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.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

Netflix does not usually jump to the top of the list when one thinks of leaders in artificial intelligence, but Netflix's VP of Product Innovation, Carlos Uribe-Gomez, and Chief Product Officer Neil Hunt published a paper informing investors that some of its algorithms help them save 1 billion each year. In the paper, the two executives detailed how the company's recommendation engine impacts its churn rate. The video streaming giant does not report its churn rate, but the paper mentions that the Netflix's retention rates "are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." This year, the streaming giant plans to spend 6 billion on content. With such a big investment, it could get all sorts of TV series and movies, but if it just presents the most popular selections to everyone, many titles would remain unseen.