Technology that can mimic and improve on the cognitive abilities of human brain has been the stuff of dystopian movie storylines for decades. But for large companies and research labs, such artificial intelligence has been a longstanding pursuit for both day-to-day and groundbreaking uses. Now, a specific breakthrough in AI -- deep learning -- is allowing business to use the vast amounts of newly available data to teach computers how to learn. Deep learning uses layers of algorithms known as neural networks, which are designed to loosely represent the layers of the human brain. These algorithms allow machines to learn patterns.

Take a sneak peek at the awesome innovative technologies built on Intel architecture featured at this year's Intel Developer Forum. Build Caffe* optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks. Find out how the Intel Xeon processor E5 v4 family helped improve the performance of the Chinese search engine Baidu's* deep neural networks Read about Bob Duffy's experiences getting his Microsoft* Surfacebook* set up to best maximize virtual reality (VR) applications. Intel Developer Zone experts, Intel Software Innovators, and Intel Black Belt Software Developers contribute hundreds of helpful articles and blog posts every month.

Fairfax, VA, August 25 2016 – Invincea, the leader in machine learning for endpoint protection, announced today that its deep learning model for analyzing unknown malware is now fully integrated in the VirusTotal site. VirusTotal is a "service that analyzes suspicious files and URLs and facilitates the quick detection of viruses, worms, Trojans, and all kinds of malware"[1]. By integrating with VirusTotal, Invincea is pushing machine learning into mainstream cyber security solutions. By participating in the VirusTotal community, Invincea is continuing to fulfill their three key principles to security market transparency and accountability: participate in independent 3rd party testing, work towards commonly accepted standards, and avoid being a black box. As part of these principles, Invincea became one of the first next gen endpoint security companies to join the Anti-Malware Testing Standards Organization (AMTSO) in June.

Why guess what will happen in the future? Put your computer to work and allow it to predict it for you. Machine Learning allows computers to learn from and make predictions on data, saving you the headache of trying to do it. With the Complete Machine Learning Bundle you can learn all you need to know about Machine Learning. You'll get over sixty hours of learning about artificial intelligence with courses on quantitative trading, R, Hadoop and MapReduce, Java, decision trees and random forests, deep learning and computer vision, and Python.

When you hear the words "cyborg," or "augmented human," you inescapably picture Arnold Schwarzenegger as The Terminator, the Borg from Star Trek: The Next Generation, or perhaps The Six Million Dollar Man, if you're a little older. In Hollywood, any futuristic pairing of man and machine had better be so superawesome, or so superscary, that you'd be willing to spend a good couple of hours (and dollars) being entertained by it. The crazy thing is, even though these images come from a time when technology was barely able to fake the on-screen action, we are now on the cusp of the real thing. We're entering an age that will enhance who we are as humans in ways that go well beyond these cultural clichés. Here's where the art and science of human augmentation is today, and a tantalizing peek at where it's going in the not-too-distant future. Our time as pure, natural humans has an expiration date. It had an expiration date, and it was about 2 million years ago. It was around that time that we first put technology to use to enhance what we could accomplish with just our bodies. It took the form of a crude cutting tool, and though it might not have been much to look at, it beat the hell out of having to use our teeth for everything. "It's going overboard but we don't know how to do it another way at the moment." This, according to Super You: How Technology is Revolutionizing What it Means to Be Human author Andy Walker, was the moment the human race became cyborgs. "Any technology that enhances your natural biology and gives you an advantage over somebody else to either survive or procreate is'cyborgism,'" Walker says. His definition likely flies in the face of your lovingly preserved image of Steve Austin leaping over walls in slow motion. Walker isn't the only one who takes such a liberal view of our cyborgian nature.

Machine learning and its artificial intelligence parent are probably most often regarded by regular-ass people as kind of opaque and esoteric subjects. Or even just tech buzzwords, which is a shame because it doesn't have to be like that. These things are just tools and as tools they can be employed for extremely complex, inscrutable-seeming tasks found in fields like neuroprosthetics or machine perception, or they can be used for everyday things like classifying spam. In other words, machine learning doesn't have to be brain surgery, though it can be useful for that. At the same time, getting into something like Google's TensorFlow open-source machine learning library is pretty daunting.

Computers that are already true wizards in terms of the first type of learning are now catching up at an extraordinary pace as regards the other two types.

As anyone who's been following iMore for a while know, concerns that Apple was behind when it came to key strategic technologies like artificial intelligence and machine learning have been misplaced. Machine learning, my briefers say, is now found all over Apple's products and services. Apple uses deep learning to detect fraud on the Apple store, to extend battery life between charges on all your devices, and to help it identify the most useful feedback from thousands of reports from its beta testers. Machine learning helps Apple choose news stories for you. It determines whether Apple Watch users are exercising or simply perambulating.

I posted a question on the Computer Vision subreddit about the most important papers in Computer Vision and a lot of people recommended that I should familiarize myself with Machine Learning papers to be up to date on the latest of what is going on in Computer Vision. What are some of the most important papers in the field of Machine Learning for Computer Vision?

The world depends on coral reefs, but they're disappearing – ravaged by climate change, coastal development, overfishing and pollution. With a quarter of Earth's reefs already gone, scientists are racing to save them, and they're getting a big boost from GPU-accelerated deep learning. Although reefs cover less than one percent of the ocean floor, they provide food and shelter for more than a quarter of all marine species, support fish stocks that feed more than a billion people and provide jobs to millions of people in coastal areas. Scientists study images of coral reefs to measure reef health and changes over time. That's now done by human experts, but it's costly and time-consuming.