The worlds of AI, Deep Learning and Machine Intelligence are rapidly evolving and it's hard to keep up! To help you get up-to-date we've rounded up the latest must-see AI news, interviews and videos. RE•WORK blogs Should We Be Rethinking Unsupervised Learning? Meet the Woman Behind the Personality of Microsoft's AI Assistant'Cortana' Deborah Harrison believes people should come to expect civility, humor, transparency and kindness from interactions with AI, just as they expect the ability to update a calendar. Democratising Deep Learning: Q&A With Prof. Neil Lawrence It has taken 20 years to go from defeating Kasparov at Chess to Lee Sedol at Go, but what have the real advances been across this time?

On Monday, Intel announced that it will purchase Movidius, an Irish company that develops computer vision processors. With newly acquired chips and deep learning algorithms, Intel will be able to imbue its RealSense cameras with independent, low-power image processing. In simpler terms, the company could build drones and other devices that can "see" on their own – no human required. At the most basic level, the biological eye is used to gather information and make decisions. Using camera "eyes," computers can also extract complex understanding from visual data.

The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Machine learning's Predictive Analysis. The link takes you to a corresponding product in The Art of Service's store to get started. The Art of Service's predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications. The platform monitors over six thousand technologies and applications for months, looking for interest swings in a topic, concept, technology or application, not just a count of mentions.

Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com Overview: More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI. Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information.

Zegami is a visual data exploration tool for viewing large collections of images and other visual data. By presenting vast quantities of images within a single field of view, Zegami allows users to quickly see correlations, outliers and relationships by leveraging the innate pattern recognition capabilities of the human brain. This combined with image processing and advanced machine learning techniques like Deep Learning presents a formidable combination of the best aspects of man and machine. In addition, by including associated metadata (structured and unstructured) users can search, sort and filter the images to find exactly what is required. In the above example researchers at the Weatherall Institute of Molecular Medicine, University of Oxford were able to quickly observe the effects of various concentrations of a particular drug against a control group.

Editor's Note: Last month, Dark Reading editors named Deep Instinct the most innovative startup in its first annual Best of Black Hat Innovation Awards program at Black Hat 2016 in Las Vegas. As you reach for a water bottle, you don't pause to analyze its material, size or shape in order to determine whether it's a water bottle. Instead, you immediately reach for it, with complete confidence in its identification. If I show the same water bottle to any traditional computer vision module, it will easily recognize it. If I partially obstruct the image with my fingers, then traditional computer vision modules will have difficulty recognizing it.

Suppose you were to watch a stream of numbers… and given the previous number you saw you had to predict what the next number should be. For example, suppose you saw 1,2,1,2,… We might guess: 1. Or perhaps, what if you saw 0,0,1,2,3…? Should it be 4? It almost feels like a domino effect. In this post we walk through predicting a specific type of spike in domain queries associated with some botnets.

This interview is part of a Machine Learning series. We invited Adam Geitgey, Director of Software Engineering at Groupon, to talk about the difference between machine learning and the older artificial intelligence effort and the progress we've made so far. JAXenter: How are you involved in machine learning? Adam Geitgey: My professional background is primarily in traditional software development, not machine learning. I've worked on scaling large-scale websites, building backend systems, building mobile apps and other things like that.

The car company announced last week that it would no longer use a vision system provided by MobileEye, an Israeli company that supplies technology to many automakers. This comes a few weeks after the National Highway Traffic Safety Administration announced that it was investigating a fatal accident that occurred while one of Tesla's cars was operating in Autopilot mode, a system designed to enable automated driving under a driver's supervision. It is unclear why Tesla is dropping MobileEye, but one reason may be the emergence of newer approaches to automated driving. MobileEye provides what amounts to an advanced image-recognition system, capable of identifying road signs or obstacles, such as other cars or pedestrians, on the road ahead. The company has said that it uses deep learning, a popular machine-learning technique based on training a many-layered network of simulated neurons to recognize input using a large number of training examples.

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts.