Scientists have developed a new artificial intelligence software that can turn any smartphone into 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.

As in other industries, business leaders in the automotive and financial-services industries have an urgent need for trusted and actionable real-world insights that can help them know and serve their customers better while enabling rapid innovation. Too often, however, executives have had to operate with uncertain, incomplete, and inconsistent information. Now advances in artificial intelligence (AI) have made the construction of data-based real-world models and simulations a reality. A 2015 Tech Pro Research survey indicated that 24 percent of businesses across industries are currently using AI or had plans to do so within the year. While the health-care sector has been among the leading adopters of AI, financial-services and automotive companies are also increasingly turning to assisted, augmented, and autonomous intelligence.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

We've long known that there are many tasks that computers can perform faster – and better – than humans. Of course, we still have to teach computers HOW to do these tasks, and when using conventional programming techniques we have to be very specific about what computers should do and when. With machine learning, we're essentially teaching computers how to learn what to do, and some of them are becoming better than we are at complex tasks. For example, machine learning is a key enabler of self-driving cars and experts predict that they will eventually be safer than human-driven vehicles. That's just one example of how machine learning is letting us use computers in new ways to do new things.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. Another thing she loves is data science. The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time. So, she decided to marry these two loves in as productive a fashion as possible.

Microsoft is pushing its analytics credentials by detailing how its platforms can be used together to produce output in a way that chief information officers and others can act on, specifically using Power BI to visualise results from the Azure Machine Learning service, now part of the broader Cortana Intelligence Suite. The company said that there has been a lot of interest among customers in using Power BI to visualise the output of an Azure Machine Learning model, and the firm recently published a tutorial showing exactly how this can be accomplished, providing useful pointers for Microsoft customers looking to make the best use of such tools to analyse data. "Imagine if you could have Power BI regularly bring in the latest output of your fraud model or the sentiment for recent tweets about your products," said Justyna Lucznik, programme manager for Microsoft's Power BI team. Microsoft's tutorial naturally assumes that customers are already using multiple Microsoft platforms, such as a cloud-hosted Azure SQL database instance as the source of their data and subscriptions to the PowerBi and Azure ML services, and also use the R language, one of Microsoft's favoured tools for statistical computing, to script all the actions together. The tutorial walks customers through the process of using an R script to extract data from Azure SQL, then calling the Azure ML web service to score the data and write the output back to the SQL database.

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!

Twitter has bought a machine learning startup that can automatically sharpen low-resolution and blurred video in real time. The social network announced this morning that it had acquired London-based Magic Pony Technology for an undisclosed sum, with Twitter CEO Jack Dorsey tweeting that the move will help the the company reach its goal of "making Twitter the first and best place to see what's happening in the world." The benefits of Magic Pony's tech are clear for Twitter. Earlier this year, the company unveiled some of its machine learning research, showing how its algorithms can essentially upgrade the resolution of low-res videos using ordinary graphics cards. The Magic Pony team includes 11 PhDs, with their expertise ranging across computer vision, computational neuroscience, and deep learning.

National Harbor, MD - 16 Jun 2016: Local Motors, the leading vehicle technology integrator and creator of the world's first 3D-printed cars, today introduced the first self-driving vehicle to integrate the advanced cognitive computing capabilities of IBM (NYSE: IBM) Watson. Starting today, Olli will be used on public roads locally in DC, and late in 2016 in Miami-Dade County and Las Vegas. "Olli offers a smart, safe and sustainable transportation solution that is long overdue," Rogers said. "Olli with Watson acts as our entry into the world of self-driving vehicles, something we've been quietly working on with our co-creative community for the past year. We are now ready to accelerate the adoption of this technology and apply it to nearly every vehicle in our current portfolio and those in the very near future. I'm thrilled to see what our open community will do with the latest in advanced vehicle technology."

No longer confined to science fiction, artificial intelligence is here and it's set to transform the way we live and work. At the forefront of this computing revolution is IBM, which has its sights set on changing the world with its cognitive computing engine Watson. As many will know, Watson shot to fame in 2011 when it appeared on US game show Jeopardy, beating two previous winners to secure the 1m prize. Since then IBM have put the technology to use in various industries including retail, healthcare and financial services. A survey carried out by IBM earlier this year showed that 50% of top CEOs predict AI and Cognitive Computing will disrupt their industry.