Intel is taking a new direction in chip development as it looks to the future of artificial intelligence, with the company betting the technology will pervade applications and web services. The company on Thursday said it is developing new chips that will handle AI workloads, which will increasingly be a part of its chip future. For now, the AI chips will be released as specialized primary chips or co-processors in computers and separate from the major product lines. But over time, Intel could adapt and integrate the AI features into its mainstream server, IoT, and perhaps even PC chips. The AI features could be useful in servers, drones, robots, and autonomous cars.

An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information. Big Data, Data Mining, and Machine Learning: Value Creation for Bus... On this resource the reality of big data is explored, and its benefits, from the marketing point of view.

The world is evolving at thunder-speed, and an increasing number of machines are being designed to catch up with humans in many activities. Ever since IBM's Artificial Intelligence (AI) Deep Blue defeated the world Chess Champion in 1997 a lot has been going on. Self-driving cars, close-to-perfect face-recognition software, stock investment geniuses, chatbots and even AI doctors are close to be (or already are) part of our daily lives. One has just to see how Watson, also an IBM AI robot "destroyed" the other (human) players in a game of Jeopardy in 2011. A few years ago, AI boundaries were given by the codes written in the robot's programming.

Machine learning is on a steep adoption curve and making its inroads in our daily lives and work. The application of the technology won't be an issue at all. There's an abundance of meaningful value propositions for many functional areas, business processes and roles across multiple industries. Software vendors of enterprise business solutions are focusing their product development on machine learning and other related artificial intelligence technologies. CEO Bill McDermott of SAP said that intelligent applications will fundamentally change the way you do work in the enterprise in the next decade.

The artificial intelligence (AI) non-profit OpenAI has agreed to partner with Microsoft to develop "cloud brains" to test its experiments. The organization, which is backed by Elon Musk, has signed an agreement that will allow it to run large-scale experiments using the company's Azure cloud services. OpenAI aims to discover more about deep learning and AI, while Microsoft will use the partnership to create new tools and technologies that use AI. OpenAI was one of the first adopters of Microsoft's Azure N-Series Virtual Machines service that was designed to handle the intense computing workloads that are needed to run simulations and deep learning projects. The service is powered by Nvidia's graphics chips and will be made generally available starting in December.

Michael Kagan: The ever-growing demand for higher performance drives technology innovations for HPC, which then spreads to other markets. We have witnessed several technology transitions over the years, such as the transition from SMP to clusters, or from single core to multi-core. We are now going through another technology transition, which some call Co-Design. There are many technology efforts to re-architect the data center from a CPU-centric architecture to a data-centric architecture in order to overcome the new performance bottlenecks. The new data centers will need to allow data operations and analysis everywhere in order to get insights in real time.

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This morning, millions of people woke up and impulsively checked Facebook. They were greeted immediately by content curated by Facebook's newsfeed algorithms. To some degree, this news might have influenced their perceptions of the day's news, the economy's outlook, and the state of the election. Every year, millions of people apply for jobs. Increasingly, their success might lie, in part, in the hands of computer programs tasked with matching applications to job openings.

Too often, machine learning requires a massive investment of time and terabytes of data before it can deliver meaningful insights. But that doesn't have to be the case. A well-configured machine learning analytics tool can rapidly provide initial results -- a key advantage for developers who can then start using those results to create value.