The 2012 publication Race against the Machine makes the case that the digitalization of work activities is proceeding so rapidly as to cause dislocations in the job market beyond anything previously experienced [1]. Unlike past mechanization/automation, which affected lower-skill blue-collar and white-collar work, today's information technology affects workers high in the education and skill distribution. Machines can substitute for brains as well as brawn. On one estimate, about 47% of total US employment is at risk of computerization [2]. If you doubt whether a robot or some other machine equipped with digital intelligence connected to the internet could outdo you or me in our work in the foreseeable future, consider news reports about an IBM program to "create" new food dishes (chefs beware), the battle between anesthesiologists and computer programs/robots that do their job much cheaper, and the coming version of Watson ("twice as powerful as the original") based on computers connected over the internet via IBM's Cloud [3]. On the darker side, you do not have to be paranoid to be paranoid about the potential technologies that the super-secret computers of the US National Security Agency (NSA) have on their digital drawing-boards.

Nothing reveals as much about a society, and its future, as its high schools. Yet amid accelerating change -- widening inequality, unprecedented globalization and technological advances -- they've woefully lagged behind. There are, of course, exceptions. Follow OZY's special series High School, Disrupted to find out about the global leaders, cutting-edge trends and big ideas reimagining secondary education -- for the better. From Siri handling our schedules to smart cars driving themselves, artificial intelligence (AI) has turned our world upside down -- except in education.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

John McCarthy coined the term Artificial Intelligence in 1955. Since then, the AI industry at large has seen dramatic ups and downs -- progress and promise mixed with disappointment and disillusion. But now with the convergence of Megatrends on massive data, lightning fast processing speeds, and renewed competitive fever from the American MAFIA (Microsoft, Alphabet, Facebook, IBM, Amazon), AI is poised to cause disruption on a scale that could surpass the Internet itself. As we prepare for a wave of AI first companies (@sundarpichai) and AI natives (Ryan Hoover), every person in the innovation economy will need to understand how AI will (or will not) change their industry and their lives. These titans shape the conversation and have the most ability to move the entire AI industry.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

On a summer day four years ago, a Stanford University computer-science professor named Andrew Ng held an unusual air show on a field near the campus. His fleet of small helicopter drones flew under computer control, piloted by artificial-intelligence software that could teach itself to fly after watching a human operator. By the end of the day, the copters were hot-dogging--flipping, rolling, even hovering upside down. It was a milestone for the field of "machine learning," the same area of artificial intelligence that lets Amazon recommend books based on a shopper's previous habits and helps Google tailor search results to a user's behavior. Mr. Ng and his team of graduate students showed that artificial-intelligence software could control one of the hardest-to-maneuver vehicles and keep it stable while flying at 45 miles an hour.

The scientist behind Google Glass wearable technology has criticised the use of restrictive and "fear-based" testing regimes in education, describing a lack of innovation in the system as a crisis. Sebastian Thrun, who led the Google research lab responsible for self-driving cars and Google Glass, made the comments ahead of a meeting with education minister Michael Gove. Thrun, who is one of the world's most influential computer scientists, is exploring online collaborative education – known as Moocs, which stands for "massively open online courses". He has developed a project called Udacity, which is working with two colleges and workplace learning schemes. "The education system is based on a framework from the 17th and 18th century that says we should play for the first five years of life, then learn, then work, then rest and then die. I believe we should be able to do all those things all the time," said Thrun.

Soon they will be sophisticated enough to fill certain faculty roles at traditional universities. But to make this revolution work for students, academic leaders at those traditional institutions will need to broker a peace between artificially intelligent teaching programs and their human counterparts, according to a new report written by the former presidents of two prominent traditional universities on behalf of the nonprofit Ithaka S R. Online education has enabled many colleges to transition into the prevailing modern medium while adding new sources of revenue in times of scarcity, according to the Ithaka report. However, these innovative colleges have shown less interest in using the novel medium to curb tuition charges and measure learning outcomes. The report, called "Barriers to Adoption of Online Learning Systems in U.S. Higher Education," was co-written by Lawrence S. Bacow and William G. Bowen, the former presidents of Tufts and Princeton Universities, respectively, along with several Ithaka analysts. It was bankrolled by the Bill & Melinda Gates Foundation.

In his 10-week course Ng takes a an engineering-oriented approach to Machine Learning that concentrates on statistical models. If you are looking for an alternative Coursera also has Neural Networks for Machine Learning, a class taught by University of Toronto professor, Geoffry Hinton who is a leading proponent in the field from a cognitive science perspective. His eight-week course sets out to teach students artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion. Its prerequisites are programming proficiency in Matlab, Octave or Python, plus knowledge of calculus, linear algebra and probability theory.

Anant Agarwal is a professor of electrical engineering and computer science at the Massachusetts Institute of Technology and president of edX, a leading provider of massive open online courses, known as Moocs. Created by the Massachusetts Institute of Technology and Harvard, edX is a non-profit, "open-source" organisation. Everybody should have access to a high-quality education. At edX we are applying technology to improve education in quality, scale and accessibility. We have about 1m people enrolled in edX.