Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

IBM's Watson platform has done a number of remarkable things since its inception. Robots in retail stores are now powered by Watson, as are virtual assistants, and (of course) intelligent chatbots. If you aren't farmilar with it, Watson gives robots and virtual platforms the ability to understand, learn, sense, and experience. Case in point: Ahshok Goel, a professor at Georgia Institute of Technology has just revealed that he has been employing a robot as one of his teaching assistants. "Jill Watson" has been doing regular TA work for Goel, answering students questions in a forum, reminding students of upcoming important dates over email--and all of this in a way that was so human, students never realized that they were talking to a robot.

We live in a world reshaped by big data and smart digital technologies that scale with ever-decreasing marginal cost. But, to date, too little attention has been given to understanding the implications of this for learning, or to setting out the ways in which artificial intelligence (AI) can be used to create learning tools that are more efficient, flexible and inclusive than those currently available; tools that will help learners prepare for an economy that is swiftly being reshaped by digital technologies. In this important new report, a positive and plausible vision is set out of how learning could be transformed by artificial intelligence in education (AIEd). For example, technology available today could be applied to support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles.

Learn how to build a personal chatbot for Facebook Messenger. I have created this step by step guide so you can create your own Facebook Messenger bot without coding. Facebook Messenger has a growing audience of 900 mln. It is an awesome opportunity to showcase your work and promote your services, automate conversations and build out your personal brand. By the end of the course you will be launch and promote your personal bot.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

George Burgess, the founder of Gojimo, a revision app, explores how artificial intelligence can be used within education. Artificial Intelligence (AI) has dominated tech news in 2016, from Google's ground breaking AlphaGo to Microsoft's racist Tay and Amazon's Echo. This has led to a heated debate on what it means for the human race, from socioeconomic concerns about loss of jobs in the fourth industrial revolution to moral, philosophical and even religious questions about our understanding of human consciousness. Rather than stray into these murky waters, I think it is best to concentrate on the sectors where AI can make a quantifiable and significant difference without threatening livelihoods or invoking metaphysics. One such area is education.

This content is designed for audience without any prior Machine learning knowledge. It starts from very basics and goes to advanced topics. We will try to keep this content live and include more and more advanced lab sessions with real life scenarious.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

It could change the workplace, our culture, our sense of humanity, and our relationship not only to one another, but to machines. We are joined by Lucy Suchman, professor of Anthropology of Science and Technology at Lancaster University. Also joining us are Nathaniel Popper a business reporter at the New York Times, and Zeynep Tufecki an associate professor at the University of North Carolina and a contributing opinion writer at the New York Times.