In this special guest feature, Debra Goldfarb from Intel writes that her recent panel discussion at SC16 illustrated just how fast Artificial Intelligence is advancing all around us. Computational science has come a long way with machine learning (ML) and deep learning (DL) in just the last year. Leading centers of high-performance computing are making great strides in developing and running ML/DL workloads on their systems. Users and algorithm scientists are continuing to optimize their codes and techniques that run their algorithms, while system architects work out the challenges they still face on various system architectures. At SC16, I had the honor of hosting three of HPC's thought leaders in a panel to get their ideas about the state of Artificial Intelligence (AI), today's challenges with the technology, and where it's going. My guests were Nick Nystrom from Pittsburgh Supercomputing Center (PSC), Ivan Rodero from Rutgers University, and Prabhat from NERSC at Berkeley National Laboratory. They answered both questions I put to them and from the audience.

AI has earned another victory against humans, this time in Nintendo fighting game Super Smash Bros. Melee. A team lead by Vlad Firoiu at Massachusetts Institute of Technology trained an AI to play the game using deep learning algorithms and then pitched it against ten highly-ranked players. The AI came out on top against every one of them. Super Smash Bros. is a cult Nintendo series where players battle classic video game characters like Super Mario and Zelda. The aim is to knock out (KO) the opponent by sending them out of bounds. The Super Smash Bros. Melee game was originally released in 2001 for the Nintendo Gamecube console.

Summary: Convolutional Neural Nets are getting all the press but it's Recurrent Neural Nets that are the real workhorse of this generation of AI. We've paid a lot of attention lately to Convolutional Neural Nets (CNNs) as the cornerstone of 2nd gen NNs and spent some time on Spiking Neural Nets (SNNs) as the most likely path forward to 3rd gen, but we'd really be remiss if we didn't stop to recognize Recurrent Neural Nets (RNNs). Because RNNs are solid performers in the 2nd gen NN world and perform many tasks much better than CNNs. These include speech-to-text, language translation, and even automated captioning for images. By count, there are probably more applications for RNNs than for CNNs. On one scale RNNs have much more in common with the larger family of NNs than do CNNs which have very unique architecture.

BRIGHTON – Artificial intelligence already plays a major role in human economies and societies, and it will play an even bigger role in the coming years. To ponder the future of AI is thus to acknowledge that the future is AI. This will be partly owing to advances in "deep learning," which uses multilayer neural networks that were first theorized in the 1980s. With today's greater computing power and storage, deep learning is now a practical possibility, and a deep-learning application gained worldwide attention in 2016 by beating the world champion in Go. Commercial enterprises and governments alike hope to adapt the technology to find useful patterns in "Big Data" of all kinds.

How they're different and why are they all essential to the Internet of Things. After all, it's been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like "Machine Learning" and "Deep Learning," sometimes used interchangeably with artificial intelligence. I'll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they're different. Then, I'll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

With huge strides in AI--from advances in the driverless vehicle realm, to mastering games such as poker and Go, to automating customer service interactions--this advanced technology is poised to revolutionize businesses. But the terms AI, machine learning, and deep learning are often used haphazardly and interchangeably, when there are key differences between each type of technology. Here's a guide to the differences between these three tools to help you master machine intelligence. SEE: Inside Amazon's clickworker platform: How half a million people are being paid pennies to train AI (PDF download) (TechRepublic) AI is the broadest way to think about advanced, computer intelligence. In 1956 at the Dartmouth Artificial Intelligence Conference, the technology was described as such: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

Released in 1998, the Game Boy camera was perhaps the first digital camera many young hackers got their hands on. Around the time Sony Mavica cameras were shoving VGA resolution pictures onto floppy drives, the Game Boy camera was snapping 256 224 resolution pictures and displaying them on a 190 144 resolution display. The picture quality was terrible, but [Roland Meertens] recently had an idea. Why not use neural networks to turn these Game Boy Camera pictures into photorealistic images? Neural networks, deep learning, machine learning, or whatever other buzzwords we're using require training data.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.

Some enterprises use Clarabridge to mine customer data, manage customer experience, and see sentiment analysis. While Clarabridge provides an intelligence platform, Signals is a more powerful solution platform in unifying customer voice. While sentiment analysis is a key function of Signals, its deep machine learning capability allows you to do something more organic. It enables you to listen to your customer data from the ground up and identify trends and patterns as they emerge. Signals results are displayed directly in front of the user.

After completing Part 1 of Jeremy Howard's awesome deep learning course, I took a look at my AWS bill and found I was spending nearly $200/month running GPUs. It's not necessary to spend that much to complete his course, but I started working on a few extracurricular datasets in parallel and I was eager to get results. After talking with fellow students and reading a number of blog posts, I decided to try building my own box. Technology and hardware change so rapidly that I'm afraid much of post will become outdated soon, but I hope my general approach will still be useful for at least a little while. I started by reading a bunch of blogs to get the current consensus on which parts to buy.