After reading Pete Warden's excellent TensorFlow for Poets, I was impressed at how easy it seemed to build a working deep learning classifier. It was so simple that I had to try it myself. I have a lot of photos around, mostly of birds and butterflies. So, I decided to build a simple butterfly classifier. I chose butterflies because I didn't have as many photos to work with, and because they were already fairly well sorted.

Azure Machine Learning (Azure ML) is a fully managed cloud service that enables you to easily build, deploy and share predictive analytics solutions. Azure ML allows you to create a predictive analytic experiment and then directly publish that as a web service. The web service API can be used in two modes: "Request Response" and "Batch Execution". A Request-Response Service (RRS) is a low-latency, highly scalable web service used to provide an interface to stateless models that have been created and deployed from an Azure Machine Learning Studio experiment. It enables scenarios where the consuming application expects a response in real-time.

Smart objects: an object that enhances the interaction with not only people but also with other Smart Objects.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (the second of seven) is a look at artificial intelligence. Future posts will look at other tech areas. An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when A.I., robotics, VR, synthetic biology and computation are all doubling, morphing and recombining. You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers." For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). Then, we explored the patterns that emerged. At A360 this year, my expert on AI was Stephen Gold, the CMO and VP of Business Development and Partner Programs at IBM Watson.

The computing industry progresses in two mostly independent cycles: financial and product cycles. There has been a lot of handwringing lately about where we are in the financial cycle. Financial markets get a lot of attention. They tend to fluctuate unpredictably and sometimes wildly. The product cycle by comparison gets relatively little attention, even though it is what actually drives the computing industry forward.

If you close your eyes and imagine a brick wall, you can probably come up with a pretty good mental image. After seeing many such walls, your brain knows what one should look like. A startup in the U.K. is using machine learning to enable computers and smartphones to model visual information in a similar way. A computer could use these visual models for various tasks, from improving video streaming to automatically generating elements of a realistic virtual world. Magic Pony Technology, created by graduates of Imperial College London with expertise in statistics, computer vision, and neuroscience, trains large neural networks to process visual information.

There are companies out there working fast and furious to develop artificial intelligence in computers and what will that mean for our human society? Will developers develop themselves out of a job? Can computer artificial intelligence evoke human emotion and move people to buy products and services? If so, I am out of a job. In all seriousness, it will be interesting to see the applications and if it will be used to assist the human race, or to replace it.

Richard Susskind spoke at a Vanderbilt Law School conference about the impact of technology on the legal profession. You don't have to look far to see how technology has changed the way people live their lives. Many patients check in with WebMD before going to their doctor's office. TurboTax has replaced accountants in some households. A new app even offers repentant churchgoers with a smartphone alternative to the confessional, complete with a drop-down menu of potential sins that need to be forgiven.

Dreyer says training a machine algorithm is similar to teaching a child, in that it requires considerable repetition. For example, by training an artificial neural network with thousands of images of correctly and incorrectly placed central lines, these algorithms can "learn" the difference between the two. While the human brain is optimized for the interpretation of visual data, these cognitive computations can be optimized to quantify nearly any data while eliminating the judgment and fatigue of humans.

The last time Microsoft released a "bot" onto the internet, it quickly became a Donald Trump-supporting, Nazi-sympathising, conspiracy theory-believing racist . It has unveiled a website called CaptionBo t, which uses clever algorithms to recognise images. Users are asked to give a star rating to reflect its accuracy, letting the bot learn from its mistakes and improve its skills. "I can understand the content of any image and I'll try to describe it as well as any human," the bot said. If you look at the tweets above, you'll see what happened to Tay, Microsoft's last creation. It was supposed to be a "chill" machine, but ended up getting tricked into spouting racist rhetoric, praising Hitler and calling for Mexico to build a wall to keep migrants out of the US.