Recent advances in deep learning for image recognition have spawned numerous challenge-based learning competitions in which participants can use a low-cost GPU graphics card to accomplish goals that required expensive resources in the recent past. Students are encouraged to explore this exciting new field of research by entering these competitions. Scooter Willis, "Stand on the Shoulders of Giants", Computer, vol.

Yesterday, AMD showed off the first real-time benchmarks of the Radeon Vega graphics card against the NVIDIA Pascal based Tesla P100 in deep learning benchmarks. In its first attempt, the RTG developed GPU was able to give NVIDIA's best compute card from last year a good beating but there's more to the benchmarks. NVIDIA launched the Tesla P100 based on Pascal GP100 back in early 2016. Since then, it has been the fastest compute solution available to date. NVIDIA kicked off 2017 with the announcement of the next chapter in graphics deep learning.

Less than a year afterestablishing an artificial intelligence lab in Canada, Google is once again expanding its presence up north. DeepMind, a subsidiary of the search giant that experiments with novel ways to apply AI, today announced the launch of a new research center in Edmonton. The facility is the unit's first major outpost outside its native UK when not counting the modestcontingent of staffers based in Google's Mountain View headquarters. Research efforts will be led by three professors from the University of Alberta, which has a long history of collaborating with DeepMind.

Ninety percent of all the data available online today has accumulated over the last couple of years. This pool of data is projected to grow exponentially as we all contribute to it each day through 500 million tweets, 4.3 billion Facebook posts, 5.75 billion "likes," and 6 billion daily Google searches. The Ridacati Group reports that 205 billion emails were sent each day in 2015, and it estimates that by 2019 that number will increase 20 percent to reach 246 billion emails. This data growth is inextricably linked to the development of AI. Deep learning models have become routine in almost every area that deals with data.

The ECG data is sampled at a frequency of 200 Hz and is collected from a single-lead, noninvasive and continuous monitoring device called the Zio Patch (iRhythm Technologies) which has a wear period up to 14 days. Each ECG record in the training set is 30 seconds long and can contain more than one rhythm type. Each record is annotated by a clinical ECG expert: the expert highlights segments of the signal and marks it as corresponding to one of the 14 rhythm classes. We collect a test set of 336 records from 328 unique patients. For the test set, ground truth annotations for each record were obtained by a committee of three board-certified cardiologists; there are three committees responsible for different splits of the test set.

If you're worried that one day the robots will revolt and either exterminate or subjugate the entire human race, you're not alone. But instead of sitting back and waiting for the robot rebellion, two leaders in AI are teaming up to tackle the problem of creating smart computer programs that won't eventually try and take over. Google DeepMind and Open AI, a lab partially funded by Elon Musk, released a research article outlining a new method of machine learning. It actually takes its cues from humans when it comes to learning new tasks. This could be safer than allowing an AI to figure out how to solve a problem on its own, which has the potential to introduce unwelcome surprises. The main problem that the research tackled was when an AI discovers the most efficient way to achieve maximum rewards is to cheat -- the equivalent of shoving everything on the floor of your room into a closet and declaring it "clean."

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.

Big data has met its match. In field after field, the ability to collect data has exploded--in biology, with its burgeoning databases of genomes and proteins; in astronomy, with the petabytes flowing from sky surveys; in social science, tapping millions of posts and tweets that ricochet around the internet. The flood of data can overwhelm human insight and analysis, but the computing advances that helped deliver it have also conjured powerful new tools for making sense of it all. In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on the data torrents. Unlike earlier attempts at AI, such "deep learning" systems don't need to be programmed with a human expert's knowledge. Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets that are far larger and messier than human beings can cope with.

DeepMind – the UK-based artificial intelligence (AI) division of Google – has announced it will be joining forces with the University of Alberta to create its first international research lab. The new lab will tap into the talent of leading academics in the field of AI, and will be led jointly by Rich Sutton, Michael Bowling and Patrick Pilarski. DeepMind describes itself as a "hybrid of start-up culture and academia", a model that will be pushed forward by its newly announced partnership. The company will provide funding and support to AI programmes at the University of Alberta, while also benefitting from the expertise of its academics. The company has already flourished in the UK by forging partnerships with top academic institutions, including University College London and Oxford University.

Caffe 2 continues the strong support for vision type problems but adds in recurrent neural networks (RNN) and long short term memory (LSTM) networks for natural language processing, handwriting recognition, and time series forecasting. MXNet supports deep learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) including Long Short-Term Memory (LTSM) networks. This framework provides excellent capabilities for imaging, handwriting and speech recognition, forecasting and natural language processing. DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.