On a chilly March afternoon last year in the South Korean capital Seoul, a computer algorithm made history. A program called AlphaGo beat the reigning human world champion at go, an ancient Chinese board game considered to be one of the most complex pastimes man has ever devised. The game has remained an inviolably human pursuit for centuries, and one of the hardest challenges for artificial intelligence (AI) because of the vast number of possible moves -- more than the number of atoms in the universe -- and the need to employ creativity to win. In Seoul's Four Seasons hotel, AlphaGo's victory over five games was ruthless: Lee Sedol, the 33-year-old human go grandmaster, lost 4-1. At a press conference afterwards, he said with a trace of wonder: "Today, I am speechless."

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. These new capabilities -- studied under the banner of artificial intelligence and machine learning -- are accelerating the pace of innovation in healthcare. Upon close evaluation of the opportunities that exist within each area, it becomes obvious that the stakes are high.

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.

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.

As we discussed in a previous article, "Analytics in The Stock Market: Made for Each Other," the financial services industry would seem to be a particularly good fit for current advances in artificial intelligence (AI) and machine learning. After all, financial services are based on understanding risk and balancing a wide range of numeric factors as well as predicting trends. However, uptake of machine learning and AI has been relatively slow in this industry. The chief reason is the need to maintain a conservative outlook and to accommodate a wide range of processes and systems based around legacy information technology. Despite this, we will likely see some change soon.

Keras, a popular deep learning library, has been updated with a new API to make it easier for developers to use machine learning in Python. Artificial intelligence is all the rage right now and techies are keen to explore ways they can use machine learning. Built in 2015 by Google software engineer and AI researcher François Chollet, Keras was designed to be used on top of TensorFlow and Theano – open-source software libraries developed by machine learning researchers at Google and the University of Montreal, Canada. The update, dubbed Keras 2, has been changed to adapt to TensorFlow API better, allowing developers to mix and match TensorFlow and Keras components together. Since the software runs on TensorFlow and Theano, there is no performance cost to using Keras compared to the other more complex frameworks. Keras is more specialized for deep learning than TensorFlow or Theano.

National Grid is to examine how artificial intelligence can be used to make the UK's power distribution infrastructure more efficient. The company admitted over the weekend that it is in talks with Google's DeepMind artificial intelligence unit, which it acquired for $400m in January 2014, as well as a number of other AI specialists. "We are in the very early stages of looking at the potential of working with DeepMind and exploring what opportunities they could offer for us," a spokesperson for National Grid told City AM. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," they added. The news was broken on Saturday when DeepMind co-founder and CEO Demis Hassabis claimed in an interview with the Financial Times. "We're [in] early stages talking to National Grid and other big providers about how we could look at the sorts of problems they have.

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.

Progress in artificial intelligence and machine learning has been impressive, but there is still much work to be done to advance learning science. While some progress is being made to bring artificial intelligence to the education space as described above, these efforts pale in comparison to advancements in the non-education space. Most of the exciting breakthroughs in 2015 were in fields outside of education. For example, companies such as Amazon and UPS have been piloting the use of drones to deliver packages and other goods to customers. Google recently purchased an AI software company, DeepMind, from a British startup for half a billion dollars.