For this guide, I spent a dozen hours trying to identify every online machine learning course offered as of May 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My end goal was to identify the three best courses available and present them to you, below. For this task, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

At universities, "people learn how to approach new things, ask questions and find answers, deal with new situations," wrote Uta Russmann, a professor of communications at the FHWien University of Applied Sciences in Vienna. "All this is needed to adjust to ongoing changes in work life. Special skills for a particular job will be learned on the job." Schools will also need to teach traits that machines can't yet easily replicate, like creativity, critical thinking, emotional intelligence, adaptability and collaboration. The problem, many respondents said, is that these are not necessarily easy to teach.

Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.

Artificial intelligence has a diversity problem. Too many of the people creating it share a similar background. To renowned researcher Fei-Fei Li, this paucity of viewpoints constitutes a crisis: "As an educator, as a woman, as a woman of color, as a mother, I'm increasingly worried," she says. "AI is about to make the biggest changes to humanity, and we're missing a whole generation of diverse technologists and leaders." From the chair next to her, Melinda Gates affirms this, adding, "If we don't get women and people of color at the table -- real technologists doing the real work -- we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible."

Joseph Qualls is a clinical assistant professor in the University of Idaho's College of Engineering. He's also an expert in artificial intelligence and the founder of a research and development company that uses AI to solve problems for the U.S. military and industry. In Qualls's vision of the future, AI will dramatically change the way we deliver healthcare, entertain ourselves, conduct warfare and, of course, teach college students. He recently spoke with EdTech Managing Editor Amy Burroughs about what he sees on the horizon. QUALLS: AI is going to come far more quickly than even I can predict.

For marketers, the tide of artificial intelligence has finally crashed ashore. It has begun making waves at industry events like WSDM: The 9th ACM International Conference on Web Search and Data Mining Web Search and Data Mining conference in San Francisco. In his keynote address, Google research fellow Jeff Dean told attendees it's time to embrace techniques like deep learning. Once a land of buzzwords, rapid improvements in machine learning have made AI a pivotal member of marketer's toolkits. As best practice guidelines on Machine Learning at Google shared by Martin Zinkevich (technical) show, Google are actively using Machine Learning in many projects.

In a classic experiment in 1953, students spent an hour doing repetitive, monotonous tasks, such as rotating square pegs a quarter turn, again and again. Then the experimenters asked the students to persuade someone else that this mind-numbing experience was in fact interesting. Some students got $1 ($9 today) to tell this fib while others got $20 ($176 today). In a survey at the end of the experiment, those paid only a trivial fee were more likely to describe the boring activity as engaging. They seemed to have persuaded themselves of their own lie.

Professor Reich comes to Google to discuss the impact of automation & artificial intelligence on our economy. He also provides a recommendation on how we can ensure future technologies benefit the entire economy, not just those at the top.It might be universal basic income or robot taxes. Robert Reich is the Chancellor's Professor of Public Policy at the University of California, Berkeley and Senior Fellow at the Blum Center for Developing Economies. He served as Secretary of Labor in the Clinton administration, for which Time Magazine named him one of the ten most effective cabinet secretaries of the twentieth century. He is also a founding editor of the American Prospect magazine, chairman of Common Cause, a member of the American Academy of Arts and Sciences, co-founder of the nonprofit Inequality Media, and co-creator of the award-winning documentary, Inequality for All.

In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees. Do I need some Skynet to run it?

Two months ago I sat in a conference (the ELI Annual Meeting 2017) with several hundred teaching and learning–focused higher ed professionals listening to Satya Nitta, director of the Cognitive Sciences and Education Technology research department at IBM Research, talk about IBM's next generation super-computer, Watson. In the course of illustrating Watson's uses, Nitta described how one university employed Watson as a teaching assistant for a writing course. The students did not know one of their TAs was a computer. They only knew that Jill Watson was giving them feedback on their writing. I pride myself on being a decent writer and I know that it is difficult to learn the craft well. During his presentation, Dr. Nitta provided samples of the types of feedback'Jill' gave to the students, and I have to admit I was impressed.