Since then, many more programs have been developed to expand the range of opportunities for global education, including student and teacher travel, global curriculum, programs to support teachers, and programs that deploy internet-based communication technologies to facilitate teacher and student global collaboration such as iEarn, the Global Scholars program at Bloomberg Philanthropies or the Out of Eden Project at Project Zero at the Harvard Graduate School of Education. The Asia Society has developed excellent materials to support curriculum development, teacher preparation and collective leadership on global education. Organizations like the Peace Corps or World Teach provide college graduates opportunities to develop their global skills through global service.

Earlier this year, Nervana Systems CEO Naveen Rao was asked what would happen if Intel began attacking the fast-growing market for chips designed specifically for running "deep learning" software. Now, Rao will be a key player in Intel's attempt to catch up in one of the most promising new silicon markets to emerge since the smartphone. Intel revealed Tuesday that it is buying Nervana and its deep-learning hardware and software for an undisclosed amount. The acquisition marks a departure for Intel and comes at a crucial moment. The company became the world's largest chip maker with a single-minded strategy to make its x86 microprocessors the standard for running a huge swath of applications, from solitaire to massive payroll systems.

UC Berkeley artificial intelligence (AI) expert Stuart Russell will lead a new Center for Human-Compatible Artificial Intelligence, launched this week. BRETT, the Berkeley Robot for the Elimination of Tedious Tasks, ties a knot after watching others demonstrate it. Russell, a UC Berkeley professor of electrical engineering and computer sciences and the Smith-Zadeh Professor in Engineering, is co-author of Artificial Intelligence: A Modern Approach, which is considered the standard text in the field of artificial intelligence, and has been an advocate for incorporating human values into the design of AI. The primary focus of the new center is to ensure that AI systems are beneficial to humans, he said. The co-principal investigators for the new center include computer scientists Pieter Abbeel and Anca Dragan and cognitive scientist Tom Griffiths, all from UC Berkeley; computer scientists Bart Selman and Joseph Halpern, from Cornell University; and AI experts Michael Wellman and Satinder Singh Baveja, from the University of Michigan.

Recently we shared an introduction to machine learning. While making machines learn from data is fun, the data from real-world scenarios often gets out of hand if you try to implement traditional machine-learning techniques on your computer. To actually use machine learning with big data, it's crucial to learn how to deal with data that is too big to store or compute on a single computing machine. Today we will discuss fundamental concepts for working with big data using distributed computing, then introduce the tools you need to build machine learning models. We'll start with some naive methods of solving problems, which are meant only as an example. As we move forward, we will make things more realistic. MapReduce is a technique that is used to distribute a data set in parts to different agents. An agent here means a single computer.

Getting into machine learning (ml) can seem like an unachievable task from the outside. However, after dedicating one week to learning the basics of the subject, I found it to be much more accessible than I anticipated. This article is intended to give others who're interested in getting into ml a roadmap of how to get started, drawing from the experiences I made in my intro week. Before my machine learning week, I had been reading about the subject for a while, and had gone through half of Andrew Ng's course on Coursera and a few other theoretical courses. So I had a tiny bit of conceptual understanding of ml, though I was completely unable to transfer any of my knowledge into code.

Conversations around school improvement have fallen under a "School 2.0" vision. As educators imagine the schools of the future, technology often has a central role, but in a particular way. Classrooms need to be organized around the needs of students, with technology that offers engaging, personalized learning experiences rather than ed tech that provides new platforms for worksheet-style assignments. The gap in school quality is clear when it comes to seeing what and how students learn. Students in upper-income neighborhoods more often learn how to think, while students in high-poverty districts are coached to memorize facts, especially when standardized test performance is largely dependent upon this memorization.

If you ask kids today why phrases like "hang up" the phone or "roll down" the window exist, chances are they'll have no idea. Fast-forward to the near future and "search the web" may also cause a few head scratches. "We're evolving, but remain electronic'hunters and gatherers,'" explained Ralph Lucci, cofounder and user experience director at Behavior Design. But that's about to change thanks to today's quickly emerging artificial intelligence (AI) technology for practically every industry, including education. "The day will soon come when we'll sardonically ask ourselves: 'Remember when we had to visit a website and look around for what we needed?' Now the data comes to us."

SchoolWise, a web-based student information system, has launched GoSchoolWise.com, The new site utilizes artificial intelligence (AI) and machine learning (ML) tools such as IBM Watson, a platform that uses natural language processing and personality traits to reveal insights from large amounts of unstructured data, to help students find the right college for them. SchoolWise aims to improve that number by assisting students at the beginning of the college admissions process. By providing a college/student personality matching tool, as well as a financial aid calculator, SchoolWise seeks to give as much information up front as possible and help prevent students from encountering unpleasant surprises, such as financial shocks or environmental discomforts. The personality matching tool on SchoolWise uses IBM Watson to analyze applicant writings and essays to determine personality characteristics and individual college fits.

This is a list of resources I think would be useful for those who are just starting to explore the amazing Machine Learning domain of Computer Science and want to learn more about Neural Networks and their applications. The general idea behind putting these resources together and publishing this list is that when I just started I saw posts with hundreds of links without description and I simply didn't know which of them are worth spending time on. Focusing on most useful ones and giving short summaries instead is a good idea. I am not a Deep Learning expert and everything I wrote down is just my personal experience with these resources, very subjective opinion. In-depth Convolutional Neural Networks course highly recommended if one wants to learn about image recognition, Computer Vision-related problems and so on. The problemset is amazing; it has probably the best numpy tutorial I have ever seen and makes people implement algorithms they saw in lectures in pure Python numpy, which seems to be a great idea as it helps to get better understanding of how everything actually works.

An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample rate using stochastic gradient descent based optimization implemented in the state estimation filtering loop. The performance of the proposed method is compared with state-of-the-art Gaussian process based batch learning methods. Finally, it is shown that the state estimation performance significantly improves due to the online learning of state dynamics.