We've all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology. However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, "…the technical feasibility of automation is lowest in education." There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education.

I already have a BA in Linguistics (French and German language teacher), but I really want to go into NLP and am doing my MA in Computational Linguistics now (my first year is about to start). I've read "Speech and Language Processing", started to learn advanced math needed for NLP (stats, the theory of probability, linear algebra) and am doing a course "Python for everybody" on Coursera now. My next plan was to study NLP for Python and try to volunteer for NLTK. But when I skim Data Scientist or NLP positions on job websites, I always read something like "the Master's degree in Math or CS" in requirements. On the other hand, when I finish my MA, I'll be 28 already, and am not sure, that I can afford to get another degree. But I have a feeling, that all the interesting things connected with NLP are more about machine learning, programming and math, and this is what I really want to do in life.

In 2015, a poll of 200 senior corporate executives conducted by the National Robotics Education Foundation identified robotics as a major source of jobs for the United States. Indeed, some 81% of respondents agreed that robotics was the top area of job growth for the nation. Not that this should come as a surprise: as the demand for smart factories and automation increases, so does the need for robots. According to Nearshore Americas, smart factories are expected to add $500 billion to the global economy in 2017. In a survey conducted by technology consulting firm Capgemini, more than half of the respondents claimed to have invested $100 million or more into smart factory initiatives over the last five years.

Lots of people will tell you they're nervous about the changes artificial intelligence will bring to the world, but Andrew Ng is confident it's all for the best. The former AI chief of Baidu and founder of Google Brain is on a mission to build what he calls an "AI-powered society" -- one where smart computers are as integral to businesses as electricity. And to bring about that future, Ng, now an adjunct professor at Stanford, will share what he knows best by teaching. Today, Ng is launching a new course on deep learning on Coursera, the online education site he co-founded. The syllabus will follow his popular machine learning course, which has attracted some 2 million enrollments since its launch in 2011.

If the next era of human progress is built using AI, who gets to engineer it? Who will have the coding skills to use the software for creating AI products, or even more importantly, the skills to write that software? In an attempt to make the answer to those questions "anyone who wants to," Andrew Ng is releasing a new set of courses teaching deep learning on Coursera, the online learning platform he co-founded in 2012. Coursera was originally set up to offer an online class in machine learning; deep learning is a variety of that, involving exceptionally large datasets. The original machine learning course attracted more than 2 million students, Ng tells MIT Tech Review.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. You can find all the interviews here. We'll be posting them regularly on Robohub.

If you were a data scientist three years ago, you could pretty much write your own ticket. Everybody in the industry, it seemed, either wanted to hire a data scientist, or wanted to be one. But today, thanks to a confluence of factors, organizations are beginning to question whether they need these digital unicorns at all. The key to understanding the dynamic at play here is to separate the activity of "data science" from the persona of "data scientists." Organizations most definitely want to do data science to get insight from their data.

The Deep Learning Specialization consists of five different courses. The courses are free to take, but you need to sign up for a subscription of $49/month if you want access to the graded assignments or earn certificates. There is a seven day free trial. The individual courses are free, but you need to visit the course pages separately (you can't sign up to them from the Specialization page). Though the courses officially start on 15 August, the course materials for the first three courses are already available.

More than half a century later, the disciplines have graduated from the theoretical to practical, real world applications. We'll have some of the top minds in both categories to discuss the latest advances and future of AI and ML on stage and Disrupt San Francisco in just over a month. We'll be joined on stage by Brian Krzanich of Intel, John Giannandrea of Google, Sebastian Thrun of Udacity and Andrew Ng of Baidu, to outline the various ways these cutting edge technologies are already impacting our lives, from simple smart assistants, to self-driving cars. It's a broad range of speakers, which is good news, because we've got a lot of ground to cover in some of the industry's most exciting advances. John (JG) Giannandrea, SVP Engineering at Google: Giannandrea joined Google in 2010, when the company acquired his startup Metaweb Technologies, a move that formed the basis for the search giant's Knowledge Graph technology.

Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.