"Now is the time for greater coordination and collaboration on AI," Microsoft CEO Satya Nadella wrote in a blog post for Slate on Tuesday. Like IBM, Google, Facebook, and other tech giants, Microsoft has jumped into AI full-force, releasing Azure Machine Learning, a cloud-based analytics tool, part of its Cortana Intelligence Suite, in 2015. It has also made mistakes, and recently sparked media attention with the release of Tay, a teenage chatbot that began uttering racist and sexist slurs on Twitter. Why Dick's Sporting Goods decided to play its own game in e commerce Dick's Sporting Goods has long partnered with eBay Enterprise on its e -commerce platform. Learn the benefits and risks of this multi -million dollar IT bet.

A Tesla Model S driver using the car's semi-autonomous Autopilot feature died when the car hit an 18-wheeler, the first known fatality involving technology that remains in beta testing. The collision occurred May 7 when the big-rig made a left turn in from of the Model S at an intersection on a divided highway in Williston, Florida. "Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied" and the car drove under the trailer, the automaker said today. The National Highway Traffic Safety Administration sent a Special Crash Investigations Team to examine the vehicle and the crash scene. Experts from the agency's Office of Defects Investigation plan to examine the design and performance of the Autopilot system.

By connecting personal computers around the globe, the World Community Grid hopes to form a network dedicated to number-crunching Zika research. Since the World Health Organization declared the Zika virus a global public health emergency in February, researchers have stepped up their efforts to develop a treatment. OpenZika researchers are working to find the one in millions of chemical compounds that could produce the anti-Zika drug. They're recruiting volunteers from the public to loan computing power and speed up the process. When you sign up, you select the cause(s) you're interested in contributing to -- others include Help Stop TB and Outsmart Ebola Together -- download the manager program, and let it do its thing in the background.

What does Machine Learning allow us to do that we just wouldn't be able to do otherwise?

Computer vision has been having a moment. No more does an image recognition algorithm make dumb mistakes when looking at the world: these days, it can accurately tell you that an image contains a cat. But the way it pulls off the party trick may not be as familiar to humans as we thought. Most computer vision systems identify features in images using neural networks, which are inspired by our own biology and are very similar in their architecture--only here, the biological sensing and neurons are swapped out for mathematical functions. Now a study by researchers at Facebook and Virginia Tech says that despite those similarities, we should be careful in assuming that both work in the same way.

Artificial intelligence that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then source answers from previous staff conversations on a subject or track down experts within the company who are able to help. Its creators refer to it as "brain technology", adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. Starmind co-founder Pascal Kaufmann said of the technology: "Thousands of human brains connected can outsmart any machine today. "But if you can find ways for humans and AI (artificial intelligence) inspired technologies to autonomously collaborate rather than focusing on ways for them to compete, you can bring out the best in both." The algorithm within the system, which was developed in Switzerland, becomes more powerful the more it is used and is able to build a map of the people in a business and the areas in which all of them are experts, or are able to provide relevant information. "Starmind acts like an artificial hyper brain that seamlessly exists at the core of a company," Mr Kaufmann added. "The algorithm is then fuelled by the know-how stored inside the brains of everyone that engages with the system." Several major companies in Europe, including UBS and Bayer are already using the system. A new version of the software - called Starmind NOW - has also been launched which enables the software to be accessed outside of company intranet for the first time. Starmind says this makes the technology more "intuitive and seamless" to use. Former Microsoft executive Peter Waser has also joined the company as CEO. "It's a new technology that has never been available on the market in this form," he said. "Brain technology is the latest technology in the megatrend of machine learning and artificial intelligence.

The allure of using machine learning in data security comes from its ability to generalize attack detection based on historical data and to detect attacks that would not be obvious otherwise. Machine learning in security analytics is gaining widespread adoption, and the security analytics market is projected to hit 7.1 billion by 2020. The biggest challenge in using machine learning for data security has to do with triaging, or prioritizing, alerts effectively. In my last post, I explored how to prevent false alerts in data security. Here, we'll explore how a generalizable algorithm-based system can detect security breaches, using ranking algorithms from the information retrieval domain.

The quantitative explosion in digital data stemming from the surge in Internet communication and the widespread use of sensors is today a major driver of business opportunities for companies. In this new world, a great deal of ink is being spilled on the subject of progress in'machine learning'. This increasingly common expression denotes families of algorithms which enable computer-aided systems to accumulate knowledge and intelligence automatically without being explicitly programmed to do so. Machine learning methods have applications in a wide range of fields including the manufacturing industries (process optimisation), the finance sector (risk management), the luxury goods and wider online markets (strategic marketing), defence (situational analysis) and in the biomedical sector (patient typology). However, 'machine learning' is far from foolproof and if applied to the economic or political field it looks certain to raise some major issues.

What was the evolution of your interest in machine learning, and how did you zero-in on your Ph.D. work? Ilya Sutskever: I started my Ph.D. just before deep learning became a thing. I was working on a number of different projects, mostly centered around neural networks.

The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. Cool stuff, but--especially for someone hoping to explore machine learning for the first time--TensorFlow can be a lot to take in. Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!