This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn't require a PhD, and you don't need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic. In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use: To do that we're going to train and use a Convolutional Neural Network (CNN). We're going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what's being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park.

This is the second installment in a three-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Part Three will cover commercial machine learning and deep learning software and services. There are thousands of open source projects on the market today, and we cannot cover them all. We've selected the most relevant projects based on usage reported in surveys of data scientists, as well as development activity recorded in OpenHub. In this post, we limit the scope to projects with a non-profit governance structure, and those offered by commercial ventures that do not also provide licensed software. Part Three will include software vendors who offer open source "community" editions together with commercially licensed software.

Cray Inc. (Nasdaq: CRAY) announced the results of a deep learning collaboration between Cray, Microsoft, and the Swiss National Supercomputing Centre (CSCS) that expands the horizons of running deep learning algorithms at scale using the power of Cray supercomputers. Running larger deep learning models is a path to new scientific possibilities, but conventional systems and architectures limit the problems that can be addressed, as models take too long to train. Cray worked with Microsoft and CSCS, a world-class scientific computing center, to leverage their decades of high performance computing expertise to profoundly scale the Microsoft Cognitive Toolkit (formerly CNTK) on a Cray XC50 supercomputer at CSCS nicknamed "Piz Daint". By accelerating the training process, instead of waiting weeks or months for results, data scientists can obtain results within hours or even minutes. With the introduction of supercomputing architectures and technologies to deep learning frameworks, customers now have the ability to solve a whole new class of problems, such as moving from image recognition to video recognition, and from simple speech recognition to natural language processing with context.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

Glancing around school classrooms in 2016, it's easy to miss just how far technology has transformed learning over the last decade. The desks, whiteboards and rows of chairs are the same, but so much else has changed that can't be seen. A third of Britain's schools are asking students to bring their own tablets and laptops into the classroom now, coding has been on the national curriculum for three years, and more and more education is happening outside school through apps and digital services. But these changes are just the start. Artificial intelligence (AI) is the next giant leap in learning and, according to those working in the field of education and technology, we haven't seen anything yet.

We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee -- in stark contrast to existing online learning approaches that are generally fragile to outliers. We specialize the ORL approach for two concrete cases: online robust principal component analysis and online linear regression. We demonstrate the efficiency and robustness advantages of ORL through comprehensive simulations and predicting image tags on a large-scale data set. We also discuss extension of the ORL to distributed learning and provide experimental evaluations.

Next time you stop for gas at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated gas pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated gas-delivering robot appeared in 1964 at a station in Westminster, Colorado. Short Stop convenience store owner John Roscoe bought an electric box that let a clerk inside activate any of the pumps outside. Self-serve pumps didn't catch on until the 1970s, when pump-makers added automation that let customers pay at the pump, and over the next 30 years, stations across the nation installed these task-specific robots and fired attendants. By the 2000s, the gas attendant job had all but disappeared.

When it comes to Artificial Intelligence (AI) and Automation, there is no debate that advances in these areas will engender profound changes in our world. Rather, the debate centers on what these changes might look like. There are many who express concern or even outright fear about the impact of AI on our future, and with good reason. A recent report from Forrester predicts that by 2021, intelligent agents and related robots will have eliminated 6% of a net jobs. A widely noted study, "The Future of Employment: How susceptible are jobs to computerisation?", estimates that 47% of all US jobs are at risk. The Luddites were textiles workers who protested automation, eventually attacking and burning factories because, "they feared that unskilled machine operators were robbing them of their livelihood".

Then consider this month's winners of the 2016 Siemens Competition, which honors math, science and technology projects from high school students around the country. These are some smart kids with plans to revolutionize fields such as medicine. Identical twin sisters from Texas won the $100,000 prize in the team event with their project that delivers an earlier diagnosis for schizophrenia. The $100,000 scholarship winner of the individual competition is from Oregon and he developed a biodegradable battery to power medical devices that you swallow. The sisters, Adhya and Shriya Beesam, are juniors (yes, juniors) at Plano East Senior High School in Plano, Texas, north of Dallas.

This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive. My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects. So I began watching the first few chapters of Udacity's Supervised Learning course, while also reading all articles I came across on the subject.