You might have detected a "bit" of intentional tongue-in-cheek in my recent coverage of the most "intriguing" products released at the 2017 Consumer Electronics Show, but rest assured that the news wasn't all bizarre. Some truly significant technologies (and products based on them) were both introduced for the first time and notably advanced from prior versions. I thought I'd devote this particular post to showcasing three that particularly stuck with me. Learning goes deep While the world may not need an electric toothbrush that claims to have "artificial intelligence", both that term and the comparable "deep learning" were everywhere at CES, often for good reason. Traditionally, computer vision, audio analysis, and other similar applications have relied on special-purpose algorithms custom-designed to recognize particular patterns.

In the past twenty years, various large corpora of computer-understandable reasoning knowledge have been developed (Harrison et al., 2014). Apart from axioms, definitions, and conjectures, such corpora include proofs derived in the selected logical foundation with sufficient detail to be machine-checkable. This is either given in the form of premises-conclusion pairs (Sutcliffe, 2009) or as procedures and intermediate steps (Wenzel, 1999). The development of many of these formal proofs required dozens of person-years, their sizes are measured in tens of thousands of human-named theorems and the complete proofs contain billions of low-level inference steps. These formal proof libraries are also interesting for AIbased methods, with tasks such as concept matching, theory exploration, and structure formation (Autexier & Hutter, 2015). Furthermore, the AI methods can be augmented by automated reasoning: progress in the development of efficient first-order automated theorem provers (ATPs) (Kovács & Voronkov, 2013) allows applying them not only as tools that redo the formal proofs, but also to find the missing steps (Urban, 2006). Together with proof translations from the richer logics of the interactive systems to the simpler logics of the ATPs this becomes a commonly used tool in certain interactive provers (Blanchette et al., 2016). Many significant proof developments covering both mathematics and computer science have been created using such technologies. Examples include the formal proof of the Kepler conjecture (Hales et al., 2015), or the proof of correctness of the seL4 operating system kernel (Klein et al., 2010).

In early August, IBM announced that it will acquire Merge Healthcare Inc., a company that sells systems that help medical professionals access and store medical images. This move is a critical step in IBM's plan to put AI to work medically by training its Watson software to identify maladies like heart disease and cancer. Merge is valuable to IBM because it owns 30 billion images, including computerized tomography, X-rays, and magnetic-resonance-imaging scans. The company can use these images in its deep learning training program. IBM is hoping that the same kind of software that lets Flickr recognize your face or a dog in your photos can help Watson identify symptoms of diseases.

This is the first article in a series "quick guide to digital tech", where I will cover some of the most discussed topics in digital technology. With the series I aim to give a simple understandable overview, with examples most readers can relate to or have heard of. In the end, I will give a short comment on where I think the technology is today and where I see it heading in a near and far future. Artificial Intelligence may be the most disruptive technology the world has seen since the Industrial Revolution. It describes every aspect of learning or any other feature of intelligence that can be described so precisely that a machine can simulate it.

Artificial intelligence is affecting everything from automobiles to health care to home automation and even sports. It's also going to have a measurable impact on software development, with developers becoming more like data scientists, an AI official with Nvidia believes. AI and deep learning will mean changes in how software is written, said Jim McHugh, vice president and general manager for Nvidia's DGX-1 supercomputer, which is used in deep learning and accelerated analytics. The long-standing paradigm of developers spending months simply writing features will change, he explained. With the advent of AI, data is incorporated to create the insight for software.

With another tsunami of hype about the wonders artificial intelligence can bring, IT professionals would be wise to seek higher ground until they can sort out exactly how machine learning business applications can help them. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

The Arterys Cardio DLTM application is vendor agnostic and was developed using data from several thousand cardiac cases. The software produces editable automated contours, providing precise and consistent ventricular function in seconds. The trained deep learning algorithm was validated as producing results within an expected error range comparable to that of an experienced clinical annotator. This clearance enables Arterys to make use of its unique clinical annotation platform, which collects ground-truth data every time a user views a study on Arterys.com. In the future, the deep learning model can be optimized as new data is collected from all global users.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster.

This is a follow up to the Introduction to Machine Learning video.

Have you ever spent time at work looking at something on your monitor that isn't work related? Afraid your boss might catch you? A programmer with the same problem built Boss Sensor, a system that uses deep learning AI to switch your screen to something work-related when your boss is near. Hiroki Nakayama explains Boss Sensor in a post on the blog Ahogrammer. First, he trained a convolutional neural network (CNN) to recognize his boss's face.