Machine Learning is rapidly becoming a required and critical component of engineering organizations across the tech industry. From movie recommendation algorithms to self-driving cars, it is clearly an exciting and compelling field. Companies are hiring armies of Machine Learning researchers to solve difficult problems like voice and object recognition. What does this all mean to the average software engineer? In many cases, extremely specialized knowledge is necessary to outperform existing state-of-the-art systems.

Greg Linden may not be a household name, but he changed the way we interact with culture and transformed retail forever. An engineer at Amazon in the late 1990s, Linden worked on a curious problem: how to recommend books without human intervention. Until then Amazon relied on editors who wrote hundreds of reviews every year. It was a costly and time-consuming process. Automating recommendations proved trickier than anyone expected.

Volvo has decided to join the ranks of automakers with offices in Silicon Valley. The Swedish car company is in the process of opening a research center in Mountain View, Lex Kerssemakers, CEO of Volvo's U.S. division, said in an interview. The company is hiring some 70 engineers for the office, he said. Volvo, which is owned by Chinese automaker Geely but operates largely independently, has had an office in Camarillo for about 30 years that focused on car design, Kerssemakers said. Within the past three to four years, engineers based in that office also started to work on car infotainment systems, he said.

Inside – on enhancing humans, Google deploys neural network to translate languages, Yann LeCun talks about the future of AI, giant battle robot has a hard time and more! Here's What the Future of Human Modification Might Look Like In this discussion (47 minutes long), British philosopher David Pearce, Sci-fi author Richard Morgan, and Nicky Ashwell (who is a pioneering user of a new kind of bionic prosthetic) talk about how our technology will impact the future of society and transform what it means to be human. Very interesting, I recommend watch it. After being fitted with implants that read brain signals, monkeys in a Stanford study were able to transcribe segments of Hamlet at a rate of 12 words per minute. The tech could eventually lead to a new means of communication for the severely disabled.

At Experian DataLabs in San Diego, a team of scientists is thwarting bad guys with math. A top-five U.S. credit card issuer recently dumped about 6 billion transaction records on Experian DataLabs to see if its machine-learning mathematical formulas could do a better job of rooting out credit card fraud than the bank's existing system. Experian scientists used neuro-embedding/natural language processing techniques to understand the "syntax" of the credit card data, computer scientist Honghao Shan said. "We thought we had figured it out and went back to them," said Eric Haller, head of Experian DataLabs. "They said, 'How did you do that?' … It turns out we reduced their false positives by half."

AI often conjures up an image of an age when machines act like humans. Some find this exciting, others terrifying, but most find it too far fetched to give it serious thought. The BBC seemed fairly unimpressed by two prominent AI innovations last year. Indeed, the day in which an AI is capable of fully replicating the human brain is still a way off, advances in deep learning and neural networks are making exciting progress in replicating the cognitive operation of our brains and ways of thinking. Though human thinking cannot be fully mimicked these tremendous advances in AI technology shouldn't be ignored and their disruptive potential.

If you're a business executive (rather than a data scientist or machine learning expert), you've probably been exposed to the mainstream media coverage of artificial intelligence or AI. You've seen articles in The Economist and Vanity Fair, you've seen emotional stories about Tesla Autopilot and the threat of AI to mankind by such luminaries as Stephen Hawking, and you might even have seen Dilbert make jokes about Artificial Intelligence and Human Intelligence. So if you're an executive who cares about growing your business, all this AI media coverage may prompt two nagging questions. First, is the business potential of AI real or not? The answer to the first question is that the business potential of AI is real.

In the public cloud business, scale is everything – hyper, in fact – and having too many different kinds of compute, storage, or networking makes support more complex and investment in infrastructure more costly. So when a big public cloud like Amazon Web Services invests in a non-standard technology, that means something. In the case of Nvidia's Tesla accelerators, it means that GPU compute has gone mainstream. It may not be obvious, but AWS tends to hang back on some of the Intel Xeon compute on its cloud infrastructure, at least compared to the largest supercomputer centers and hyperscalers like Google and Facebook, who tend to get chips earlier in the Xeon product cycle. So it has been – and continues to be – with AWS and its GPU compute instances.

You should be, says neuroscientist and philosopher Sam Harris -- and not just in some abstract theoretical way. We're going to build superhuman machines, says Harris, and our collective emotional and analytic response to the dangers is not where it should be. TED has finally released the long awaited talk by neuroscientist and author Sam Harris. In his presentation, Harris emphatically urges the audience to consider just how important the development of artificial intelligence is, and how our emotional response so far is "not appropriate tot he dangers that lie ahead." Even Harris admits,"I am unable to marshal this response, and I'm giving this talk."