Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

AI is touted to change the world in the same way electricity did. But are we moving too fast? While AI has created impact in some sectors, there are some documented instances of enterprises claiming to have automated systems, but actually relying on humans behind the scenes. For our future to stay bright, the real AI startups will need to stand up. On March 22, 2017, Andrew NG, who had been heading Artificial Intelligence (AI) efforts at Chinese tech giant Baidu, announced that he was resigning from the company.

This article is by Featured Blogger Isaac Sacolick from his blog Social, Agile, and Transformation. AI is coming along with artistic impression. There is Paul and e-David that can draw, an AI that can write a Beatles inspired song, followed by an AI that wrote a Christmas song. There's been a lot of work on natural language processing, but natural language understanding remains elusive. It's hard to keep up with all of AI's creative works and how fast AI will go from pattern-based expressions to truly creative ones.

A new global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise. The threat that automation will eliminate a broad swath of jobs, across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. But here's what we've been overlooking: Many new jobs will also be created -- jobs that look nothing like those that exist today. In Accenture's global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs.

Higher Educational Failure - Colleges and universities are still educating the majority of its students for the last century, not this one. Students graduating from medical school, law school, education, journalism, and dozens of other majors have no training in how robotics or AI will impact them. Higher education needs to rethink curriculum for the future. Massive, Permanent Underclass - More than 26m people in the US perform some form of manual labor as their sole source of income. Older Americans are, because of the high costs of health insurance and benefits, with no long-term loyalty by a company, much harder to reemploy.

Dr. Bernhard Schindlholzer is a technology manager working on Machine Learning and E-commerce. In this talk he gave at TEDx FHKufstein, Bernhard Schindlholzer contemplated the implications of ephemeralization - the ability of technological advancement to do "more and more with less and less until eventually you can do everything with nothing" - through artificial intelligence and machine learning. He explores the challenges that this technological approach poses to our economy and, furthermore, how they could be addressed by questioning established norms of our education systems. Dr. Bernhard Schindlholzer is a technology manager working on Machine Learning and E-commerce. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need motivation and the ability to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

The intersection of causal inference and machine learning is a rapidly advancing field. We propose a new approach, the method of direct estimation, that draws on both traditions in order to obtain nonparametric estimates of treatment effects. The approach focuses on estimating the effect of fluctuations in a treatment variable on an outcome. A tensor-spline implementation enables rich interactions between functional bases allowing for the approach to capture treatment/covariate interactions. We show how new innovations in Bayesian sparse modeling readily handle the proposed framework, and then document its performance in simulation and applied examples. Furthermore we show how the method of direct estimation can easily extend to structural estimators commonly used in a variety of disciplines, like instrumental variables, mediation analysis, and sequential g-estimation.

Essential Education: Could one-stop shopping for schools come to L.A. Unified? Welcome to Essential Education, our daily look at education in California and beyond. Several organizations are working together to encourage L.A. Unified to create a universal enrollment system. Students and professors nationwide have launched a campaign to push the Trump administration to enforce Title IX. Several organizations are working together to encourage L.A. Unified to create a universal enrollment system.

What is the hardest concept for high school students to grasp when learning programming? The concept of an "object" is initially hard to understand, making it hard to decide how two objects should be related, and which methods should belong to which classes. At a higher level though, the hardest concept is a willingness to fail. Granted, learning to fail is difficult and uncomfortable, and the vast majority of adults don't think in this way. It's also by no means necessary to being able to program. But it's an incredibly valuable skill - willingness to tolerate and even embrace failure enables risk-taking, experimentation, innovation.