The Turing Award has recognized some of the biggest names in AI and computing over the years, and the latest winners are particularly heavy hitters. The three prize recipients for 2018 are Google VP Geoffrey Hinton, Facebook's Yann LeCun (above) and Yoshua Bengio, the Scientific Director of the giant AI research center Mila. All three helped "develop conceptual foundations" for deep neural networks, according to the Association for Computing Machinery, and created breakthroughs that showed he "practical advantages" of the technology. Hinton, for instance, proved that a then-rare backpropagation algorithm could help neural networks solve problems that were previously unfeasible. LeCun was instrumental to developing technologies behind modern computer vision, while Bengio helped foster generative adversarial networks (that is, pitting a creative network against another that serves as a kind of quality control) that can create original images.

British-born artificial intelligence (AI) expert Geoffrey Hinton has won the Turing Award, sometimes referred to as "the Nobel Prize of computing". Mr Hinton, who now lives in Canada, shares the award with Yoshua Bengio and Yann LeCun - two other proponents of deep learning, a popular form of AI. "The three of us have been the people who most believed in this approach," he told BBC News. "It's very nice to be recognised now that it is fashionable." A deep neural network uses many layers of artificial neurons, loosely mimicking the structure of animal brains. Such AI is increasingly used in products that people use every day - from smart speakers to Facebook.

To learn who's taking home the Turing Award, people might turn to their trusted talking bots, like Siri or Alexa. Or, in fact, some of the very technology the three winners helped bring to life. Yoshua Bengio, Geoffrey Hinton and Yann LeCun have earned what's often referred to as the Nobel Prize of the tech world for their pioneering work in artificial intelligence, the Association for Computing Machinery announced Wednesday. The researchers, working both independently and together, helped advance the thinking and application of neural networks, the technology that gives computers the ability to recognize patterns, interpret language and glean insights from complex data. "Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society," Cherri Pancake, president of the computing society, said in a statement.

In this article, I'm interviewing a veteran data scientist, Dr Stylianos (Stelios) Kampakis, about his career to date and how he helps decision makers across a range of businesses understand how data science can benefit them. While data science is a field showing immense growth at present, it's somewhat nebulous in its description. I think there's a lot of uncertainty as to exactly what it is and how to apply it. Fortunately, Stelios is an expert data scientist with a mission to educate the public about the power of data science and AI. He is a member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies and CEO of The Tesseract Academy.

In the late 1980s, Canadian master's student Yoshua Bengio became captivated by an unfashionable idea. A handful of artificial intelligence researchers was trying to craft software that loosely mimicked how networks of neurons process data in the brain, despite scant evidence it would work. "I fell in love with the idea that we could both understand the principles of how the brain works and also construct AI," says Bengio, now a professor at the University of Montreal. More than 20 years later, the tech industry fell in love with that idea, too. Neural networks are behind the recent bloom of progress in AI that has enabled projects such as self-driving cars and phone bots practically indistinguishable from people.

Although they have known each other for than 30 years, Bengio, Hinton and LeCun have mostly worked separately on technology known as neural networks. These are the electronic engines that power tasks such as facial and speech recognition, areas where computers have made enormous strides over the past decade. Such neural networks also are a critical component of robotic systems that are automating a wide range of other human activity, including driving.

It's Friday, which means it's once again time for Replay, WIRED's look at the world of gaming. Since nearly everyone involved in the videogame industry spent the week at the Game Developers Conference discussing the future and reflecting on the past, a lot of news came out of that annual gathering. But a lot of news came from elsewhere too. Well, there are awards, a new title to look forward to, and an old conflict finally (almost) coming to an end. Earlier this week at the Game Developers Conference, the Independent Gaming Festival held its yearly awards ceremony, honoring the best and most exciting works in the indie gaming sphere.

Enterprise security has always been a cat and mouse game, with cyber adversaries constantly evolving their attack systems to get past defenses. Can AI based systems help in warding off new age threats and zero day attacks. To get a perspective, we spoke with Vikas Arora, IBM Cloud and Cognitive Software Leader, IBM India/South Asia, who shares his view on how AI can impact enterprise security. What are your views on the cyber security landscape in India? Which sectors do you think are the most vulnerable today?

In summer 2013, I interviewed for a lead role in the data science and analytics team at tech-for-good company JustGiving. During the interview, I said I planned to deliver batch machine learning, graph analytics and streaming analytics systems, both in-house and in the cloud. A few years later, my former boss Mike Bugembe and I were both presenting at international conferences, winning awards and becoming authors! Here is my story, and what I learnt on the journey -- plus my recommendations for you. I've always been interested in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP).

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources.