In March 2016, DeepMind's AlphaGo beat Lee Sedol, who at the time was the best human Go player in the world. It represented one of those defining technological moments not unlike IBM's Deep Blue beating chess champion Garry Kasparov, or even IBM Watson beating the world's greatest Jeopardy champions in 2011. Yet these victories, as mind-blowing as they seemed to be, were more about training algorithms and using brute-force computational strength than any real intelligence. Former MIT robotics professor Rodney Brooks, who was one of the founders of iRobot and later Rethink Robotics, reminded us at the TechCrunch Robotics Session at MIT last week that training an algorithm to play a difficult strategy game isn't intelligence, at least as we think about it with humans. He explained that as strong as AlphaGo was at its given task, it actually couldn't do anything else but play Go on a standard 19 x 19 board.

Mobile chip maker Qualcomm wants to enable deep learning-based software development on all kinds of devices, which is why it created the Neural Processing Engine (NPE) for its Snapdragon-series mobile processors. The NPE software development kit is now available to all via the Qualcomm Developer Network, which marks the first public release of the SDK, and opens up a lot of potential for AI computing on a range of devices, including mobile phones, in-car platforms and more. The purpose of the framework is to make possible UX implementations like style transfers and filters (basically what Snapchat and Facebook do with their mobile app cameras) with more accurate applications on user photos, as well as other functions better handled by deep learning algorithms, like scene detection, facial recognition, object tracking and avoidance, as well as natural language processing. Basically anything you'd normally route to powerful cloud servers for advanced process, but done locally on device instead. Facebook is actually one of the developers that gained early access to the NPE and the social giant is already seeing five-fold improvements in performance for its AR features on images and live video, when using Qualcomm's Adreno GPUs on Snapdragon SoCs.

I know it's a weird way to start a blog with a negative, but there was a wave of discussion in the last few days that I think serves as a good hook for some topics on which I've been thinking recently. It all started with a post in the Simply Stats blog by Jeff Leek on the caveats of using deep learning in the small sample size regime. In sum, he argues that when the sample size is small (which happens a lot in the bio domain), linear models with few parameters perform better than deep nets even with a modicum of layers and hidden units. He goes on to show that a very simple linear predictor, with top ten most informative features, performs better than a simple deep net when trying to classify zeros and ones in the MNIST dataset using only 80 or so samples. This prompted Andrew Beam to write a rebuttal in which a properly trained deep net was able to beat the simple linear model, even with very few training samples.

If you're confused about machine learning, or can't tell the difference between a'principal component' and a'cluster': This talk is for you! Modern software is often based around complex computational decision making. But how do programs make decisions? Is artificial intelligence just a complicated series of if-statements? And how is regression different from classification?

While most attention to the AI boom is understandably focused on the latest exploits of algorithms beating humans at poker or piloting juggernauts, there's a less obvious scramble going on to build a new breed of computer chip needed to power our AI future. At a computer vision conference in Hawaii, Harry Shum, who leads Microsoft's research efforts, showed off a new chip created for the HoloLens augmented reality googles. The chip, which Shum demonstrated tracking hand movements, includes a module custom-designed to efficiently run the deep learning software behind recent strides in speech and image recognition. The TPU, for tensor processing unit, was created to make deep learning more efficient inside the company's cloud.

There are countless ways to perform audio processing. The usual flow for running experiments with Artificial Neural Networks in TensorFlow with audio inputs is to first preprocess the audio, then feed it to the Neural Net. What happens though when one wants to perform audio processing somewhere in the middle of the computation graph? TensorFlow comes with an implementation of the Fast Fourier Transform, but it is not enough. In this post we will explain how we implemented it and provide the code so that the Short Time Fourier Transform can be used anywhere in the computation graph.

Using algorithms that learn iteratively, machine learning lets you discover hidden insight from your data. Strata is a unique opportunity to get up to speed quickly on the latest in machine and deep learning. Take a look at the machine learning sessions available to you at Strata.

In this episode of the O'Reilly Data Show, I spoke with Christopher Nguyen, CEO and co-founder of Arimo. Nguyen and Arimo were among the first adopters and proponents of Apache Spark, Alluxio, and other open source technologies. Most recently, Arimo's suite of analytic products has relied on deep learning to address a range of business problems. When we started Arimo (our company name then was Adatao), the vision was about big data and machine learning. At the time, the industry had just refactored itself into what I call the'big data layer'--big data in the sense of the layer at the bottom, the storage layer.

A quarter of Australians fear redundancy due to increased use of artificial intelligence and automation as businesses increasingly investigate options, according to a new report into business use of emerging technologies. The study from research firm Telsyte looks broadly across Australian businesses and the rapid adoption of new technologies under way, including artificial intelligence and automation, wearable technology, augmented and virtual reality and drones. It finds that nearly two-thirds of businesses are already dabbling with machine learning or deep learning to improve operations or influence business decision making, with so-called artificial intelligence and automation technology use growing for things ranging from physical robots to digital assistants and chatbots. Telsyte managing director Foad Fadaghi said there was a distinct difference in the enthusiasm for intelligent automation among company executives from the general population. Despite regular statements that automation will augment rather than replace jobs, workers are not buying it.

After decades of experiencing a slow burn, artificial intelligence innovation has caught fire to become the hottest item on the agendas of the world's top technology firms. "Faced with a constant onslaught of data, we needed a new type of system that learns and adapts, and we now have that with AI," says Arvind Krishna, Senior Vice President of Hybrid Cloud and Director of IBM Research. "What was deemed impossible a few years ago is not only becoming possible, it's very quickly becoming necessary and expected." As a result, leading tech companies, as well as scores of startups and researchers, have been racing to develop AI solutions that can provide competitive advantage by augmenting human intelligence. Today's flurry of AI advances wouldn't have been possible without the confluence of three factors that combined to create the right equation for AI growth: the rise of big data combined with the emergence of powerful graphics processing units (GPUs) for complex computations and the re-emergence of a decades-old AI computation model--deep learning.