Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study?

Andy Rubin, the Google veteran who built Android into the world's largest mobile operating system, is convinced that artificial intelligence is the next big thing. Rubin is the founder of Playground Global, a hardware and software incubator and venture capital firm overseeing at least 300 million. A veteran of past technology transitions, Rubin is now thinking about what could be the next big change to ripple through the industry. "New computing platforms happen every 10 to 12 years," he said at the Bloomberg Technology Conference, citing MS-DOS, Windows PCs and mobile as examples. "What's the next platform?... It's about data and people training AI systems to learn."

Boston: In a latest discovery by the scientists, including one of Indian-origin, have developed artificial software that can turn smartphone in to an eye tracking device. Eye-tracking technology - which can determine where in a visual scene people are directing their gaze - has been widely used in psychological experiments and marketing research, but the required pricey hardware has kept it from finding consumer applications. In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. "Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student. "Since there are no applications, there's no incentive for people to buy the devices. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.

In 2011, Artificial Intelligence (AI) came of age when IBM's Watson computer beat two human contestants to win Jeopardy. These were not any two contestants. Ken Jennings had won 74 times consecutively and Brad Rutter had pocketed the biggest pot in history -- 3.25 million. In the battle between men and machine, Watson's win was historic. Jennings was sanguine about losing: "I, for one, welcome our new computer overlords."

Facebook launched chatbots to much fanfare at its Developer Conference in April. Since then, the hype has been replaced by a deathly silence, but the social network's Artificial Intelligence (AI) chief, Yann LeCun, is working on changing that. LeCun, known for his innovative work with AI in the field of deep learning, plans to teach Facebook chatbots common sense. Speaking at the Wired Business Conference, LeCun claimed that his strategy is essential to his company's goal of creating a comprehensive digital assistant in the form of Facebook M. Recently Facebook decided to take a new approach and open-source its AI hardware, a transparency effort that is meant to accelerate growth, said LeCun.

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started.

The 2016 Embedded Vision Summit recently took place in the heart of Silicon Valley. The summit started with a bang when Jeff Dean announced some impressive results using reduced precision deep learning models for inference. For embedded and edge applications of deep learning models, reduced precision inference is a big deal. A brief primer is that model size is reduced by four times since normally single precision uses 32 bits per value. The power draw is significantly reduced as 16 bit arithmetic is nearly two times as fast and memory transfers can account for the majority of the power budget.

Google announced Thursday that it is opening a dedicated machine learning (ML) center in Europe, which will be based in Zurich, Switzerland. It is extending its biggest non-U.S. The search giant revealed the new artificial intelligence research push on Thursday in a blog post. Through its latest effort, Google aims to build a centralized venue for machine language researchers and software engineers to join forces and develop ideas that further enhance the available technology today. The Zurich lab will focus on the development of research and products in the areas of Machine Intelligence.

Google also informed that why it took initiative to have a research center in Europe because world's top technical universities reside in Europe. This isn't Google first research center in Europe either. Google Germany, the research center for artificial intelligence in a nonprofit and base home to 450 scientists, academics and other researchers who work mostly on language technology, embedded intelligence, augmented reality, knowledge management and multimedia analysis, and data mining. Google later invested Oxford Universities AI research team in the startup project.

Google also informed that why it took initiative to have a research center in Europe because world's top technical universities reside in Europe. Google is taking this opportunity to build up their own team for the betterment of future AI creations. Google Germany, the research center for artificial intelligence in a nonprofit and base home to 450 scientists, academics and other researchers who work mostly on language technology, embedded intelligence, augmented reality, knowledge management and multimedia analysis, and data mining. Google recently bought a UK based startup named DeepMind with 500 million USD. Google later invested Oxford Universities AI research team in the startup project.