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.

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.

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.

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.

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.

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.

Artificial intelligence can find, map poverty, researchers ...

Artificial intelligence (A.I.) dates back to the 1950s, when the term was coined. But the way Intel sees it, the field is not old -- it represents a growth opportunity for the chipmaker. Last week Intel made a bold move and acquired Nervana, one of the preeminent startups in deep learning, a type of A.I. that involves training artificial neural networks on data and then getting them to make inferences on new data. Today, when Intel announced a new generation of Xeon Phi server chips, the emphasis was on their ability to handle A.I. workloads. In previous years, Xeon Phi has been geared toward the high-performance computing market.

IN "Minority Report", a policeman, played by Tom Cruise, gleans tip-offs from three psychics and nabs future criminals before they break the law. In the real world, prediction is more difficult. But it may no longer be science fiction, thanks to the growing prognosticatory power of computers. That prospect scares some, but it could be a force for good--if it is done right. Machine learning, a branch of artificial intelligence, can generate remarkably accurate predictions.

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts. I'll walk you through it.