooked, under-valued players into oddball yet cheap and winning teams. As depicted in the book Moneyball, Beane enshrined a new job category in serious sports — director of analytics. But there is one big thing that it never accomplished: win Beane a championship. Enter artificial intelligence: Some pro-sports teams are exploring how machine learning, the leading form of AI, might help where Moneyball has fallen short.
Computational linguists and computer scientists, among them University of Texas professor Jason Baldridge, have been working for over fifty years toward algorithmic understanding of human language. They’re not there yet. They are, however, doing a pretty good job with important tasks such as entity recognition, relation extraction, topic modeling, and summarization. These tasks are accomplished via natural language processing (NLP) technologies, implementing linguistic, statistical, and machine learning methods.
Information technology evolves through disruption waves. First the computer, then the web and eventually social networks and smartphones all had the power to revolutionize how people live and how businesses operate. They destroyed companies that weren’t able to adapt, while creating new winners in growing markets. While the exact timing and form of such waves of disruption are hard to predict, the pattern they follow is easy to recognize. Take the web/digital disruption, for example: There was a technological breakthrough (e.g. Sir Tim Berners-Lee’s WWW), which built on/took advantage of existing technologies.
While great advances are being made in the analytical capabilities of computer systems there are also impressive developments being made in making computers more emotionally intelligent. This field is known as Affective Computing, and is defined as the study and development of systems and devices that can recognize, interpret, process, and simulate human emotions (or affects).