What sets the present generation of cognitive computing solutions apart from the past is the advent of hugely more powerful and cost-effective computer systems that can process information at high speed, says David Powers, professor of computer science and director for the Centre of Knowledge and Interaction Technology at Flinders University. Combined with big data collections forged from enterprise databases, social networks, sensors – even CCTV – plus the advances in machine learning and deep neural networks, this means it's now possible to use cognitive platforms to generate far more useful insight. Powers, who has been working in the field of artificial intelligence and cognitive science for close to 40 years, says that the support rendered by cognitive computing platforms already ranges from the automation of contact centres to handle routine queries, to training young surgeons using haptic devices, which give tactile feedback, before the doctor is let loose with a scalpel on a human body. He describes the present generation of cognitive platforms as offering "triage" style support, dealing with the routine automatically and supporting humans through more complex challenges. He has been involved with a series of university spin-off businesses such as Clevertar.com.

Artificial intelligence (AI) in fictional films is a thing of the past. In fact, the ever-evolving field of AI has become ubiquitous, making big strides in technological innovations. But as artificial intelligence revolutionizes healthcare, education, businesses and becomes a "meta-solution" to the world's biggest problems, its ubiquity spawns one major question: Will future AI wipe out humanity? Artificial intelligence has a more useful presence in humanity today. In fact, many entrepreneurs and innovators are investing more time and money on AI-driven programs, including Google and chipmaker Movidius, as previously reported.

Those of us working in creative fields have often consoled ourselves that although automation may claim many other jobs, at least robots can't make art. That's not exactly true for a variety of reasons (depending on how you define'art'), but it really goes out the window when you look at these astonishing images released recently by Google. The landscapes produced on the company's image recognition neural network reveal the answer to the question, "Can artificial intelligence dream?" It turns out that it can – sort of. Google created a method to'teach' its neural network to identify features like animals, buildings and objects in photographs.

The early identification of toxic paraquat (PQ) poisoning in patients critical to ensure timely and accurate prognosis. Though plasma PQ concentration has been reported as a clinical indicator of PQ poisoning, it is not commonly applied in practice due to the inconvenient necessary instruments and operation. In this study, we explored the use of blood routine indexes to identify the degree of PQ toxicity and/or diagnose PQ poisoning in patients via machine learning approach. Specifically, we developed a method based on support vector machine combined with the feature selection technique to accurately predict PQ poisoning risk status, then tested the method on 79 (42 male and 37 female; 41 living and 38 deceased) patients. The detection method was rigorously evaluated against a real-world dataset to determine its accuracy, sensitivity and specificity. Feature selection was also applied to identify factors correlated with risk status, and results showed that there are significant differences in blood routine indexes between dead and living PQ-poisoned individuals (p-value 0.01).

Craft beer could be the next unlikely beneficiary of the artificial intelligence revolution. London-based IntelligentX Brewing Company has revealed its new AI Beer range. These are beers that will be improved over time using advanced algorithms. The brewer is employing an online feedback system (via a Facebook Messenger bot) to obtain data on how well-liked its Pale, Amber, Black and Golden beers are by customers. The company will then employ "complex machine learning algorithms," combining reinforcement learning and bayesian optimisation, to search for trends among this feedback and tune the recipes accordingly. "Because our A.I. is constantly reacting to user feedback, we can brew beer that matches what you want, more quickly than anyone else can," says IntelligentX Brewing Company.

The Apache Mahout project, a set of highly scalable machine-learning libraries, recently announced it's first public release. InfoQ spoke with Grant Ingersoll, co-founder of Mahout and a member of the technical staff at Lucid Imagination, to learn more about this project and machine learning in general. In describing what machine learning was, Ingersoll quoted Introduction To Machine Learning by Ethem Alpaydin, "Machine Learning is programming computers to optimize a performance criterion using example data or past experience". When asked to describe sample applications for some of these algorithms, Ingersoll indicated that the Taste filtering provided recommendations of items that a user would like based on their preferences, such as movie recommendations. Clustering is used to group together arbitrary data into categories of similar items, with the grouping of similar news stories being an example of this.

For more information about embedded vision, please visit: http://www.embedded-vision.com Bill Jenkins, Senior Product Specialist for High Level Design Tools at Intel, presents the "Accelerating Deep Learning Using Altera FPGAs" tutorial at the May 2016 Embedded Vision Summit. While large strides have recently been made in the development of high-performance systems for neural networks based on multi-core technology, significant challenges in power, cost and, performance scaling remain. Field-programmable gate arrays (FPGAs) are a natural choice for implementing neural networks because they can combine computing, logic, and memory resources in a single device. Intel's Programmable Solutions Group has developed a scalable convolutional neural network reference design for deep learning systems using the OpenCL programming language built with our SDK for OpenCL.

A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machine with Nvidia Titan X, running Ubuntu 14.04 There are several great guides with a similar goal. Some are limited in scope, while others are not up to date.

The applications for machine learning are fascinating, practical, and seemingly endless. You can even build your own machine learning systems using open source libraries. For one week only, SAVE 50% on select machine learning and Python ebooks and video training and start transforming your data into actionable knowledge. You get free lifetime access, multiple file formats, and free updates. Use discount code WKMLPY - Deal expires July 14, 2016 at 5AM PT, and cannot be combined with other offers.

Among all the AI helpers from major tech companies, Google Assistant is the most mysterious. Google has billed Assistant as one component in "the next evolution of Google." Others, such as Danny Sullivan at Search Engine Land, have gone a step further, imploring us to think of Assistant as "Google 2.0." Backchannel's Steven Levy has documented how Google is rebuilding its entire company around the type of machine learning that Assistant will showcase. Yet despite all of Assistant's potential, we know very little about how it'll operate.