Igor Mordatch is working to build machines that can carry on a conversation. That's something so many people are working on. In Silicon Valley, chatbot is now a bona fide buzzword. He doesn't deal in the AI techniques that typically reach for language. He's a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans.

Prescriptive analytics is about using data and analytics to improve decisions and therefore the effectiveness of actions. Isn't that what all analytics should be about? A hearty "yes" to that because, if analytics does not lead to more informed decisions and more effective actions, then why do it at all? Many wrongly and incompletely define prescriptive analytics as the what comes after predictive analytics. Our research indicates that prescriptive analytics is not a specific type of analytics, but rather an umbrella term for many types of analytics that can improve decisions. Think of the term "prescriptive" as the goal of all these analytics -- to make more effective decisions -- rather than a specific analytical technique.

That's how long Google's Director of Engineering Ray Kurzweil thinks it will take for computers to reach human levels of intelligence. "By 2029, computers will have human-level intelligence," Kurzweil said in an interview at the SXSW Conference with Shira Lazar and Amy Kurzweil Comix. Known as the Singularity, the event is oft discussed by scientists, futurists, technology stalwarts and others as a time when artificial intelligence will cause machines to become smarter than human beings. The time frame is much sooner than what other stalwarts have said, including British theoretical physicist Stephen Hawking, as well as previous predictions from Kurzweil, who said it may occur as soon as 2045. Softbank CEO Masayoshi Son, who recently acquired ARM Holdings with the intent on being one of the driving forces in the Singularity, has previously said it could happen in the next 30 years.

For more on advances in machine learning, prediction, and technology, check out the Data science and advanced analytics sessions at Strata Hadoop World London, May 22-25, 2017. Early price ends April 7. You've probably heard by now that machine learning algorithms can use big data to predict whether a donor will give to a charity, whether an infant in a NICU will develop sepsis, whether a customer will respond to an ad, and on and on. Machine learning can even drive cars and predict elections. I believe it can, but these recent high-profile hiccups should leave everyone who works with data (big or not) and machine learning algorithms asking themselves some very hard questions: do I understand my data? Do I understand the model and answers my machine learning algorithm is giving me? And do I trust these answers? Unfortunately, the complexity that bestows the extraordinary predictive abilities on machine learning algorithms also makes the answers the algorithms produce hard to ...

Video: Google's newest machine learning API can detect objects in videos Video: How interns developed an app that lets you pay your neighbor's heating bill Video: What are 2017's hottest IT jobs?

The innovative ideas and social inspiration that flowed through SXSW Interactive will drive the state of technology in the coming year. Just like we saw at Mobile World Congress, autonomous cars were everywhere at SXSW. Though they weren't cruising on the road, there were plenty of showcases where you could gaze into the luxurious, self-driving future. One model from NIO featured lounge chairs and an interior that's fancier than my living room. Though aesthetic is certainly a big draw, it's important to keep in mind AI and deep learning will be necessary components to these vehicles.

Billionaire entrepreneur Mark Cuban's prediction for the future of the workforce includes more robots and less human workers. Billionaire entrepreneur Mark Cuban's prediction for the future of the workforce includes more robots and less human workers. "We're about to go into a period with artificial intelligence, machine learning, deep learning, those things where we literally are going to see a change in the nature of employment," Cuban said in an interview with CNN's Jake Tapper. In that same interview, he criticized President Trump's leadership skills before calling Trump "technologically illiterate." He used that claim as a launch point to discuss the swiftly evolving nature of jobs due to automation, using Trump's work with US factories to underscore his point.

Igor Mordatch is working to build machines that can carry on a conversation. That's something so many people are working on. In Silicon Valley, chatbot is now a bona fide buzzword. He doesn't deal in the AI techniques that typically reach for language. He's a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans.

Megh Gupta is a member of the investment team at OMERS Ventures. Qasim Mohammad is a Toronto-based venture capitalist and a member of the investment team at OMERS Ventures. The healthcare technology sector has given rise to some of the most innovative startups in the world, which are poised to help people live longer, better lives. The innovations have primarily been driven by the advent of software and mobility, allowing the health sector to digitize many of the pen and paper-based operations and processes that currently slow down service delivery. More recently, we're seeing software become far more intelligent and independent.

The workplace is going to look drastically different ten years from now. The coming of the Second Machine Age is quickly bringing massive changes along with it. Manual jobs, such as lorry driving or house building are being replaced by robotic automation, and accountants, lawyers, doctors and financial advisers are being supplemented and replaced by high level artificial intelligence (AI) systems. So what do we need to learn today about the jobs of tomorrow? The robots and computers of the future will be based on a degree of complexity that will be impossible to teach to the general population in a few short years of compulsory education.