It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or, AI as it's commonly referred to), is an area of computer science where computers are "developed" to behave much the way humans do.

Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

When we offered CS231n (Deep Learning class) at Stanford, we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The students had to implement the forward and the backward pass of each layer in raw numpy. This is seemingly a perfectly sensible appeal - if you're never going to write backward passes once the class is over, why practice writing them? Are we just torturing the students for our own amusement? Some easy answers could make arguments along the lines of "it's worth knowing what's under the hood as an intellectual curiosity", or perhaps "you might want to improve on the core algorithm later", but there is a much stronger and practical argument, which I wanted to devote a whole post to: In other words, it is easy to fall into the trap of abstracting away the learning process -- believing that you can simply stack arbitrary layers together and backprop will "magically make them work" on your data.

The report anticipates economic effects across several fronts. AI, like any new technology, is key to growth because it increases output without requiring increases in labor or capital. "In the last decade, despite technology's positive push, measured productivity growth has slowed in 30 of the 31 advanced economies, slowing in the United States from an average annual growth rate of 2.5% in the decade after 1995 to only 1.0% growth in the decade after 2005," the report states. Any increase in aggregate productivity from adopting artificial intelligence would be a welcomed change. But the resultant job automation is causing alarm.

"How to Become a Super Learning Machine" is an excellent course that focuses on the practical basics of how to learn. The course teaches students about the right attitude to take when learning, the best way to absorb knowledge and how to set goals and achieve them. Thanks to my experience as a teacher, I went into the course understanding most of the concepts that Joe Parys covers. However, thanks to Joe's progressive and hybrid attitude towards learning I was able to take away some new things that have already helped me in my studies. First, Joe covers the importance of surrounding yourself with positive influence.

Palantir CEO Alex Karp Says Going Public Is'A Possibility' Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

You ask your smartphone virtual assistant to make an appointment for you. You receive a message alert from your bank enquiring if you made a certain transaction. You receive recommendations for music or movies or online purchases based on your past behaviour. These are all examples of Artificial Intelligence (AI) entering your daily life. There is no widely accepted definition of the term or what constitutes AI. Definitions are usually based on some variation of computerized systems or computers exhibiting behaviour or thought that is normally demonstrated by humans or requires intelligence (which itself is hard to define). It could involve rationally solving complex problems or taking appropriate actions to achieve objectives in real world circumstances.

Unlike most other lists of top experts, this one is a hand-picked selection, not based on influence or Klout scores, or the number of Twitter followers and re-tweets, or other similar metrics. Each of these experts has his/her own Wikipedia page. Some might not even have a Twitter account. All of them have had a very strong academic and research career in the most prestigious places. Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo).

Organizations like Code.org are working to expand access to computer science and increase participation by women and underrepresented students of color. The technology world has a diversity problem. A recent U.S. Equal Employment Opportunity Commission (EEOC) report found that the high-tech industry employed far fewer African-Americans, Hispanics, and women, relative to whites, Asian-Americans, and men. The difference is especially glaring in Silicon Valley. At Google and Facebook, African-Americans represent just 1% of the tech work force.