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 my previous blog on artificial intelligence (AI), I dealt with the general characteristics of AI and machine learning. Thanks to complex virtual learning techniques, machines are now able to perform a wide range of physical and cognitive tasks. And the efficiency and accuracy of their work is expected to increase as AI systems advance through machine learning, big data and increased computational power. The benefits are clear, but there are also concerns for the future of human work and employment. If indeed machines continue to improve their performance beyond human levels, a natural question to ask is whether machines will put humans' jobs at risk and reduce employment.

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.

Last week we introduced you to GoGuardian Admin 2.0, launching this June. With Admin 2.0, we took our first iteration of the Admin product and improved every feature to offer a better understanding of student online behavior and make students safer. The final product is a direct reflection of the insights and feedback given to us by you, our 2500 customers, our 200 alpha users, and 15 alpha organizations. Introducing Smart Alerts This week we're going to take a deep dive into perhaps the most game-changing aspect of Admin 2.0 - the new "Smart Alerts" feature. Check out this video for a closer look at Smart Alerts' new and improved features!

I'm a software engineer with 10 years of experience who recently decided to switch my focus to machine learning. I did the coursera course and did CS231n: Convolutional Neural Networks for Visual Recognition, read up on basic theory, did some image processing networks like VGG, Resnets and most recently trying to get Faster-RCNN to work, so my currently knowledge is ML basics and heavily focussed on ML in the Image domain. I recently landed my first ML job at a company that does mostly NLP, so I lack a lot of knowledge in that domain. I'm currently reading the NLTK book, which has been very approachable in introducing basic concepts in a code-focussed way. So I was wondering if anyone could point me to some good mid to advanced level resources (online courses/videos/books) to get up to speed with where the field is at now, to help me understand current research and more advanced concepts?

Young people who are going to be leaving school and starting their tertiary education studies soon might be wondering what career paths they should focus on. And for anyone who is in touch with what is happening in the world and future technology trends, the answer should be obvious. Literally changing the way our world operates, we're all dealing with aspects of this tech daily and likely don't even know it. Youth unemployment globally is reaching higher levels than ever and is set to continue increasing this year. Research by the International Labor Organization shows unemployment is set to increase by half a million this year to reach 71 million – the first such increase in three years.

Swedish startup Gavagai AB, a language technology company that originated at the Swedish Institute of Computer Science, has mastered 40 human languages with its language analysis software. Now, researchers from the KTH Royal Institute of Technology are teaming up with Gavagai AB to take on the language of dolphins, in a project undoubtedly focused as much on testing and expanding the system's capabilities as deciphering the thoughts of dolphins. The team will monitor bottlenose dolphins in a wildlife park and use Gavagai's artificial intelligence (AI) language analysis technology to decode the sounds and, if all goes according to plan, compile a dictionary of dolphin language. The team is confident that they'll be able to do this, thanks not only to the AI capabilities of the Gavagai AB system, but also to the availability of more dolphin data, larger computational resources, and newer recording methods. "We hope to be able to understand dolphins with the help of artificial intelligence technology," KTH adjunct professor and Gavagai co-founder Jussi Karlgren said in an interview with Bloomberg.

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. When Timnit Gebru attended a prestigious AI research conference last year, she counted 6 black people in the audience out of an estimated 8,500. As a PhD candidate at Stanford University who has published a number of notable papers in the field of artificial intelligence, Gebru finds the lack of diversity in the industry to be "extremely alarming" and effectively an "international emergency."