Thanks to advances in AI and machine learning, a slow but steady transformation is coming to education -- under the hood. When you compare the typical 21st century classroom with that of the early 1900s, the differences aren't terribly obvious. Teachers will be standing in front, giving instructions and sharing notes on a modern-day version of the old blackboard -- say, an overhead projector or a shared computer display. Students will be sitting at their desks in the classroom or watching via online video-conferencing software. The technology has changed: A lot of the tools and processes have been digitized, some of it has been automated, and geographical barriers have been removed to some extent -- but the actors and elements have remained much the same.

This post is authored by Chris Testa-O'Neill, Applied Data Scientist in the Microsoft Cloud AI team. Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to provide the best platform and tools to make them successful. You can read more Microsoft's approach to AI here. In keeping with our mission, we are currently running a series of popular AI boot camps around the world.

A woman glumly considers her chances of expanding knowledge and skills for a rapidly changing world at the facilities of TCDC Resource Center, one spot to get help. The Fourth Industrial Revolution is at our doorstep, with artificial intelligence (AI), robotics and automation threatening unskilled workers and new graduates who are at risk of being left unemployed by job-killing androids. As these technological breakthroughs take place, automation and AI are compelling blue-collar workers to improve their skills and do more sophisticated work. Those who fail to adapt will be left behind. Plans to cut jobs have already begun in the banking and telecom sectors, signalling a trend that retail may soon follow as customers enjoy self-service through digital channels.

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation". We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate.

Big numbers are nothing new to Quizlet, one of the most widely-used digital study tools in the United States. And according to one website tracker, it is among the most visited sites in the country (besting LinkedIn and Spotify). But there's another number that also has the company excited. This week Quizlet raised an additional $20 million in a Series B round led by Icon Ventures. Other investors include Union Square Ventures, Costanoa Ventures, Owl Ventures and Altos Ventures.

For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning. There seems, however, to be an increasing trend of getting on to the practical from the start, and mixing practice and theory along the way as resources progress. This post presents 5 such resources. Covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks, these resources cover quite a bit of ground. They are also all free, so get reading, get watching, and get coding.

A 2017 Tractica report on the natural language processing (NLP) market estimates the total NLP software, hardware, and services market opportunity to be around $22.3 billion by 2025. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025. In order to shed more light on the growing applications of NLP solutions, Dan Faggella, the CEO of TechEmergence, converses with Vlad Sejnoha, the CTO of Nuance Communications, an organization offering AI and NLP solutions in voice, natural language understanding, reasoning and systems integration. Vlad Sejnoha has been the Senior Vice President and CTO at Nuance since 2001. He holds a Masters degree in Electrical Engineering from McGill University. Vlad has been working in the field of NLP and speech recognition for over 30 years and holds 22 patents to date.

We are living in interesting times, where digital assistants schedule meetings, chatbots work alongside humans as teaching assistants, and your suitcase can now become self driving luggage as showcased at CES, 2018. The implications are just starting to be felt in the workplace. In 2017, I wrote about how The Employee Experience is the Future of Work. Now, as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience. As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

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Is it the German operators that created the AI in 2016, or is it the deep learning machine itself? That was one of the questions a panel of intellectual property experts grappled with at an AI conference in Toronto hosted by Osgoode Hall Law School last week. As businesses struggle to keep step with the rapid advancement in AI, policies and laws are also being stretched, said lawyer Carole Piovesan of McCarthy Tetrault LLP. "Canada is really at the precipice, as is much of the world, of trying to define what its legal framework is going to look like in the face of AI," she said. "But with the current pace of AI innovation -- it's happening so quickly and it's of such a transformative nature -- that policy-makers are being forced to anticipate issues that don't necessarily exist here and now."