Microsoft's recent purchase of LinkedIn for a reported 26.2 billion may be the biggest acquisition news so far in 2016. But Twitter is betting that its own recent acquisition of Magic Pony Technology – a neural networks/machine learning company – for a mere 150 million will pay big dividends down the stretch. Commenting on the acquisition in a recent Twitter blog, Twitter CEO and co-founder Jack Dorsey said, "Machine learning is increasingly at the core of everything we build at Twitter." Dorsey went on to say that, "Magic Pony's machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what's happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community." Magic Pony Technology is the third machine-learning startup that Twitter has acquired since Madbits in 2014, which begs the question: Why is Twitter so heavily focused on machine learning?

Deep learning has a wide range of applications, from speech recognition, computer vision, to self-driving cars and mastering the game of Go. While the concept is intuitive, the implementation is often heuristic and tedious. We will take a stab at simplifying the process, and make the technology more accessible. We illustrate our approach with the venerable CIFAR-10 dataset. The following code snippet will download the data from its known location to a folder "data/cifar" inside the current workspace.

Researchers at IBM Australia will play a role in building a "cognitive assistant" the IT giant hopes will help ophthalmologists diagnose eye conditions from medical image data. "IBM research is building the next generation cognitive assistant with advanced multi-media capability for early detection and management of diseases that can affect both the eyes and overall health of a person," the firm said in a now closed advertisement. Participating full and part-time interns would apply their clinical knowledge to analyse retinal image data and come up with "novel ideas and insights for cognition on this type of data". Back in June, IBM Australia revealed agreements with organisations including Melanoma Institute Australia to "apply cognitive computing to dermatology images" in the hope of earlier detection and identification of skin cancer.

Researchers at IBM Australia will play a role in building a "cognitive assistant" the IT giant hopes will help ophthalmologists diagnose eye conditions from medical image data. The company recruited a batch of research interns to lend their expertise to the project via the IBM Australia research lab in Melbourne. The interns were slated to begin work last month. "IBM research is building the next generation cognitive assistant with advanced multi-media capability for early detection and management of diseases that can affect both the eyes and overall health of a person," the firm said in a now closed advertisement. "We are building the image-guided informatics system that acts as a filter to extract the essential clinical information ophthalmologists need to know about a patient for diagnosis and treatment planning. "This filtering employs sophisticated medical image processing, pattern recognition and machine learning techniques guided by advanced clinical knowledge.

In the year 1820, a person could expect to live less than 35 years, 94% of the global population lived in extreme poverty, and less that 20% of the population was literate. Today, human life expectancy is over 70 years, less that 10% of the global population lives in extreme poverty, and over 80% of people are literate. These improvements are due mainly to advances in technology, beginning in the industrial age and continuing today in the information age. There are many exciting new technologies that will continue to transform the world and improve human welfare. Here are eleven of them.

Members of Rajat Monga's team at Google will be teaching tutorials on deep learning with TensorFlow at Strata Hadoop World in Beijing (August 4th) and NYC (September 27th). Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies. There's not going to be too many areas left that run without machine learning that you can program.

It seems like machine learning and artificial intelligence are topics at the top of everyone's mind in tech. Be it autonomous cars, robots, or machine intelligence in general, everyone's talking about machines getting smarter and being able to do more. At the same time, for many developers, machine learning and artificial intelligence are nebulous terms representing complex mathematical and data problems they just don't have the time to explore and learn. As I've spoken with lots of developers and CTOs about Fuzzy.io and our mission to make it easy for developers to start bringing intelligent decision-making to their software without needing huge amounts of data or AI expertise, some were curious to learn more about the greater landscape of machine learning. Here are some of the links to articles, podcasts and courses discussing some of the basics of machine learning that I've shared with them.

For those of us working in virtual and augmented reality, our days are spent thinking of better and better ways to create more lifelike virtual worlds. It's easy for us to believe that one day we will be living in a sim indecipherable from "base" reality -- or even more likely, that we're already living in one. This year at Burning Man, the Metaverse Scholars Club, a non-profit committed to building an ethical metaverse, is creating an immersive theater/mixed reality experience reflecting these ideas. Our backgrounds are as augmented and virtual reality creators and community builders and theater. I'm Jodi Schiller, director of the immersive experience -- a professional with more than 20 years experience creating theater as well as a drama therapist.

There's been quite the spate of discussion of late about Artificial Intelligence (AI) and adaptive learning. You've no doubt seen the commercials where Watson conducts conversations with talents from Bob Dylan to teacher Ashley Bryant, the latter in which great learning outcomes are proposed. And I think it's important to know what is real, where we are, and where we are going, if we're to plan accordingly. We've previously touched on AI, but it's worth going deeper. To start, we need to clarify what AI really is.

When you think about any era, its defining creative style is often a combination of human creativity with the limitations of technology. With the rise of machine intelligence (artificial intelligence, and virtual, augmented and mixed reality) how will its limitations - or the struggle against it - define today's style? Tech can be a boon to creativity in any medium. It allows us to stretch boundaries, create new perspectives, and translate imagination. We take a look at the brilliance that human ingenuity in face of technological limitations has yielded in art and design over time, and provide real-time analysis of what we see today that creatives may lean into for breakthrough.