Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning it's more true to say their role is to prepare students for life long careers. But while that was a relatively simple task in the past it's now much more difficult.

Ian McVey is UK director at Qualtrics, an employee engagement firm. In this article, he explores the state of artificial intelligence. Last week, the Institute of Directors (IoD) warned that 15 million jobs are at risk from the rise of automation, a figure echoed by the Bank of England. The IoD wants schools to place less emphasis on pupils' ability to recall facts and apply standardised methods, which machine learning and artificial intelligence do faster and with more precision, and focus instead on how to apply knowledge in a market that no longer rewards workers primarily for what they know, but for what they can do with what they know. Modern reflections on an educational system rooted in an old fashioned calendar are well timed.

In the beginning of 2016 Jill Watson, an IBM-designed bot, has been helping graduate students at Georgia Institute of Technology solve problems with their design projects. Responding to questions over email and posted on forums, Jill had a casual, colloquial tone, and was able to offer nuanced and accurate responses within minutes. A robot has been teaching graduate students for 5 months and none of them realized. Here are just a few of artificial intelligence tools and technologies that will shape and define the educational experience of the future. Duolingo is the world's most popular platform to learn a language.

If you want to start implementing deep networks, you will need to learn tools like TensorFlow or Caffe or Theano or Keras. I would recommend TensorFlow (which is pretty new and is constantly improving with time. I haven't myself gotten around to learning it, but it is something I want to do in the near future). If you want to do deep learning on clusters, you will probably need to learn Spark or Hadoop. Getting a good handle on these tools is probably as important as learning about the math behind the models.

A few years ago, Magnus Egerstedt was walking through the swarm robotics laboratory at the Georgia Institute of Technology, where he is associate director of research, feeling proud of the research spearheaded there, when a disturbing thought crossed his mind. "I began thinking about the robotics laboratories where people are doing things that matter. There's not even ten of them globally," Egerstedt says. "That's weird, because so many people are working on swarm robotics, but it takes money and people to drive research that matters. He immediately envisioned a way to give robotics researchers who aren't with those top labs access to top-lab capabilities. And he knew students at all levels, grade school to graduate school, could benefit as well. "I used as a model the Large Hadron Collider," Egerstedt says. "Physicists realized large particle colliders were too expensive to build separately, so they share.

Wouldn't it be neat to have a brain that could read and understand all that information? There's this startup, Iris AI, that works to fix this problem by building an artificial one. This Singularity University backed team that wants to build an AI capable of highlighting new trends and interconnections of discoveries. This could help researchers in startups, corporations and research institutes to implement what is already out there. I first came across them via TED where they had processed my 2011 (gosh a lot has happened since) TED talk into their awesome system.

Less than 100 years from now, robots will be friendly, useful participants in our homes and workplaces, predicts UBC mechanical engineering professor and robotics expert Elizabeth Croft. We will be living in a world of Wall-Es and Rosies, walking-and-talking avatars, smart driverless cars and automated medical assistants. But much work remains before robots will truly be integrated into our daily lives. In this short Q&A, Croft lays out the rules for engagement between humans and robots and explains why it's crucial to get this aspect right. What role will robots play in our lives in the future?

Language is a system or method of communication as defined by the Oxford Dictionary. Humans acquire skills based on cognition, patterns and connections, while machines learn through programming and algorithms. After years of technological advancement and progress in Artificial Intelligence (AI), language acquisition and understanding is still the number one thing that sets humans apart from machines. An insight into human language acquisition versus machine AI will provide a clear picture of language learning for humans and machines. Advancement of AI is based on understanding different attributes of humans and replicating it on machines.

Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning, it's more true to say their role is to prepare students for life-long careers. But while that was a relatively simple task in the past, it's now much more difficult.

It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities - and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider. "But one reason is that these MOOCs do not provide any teaching assistants. So you can sign up for a course, say in mathematics, or computer science, or web design, or whatever. But you cannot ask anyone a question like'So how do I download this material?'