On Monday, Microsoft made headlines by plunking down 26 billion for LinkedIn. Now it's announcing its second acquisition of the week: For an undisclosed sum, the company has bought Wand Labs, a Silicon Valley-based startup that declares its mission is "to tear down app walls, integrate your services in chat, and make them work together so you can do more with less taps." Founded in 2013, Wand is tiny--it has just seven employees--and, though no longer in stealth mode, is hardly a household name. The iOS and Android apps it built haven't yet reached general availability, and now they never will, as their creators put them aside and contribute to the greater Microsoftian effort. But for all the ways that this acquisition is different than the mammoth LinkedIn deal, both transactions are pieces of the same puzzle.

Data volume is not only increasing, but data sources are also changing. More people do business online or via their mobile devices. Chao explained that as part of American Express's ongoing journey, they must keep up with these changes in style of interactions as well as with the increasing volume. Part of that involves making a huge number of decisions, millions every day. If American Express can become just a little bit smarter in these decisions, it can have a huge advantage to customers and to the company.

Current deep learning technology is not enough for computers to understand language, a major figure in the field said today. The ability to learn the way people learn through observation and experience is what Facebook will use to teach chatbots and computers to carry on a conversation like a human, said Yann LeCun, the head of Facebook's artificial intelligence (AI) research lab. LeCun spoke about AI and steps being taken to make virtual assistant M stop relying on human training at the 2016 Wired Business Conference, as Wired reported. People have been a part of decisions made by Facebook's M since the bot debuted last year, before the launch of the company's bot platform. Facebook has begun research on ways to make machines understand language more independently.

In an earlier post I explored the value of using scalable machine learning to extract value from huge amounts of data. In this post, I will dive down into the technical side of things, particularly the challenges and benefits that come with making algorithms scalable on large clusters of computers. Machine learning algorithms are written to run on single-node systems, or on specialized supercomputer hardware, which I'll refer to as HPC boxes. They grew up in a world where they didn't have to scale across multiple nodes. It's relatively easy to get high performance when running algorithms on a single computer.

Probabilistic programming languages are in the spotlight. This is due to the announcement of a new DARPA program to support their fundamental research. But what is probabilistic programming? What can we expect from this research? Will this effort pay off?

DeepMind, Google's artificial intelligence (AI) division, certainly thinks there's a risk. They've teamed up with Oxford University to develop a "red button" that would interrupt an AI machine's actions. Their paper "explores a way to make sure a learning agent will not learn to prevent (or seek!) being interrupted by the environment or a human operator."

NATIONAL HARBOR, MARYLAND – A new maker of self-driving vehicles burst onto the scene Thursday in partnership with IBM's supercomputer platform Watson, and it is ready to roll right now. Arizona-based startup Local Motors is offering Olli, an electric minibus capable of carrying 12 people. It says the vehicle can be produced to order by 3-D printing. Olli was designed as an on-demand transportation solution that passengers can summon with a mobile app, like Uber rides. And it can be "printed" to specification in "micro factories" in a matter of hours. Olli will be demonstrated in National Harbor, Maryland, over the next few months with additional trials expected in Las Vegas and Miami.

Machine Learning has definitely gone mainstream with almost all the major vendors announcing support for Machine Learning platforms, frameworks, libraries and how they have started to use it in their applications. This blog post highlights the key Machine Learning process and the spectrum of Machine Learning platforms that are available today for developers to get started. The first thing that developers need to understand that Machine Learning techniques are very different from the traditional programming constructs that they use, while developing their applications. It is often remarked in a lighter vein that some vendors pass of multiple if, then, else statements are machine learning in their applications. The diagram shown below depicts a typical Machine Learning process. The key point to take away from the above process is that the individual iterations around Data Preparation, Model building will keep on happening and we are never finished.

GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets. GraphX gives you unprecedented speed and capacity for running massively parallel and machine learning algorithms.

The defining advance of the next decade, if you listen to the prophets of Silicon Valley, will be the seismic and unavoidable ascent of artificial intelligence. It might be hard to take the thought seriously when a satnav sends you down a dead-end country road, or your phone's autocorrect feature turns a carefully-constructed text message to gibberish, but the milestones reached in the past year alone have been exceptional. DeepMind, the British AI company owned by Google, has defeated the world champion at Go, the ancient game that requires a finely tuned sense of intuition to master. Driverless cars now seem like an inevitability rather than a curiosity. Error rates on image recognition technology have dropped from 25pc in 2011 to less than 4pc.