Soon, the glow of hundreds of screens illuminates each face in every classroom. Inside Skye Templeton's seventh-grade Social Studies class, students are enthralled by online documents and videos about the casualties of World War II. Nearby, in Sara Sharpe's sixth-grade math class, a small group of students works through computer drills covering ratios and percents. And, across the hallway, English and Language Arts teacher Lori Meyer expresses amazement at how much her eighth graders enjoyed doing their final project: a research paper and iMovie on the 1960s. With their MacBooks, students researched topics, wrote their papers, and submitted to their teacher via email. "This is the first time in my 12 years of teaching that students said writing the research paper was their favorite assignment," Meyer said, "and I know it was due to the laptops."

If free books on machine learning aren't enough for you, Open Source Society University has a free online course on Data Science. I wonder how their football team will do this Fall?

Celestion loudspeakers have powered the performances of many noted guitar and bass players, including legends such as Jimi Hendrix. Deciding whether a loudspeaker is good enough for professional musicians is a lengthy and painstaking process. Each speaker has its own unique sound based on a combination of sonic characteristics, such as midrange character and brightness. Evaluating a musical instrument loudspeaker involves subjective judgement about whether it generates a "good" sound. Only engineers with years of experience can reliably make that decision, and then only after repeated listening to a single loudspeaker and comparing the sounds it produces with those produced by a reference speaker.

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. If your company isn't using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power and ease of use of new ML platforms (whether it is from large technology companies like Amazon, Google and Microsoft or from startups like Dato), we expect to see more and more applications that generate real-time predictions and continuously get better over time.

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.