I was downvoted to hell on my main post, and the replies ("but Kaggle and Gaussian noise!") strongly suggest we have a lot of enthusiasts/amateurs in the subreddit. Working professionals attracting top dollar know that focusing on non-NN techniques is currently a poor idea at best. I can't convey that better than Google/Facebook/everyone else in the world already have, through their hiring and their solutions to pretty much everything in the past few years. I feel for the people new to the field, and they should have exposure (through books and basic problems) to other techniques. They should then take "big" data sets, run NNs and the other techniques on them, and see what the performance differences are.

Machine learning and artificial intelligence have gained notoriety among the general public through applications such as Siri, Alexa or Google Now. This is most evident in compute technology, which grew more and more powerful to make machine learning and AI happen. It's less obvious how networking contributes to and benefits from AI, he said. "I've never seen a technology this powerful that's moving as fast or is as cool," Meyer said. "Everybody I know who's in networking or any other discipline when they understand what this is, they want to work on it."

And I immediately think they will die irrelevant.

This is an age of techno-utopianism. Topics like big data, advanced analytics, and artificial intelligence are at the forefront of the CEO agenda, a sign that companies see technology as a potential answer to many or even most of their challenges. We have good reasons to be excited: an explosion of data and advances in analytics have enabled technology to perform well-defined but complex tasks like recommending movies and diagnosing cancer--not only independently of humans but in many cases better than people can. So it's not implausible to think that technology could also address broad, open-ended, and ambiguous problems like developing and executing a business strategy. In fact, we've spoken to business leaders who believe in such an outcome--and companies such as Amazon and Alibaba are already beginning to make it a reality.

Self-driving cars seem an awful lot like magic. So, if you wanted to learn how it all hangs together, where would you even start?

Elon Musk, founder of SpaceX, Tesla and PayPal, is worried about killer robots. "You know those stories where there's the guy with the pentagram and the holy water, and he's sure he can control the demon?" he has warned. That "unfriendly AI", as it is known in tech circles, would not be a boon for humanity is an easy cause to get behind. But unlike Musk – a tech entrepreneur who stands to make huge financial gains from AI in the short term – most of us don't have the luxury of taking the long view. The defeat, last week, of one of the world's strongest Go players, Lee Sedol, demonstrates the qualitative leap in AI that has already taken place.

Deep Learning Robot is built for research in deep learning and mobile robotics. It comes with pre-installed Ubuntu, Caffe, Torch, Theano, cuDNN v2, and CUDA 7.0. With researchers creating new deep learning algorithms and mobile robots collecting unprecedented amounts of data, computational capability is the key to unlocking insights from data in real time.

The moment that humanity is forced to take the threat of artificial intelligence seriously might be fast approaching, according to futurist and theoretical physicist Michio Kaku. In an interview with CNBC's "The Future of Us," Kaku drew concern from the earlier-than-expected victory Google's deep learning machine notched this past March, in which it was able to beat a human master of the ancient board game Go. Unlike chess, which features far fewer possible moves, Go allows for more moves than there are atoms in the universe, and thus cannot be mastered by the brute force of computer simulation. "This machine had to have something different, because you can't calculate every known atom in the universe -- it has learning capabilities," Kaku said. "That's what's novel about this machine, it learns a little bit, but still it has no self awareness ... so we have a long way to go."

That is one of the slogans found on the hauntingly cheerful website for Eternime – a startup firm aiming to let you store your memories and your personality in digital form past your physical expiration date (also known as death). They hope to feed the data into chatbots that will allow us to "speak to the dead". This is illustrated through a shoddy photoshop of a bearded man's head floating inside a laptop. Enjoy having a chat with that, if you can. The website goes on to describe the service as "a library that has people instead of books", which truly does add new meaning to the phrase "I'm checking you out", doesn't it?

The team from the university's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx developed the new platform that can identify cyberattacks 85% of the time and even reduce the amount of false positives by a factor of five. AI2 goes through data and then spots suspicious activity through unmanned machine learning. From there, human reviewers check for signs of a security breach, a solution that can predict attacks with precision and eliminate the need to pursue bogus intelligence leads. AI2 uses three machine learning algorithms for detecting suspicious events, but just like other AI systems it also needs human feedback to verify its findings, so the system is constantly being enhanced through the team's so-called'continuous active learning system'. For computer science professor Nitesh Chawla of University of Notre Dame, the research is a potential'line of defense' against fraud, account takeover, service abuse, and other attacks faced by consumer-oriented systems today.