As cool as thermal cameras may be, they're not usually very bright -- they may show you something hiding in the dark, but they won't do much with it. FLIR wants to change that with its new Boson thermal camera module. The hardware combines a long wave infrared camera with a Movidius vision processing unit, giving the camera a dash of programmable artificial intelligence. Device makers can not only use those smarts for visual processing (like reducing noise), but some computer vision tasks as well -- think object detection, depth calculations and other tasks that normally rely on external computing power.

In a new article in Fortune, Facebook executives detail how their research into machine learning and artificial intelligence allowed them to build a social network that serves photos, text posts, and video to more than a billion people around the world. Actually, let's rephrase that sentence: it's only because of machine learning and artificial intelligence that Facebook could effectively scale its operations to such gargantuan size. You simply can't hire enough human beings to tag, sort, and monitor every aspect of such a huge system. But that's not to say that machine learning is the silver bullet that will allow any technology company to ramp up to meet demand. "It seems like a very useful thing to build a platform to support [machine learning algorithms] but you discover that each application that uses machine learning needs a different application to use it," Andrew Moore, the dean of computer science at Carnegie Mellon University, told the magazine.

Deep learning machines could help decode ISIS as a network and develop strategy for defeat. The use of deep learning machines could help the Pentagon decode the structure of ISIS as a network and allow for a more precisely, developed strategy for its defeat, according to Pentagon Deputy Secretary Robert Work. He was making the case for using artificial intelligence (A.I.) for open-source data crunching, Inverse.com "We are absolutely certain that the use of deep-learning machines is going to allow us to have a better understanding of ISIL as a network and better understanding about how to target it precisely and lead to its defeat," said Secretary Work, according to DoD's website. Speaking at an event organized by the Washington Post, Work said he had his epiphany while watching a Silicon Valley tech company demonstrate "a machine that took in data from Twitter, Instagram, and many other public sources to show the July 2014 Malaysia Airlines Flight 17 shoot-down in real time."

Modeling with machine learning is a challenging but valuable skill for anyone working with data. No matter what you use machine learning for, chances are you have encountered a modeling or overfitting concern along the way. This white paper shows how can overcome these challenges, including how to choose the right classification model for your data and how to avoid and correct for overfitting. Finally, you'll see how much easier these tasks can be when you use MATLAB .

Have you heard the one about how our jobs are about to be snatched away by machines? Or how artificial intelligence will ultimately rise up against us? AI is a field full of tropes, many of which come from places of truth: AI is evolving at an incredible speed, and humans are teaching some AI to learn using the same basic model found in our own craniums. But for a more realistic take on the future of AI, look no further than the many software engineers and companies that have struggled to create an intelligent system that can identify cyber attacks. "We were trying to figure out what is the foundational problem--why do we have so many cyber attacks and data breaches that are going undetected?" says Kalyan Veeramachaneni, a research scientist at MIT's Computer Science and Artificial Intelligence Lab and the author of a paper released today titled "Training A Big Data Machine To Defend."

There has been an explosion in machine learning activity, and Shivon Zilis recently mapped out the current machine intelligence ecosystem as we enter 2016. This is one of the key areas that we'll be following this year. While the opportunities here are tremendous, the exuberance surrounding machine learning distracts startups from a key hurdle: it's data, not algorithms, that will dictate who wins in this space. Algorithms have largely been commoditized by now, so a machine learning company built around publicly accessible data isn't defensible. But, startups face a serious chicken and egg problem: they have to convince people to give them data, but the machine intelligence service won't be useful until people (and a lot of people) are actually using the service and sharing their data.

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available.

Accenture has introduced an intelligent automation platform for application services that provides human technology workers with better productivity and insights to enhance business performance. The platform, Accenture myWizard, augments human technologists with virtual agents powered by artificial intelligence. It helps humans and machines reach their exponential potential in application services. The platform currently includes several intelligent virtual agents that use machine learning to collaborate with their human co-workers. The platform brings together various Accenture industry assets such as intelligent and analytics tools and methods, as well as tools from across the company's alliance partner ecosystem.

Machine learning and artificial intelligence (AI) works best on large volumes of data. One would think that with all its complexity and its mountainous volumes of data, the medical industry would be the perfect place for AI to be a disruptive force. The problem isn't that AI isn't acquiring enough data to be a disruptive force. The problem is that it's not acquiring the right data to solve some of the most egregious cost increases in the history of health care industry. In 2014, the U.S. Government Accountability Office reported 77.4 billion in improper payments of Medicare and Medicaid collectively.

Even though they are a 38-year-old company, FLIR System's advanced, miniaturized thermal imaging sensors and cameras keep them hip in the world of computer vision where mobile startups are propelling the field. This morning they announced a new product--the Boson Thermal Camera. There are many more things these cameras can do…even the super-resolution similar to the much internet-maligned CSI "zoom in" is actually possible through software manipulation of these kinds of cameras according to Movidius's Jack Dashwood. One is the continued trend in miniaturization and its implications on interactive eyewear. The second is the fact that the Boson has a processor integrated directly into it.