The automotive market is seeing accelerated growth and rapid adoption of vision applications that will lead the way to autonomous vehicles. With the complexity of these systems, Tier-1 suppliers, OEMs, and the entire automotive industry are utilizing artificial intelligence and deep learning algorithms to identify objects, determine free space for vehicles and plan the vehicle movement. As companies explore these deep learning algorithms and shift from R&D labs to the realization and deployment of low power embedded solutions, it is important to have a sound foundation in the form of an efficient HW and SW platform that is optimized for CNN workloads and other deep learning approaches.

There are many good reasons why traditional security practices are becoming less effective at protecting against cyberattacks. There is too much security-related data flooding the network from an increasing number of users and devices. There is a lack of skilled personnel to watch over and analyze this data. And the security staff you have likely wastes too much time chasing down false positives. Valuable minutes -- or even hours -- can tick by before analysts and incident responders are aware of a threat.

Azure Machine Learning is a handy tool, absolutely. If I need to run some model quickly to justify gut feeling or to have a simple overview over data, it fits really well. Or, for example, set up a web service from a Machine Learning experiment is really easy, so kudos for that! But there are some things which annoy me time after time, which I really want to be implemented or done differently. Here is my top 3 "wish-list": 1. Navigation inside the experiment mean, honestly...

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

Machine learning may once have been a topic of discussion only for computer scientists and researchers. Now, however, it is a technology businesses are eager to use. The need for machine learning and Artificial Intelligence (AI) is being driven by the massive amount of data being generated today. Statisticians can get insight from this data. But the volume is so large and growing at such a rate, the best way to tackle it is using the very same machines that are in part responsible for creating the data.

In conjunction with the Web Summit conference in Lisbon today, Facebook is unveiling artificial intelligence (AI) software that it's using in order to let users apply and switch artistic styles for live video streams on Android and iOS. After demonstrating the technology at a conference last month, Facebook is now testing the video style transfer technology on mobile in a few countries, and it will be deployed more widely in the near future. The Caffe2go technology Facebook developed in the past three months is an implementation of a hot type of AI called deep learning, which typically involves training on lots of data, like images, and then making inferences about new data. In this case, Facebook has developed pre-trained neural networks that can then make inferences about new data on the fly on mobile. Google did something similar with a part of Google Translate last year, but Google also recently demonstrated neural style transfer technology of its own, although it's not yet been shown to run on mobile devices.

Enterprise success in the digital economy requires levels of speed and flexibility that legacy IT systems simply can't provide. Fortunately, the emergence of big data analytics, artificial intelligence and automation gives enterprises powerful new tools to improve efficiency and decision-making. Over at Information Management, contributor Mark Feldner makes the case that "big data and artificial intelligence (AI) stand to become the driving force behind innovation in the workplace." "By automating previously manual processes, we have more capabilities to identify patterns in real-time and make predictions that can streamline how we run our businesses," Feldner says. The key to making customers happy is to understand and meet their needs.

With the rise of automation in nearly every industry, there is still a considerable debate on the nature of jobs responsible for the automation. Jobs can vary from linguistics in natural language processing, predictive modeling in data mining to software engineers in self-driving cars. However, there has to be some underlying distinction between the jobs, at least as far as machine learning (ML) is concerned. In simple terms, it is a process of training a system to perform a task without describing how it should perform the task. A more technical definition would be: "… a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E." 1 This involves taking a series of inputs, feeding them into a system, and allowing a system to learn what is a desired output.

The researchers, working with Google's artificial intelligence division DeepMind, trained the software on more than 30,000 videos of test subjects speaking sentences. Over time, it would match certain words with particular lip movements to learn what words were being spoken. The researchers then played it further videos of people speaking sentences and the LipNet software succeeded with 93.4 per cent accuracy. This compares to 52.3 per cent for hearing impaired students, and surpassed other lip-reading programs. Unlike previous software, LipNet digested the phrases as full sentences, and allowing it to put words in context rather than decipher them individually allowed much greater accuracy.

Historically, when new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc.), more and more industries will put the technologies into use, and more and more startups will appear with new ideas of how to disrupt the status quo with these technologies. By my predictions, the AI revolution isn't coming, it's already here, and we'll see it first in a few key sectors. Most people agree that healthcare is broken, and many startups believe that the biggest answer is putting the power back in the hands of the patient. We're all carrying the equivalent of Star Trek's tricorder around in our pockets (or an early version, at any rate) and smartphones and other smart devices will continue to advance and integrate with AI and big data to allow individuals to self-diagnose.