The first thing we asked when we were turned on to this niche was, what's an artificial intelligence chip? It's best to first think about what artificial intelligence software requires which is a great deal of processing speed, then a great deal of power in order to feed that processing speed. However, it's not just speed and low power that matter, it's also the way the processor functions. This excerpt from MIT Technology review explains why we can't just use a high-end Intel processor chip for artificial intelligence: While a top-of-the-line Intel processor packs more than enough punch to run sprawling financial spreadsheets or corporate operations software, chips optimized for deep learning break particular types of problems--such as understanding voice commands or recognizing images--into millions of bite-size chunks. Because GPUs like Nvidia's consist of thousands of tiny processor cores crammed together on one slice of silicon, they can handle thousands of these chunks simultaneously.

Deep Learning gets state-of-the-art results and Python hosts the most powerful tools.

Artificial intelligence (AI) is not a new concept, but thanks to breakthroughs in deep learning, recent years have seen a rapid resurgence leading to an increasing impact across many industries. Finance in particular is seeing significant disruption by AI, as deep learning tools and techniques become more widespread and accessible, companies are using algorithms or'neural networks' that learn from data, allowing computers to make better predictions and take smart actions in real time. At the RE•WORK Deep Learning in Finance Summit, in London on 23 September, we'll explore how AI is revolutionising the financial sector, through stock market prediction and forecasting, robo-advisors, mobile banking, blockchain technology and more. Speakers in both industry and academia will share insights into recent breakthroughs in technical advancements and fintech applications alongside academics and startups sharing their work from the financial industry. By bringing together key influencers to share cutting-edge research and developments, we can explore how to successfully apply artificially intelligent software to enhance and grow the finance, banking and trading industry.

You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends. In this post you will discover how you can review and visualize the performance of deep learning models over time during training in Python with Keras. Display Deep Learning Model Training History in Keras Photo by Gordon Robertson, some rights reserved. Keras provides the capability to register callbacks when training a deep learning model.

Researchers have developed a deep learning algorithm capable of successfully predicting what will happen in a video clip based on one still clip from the footage. The Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology (MIT) made the breakthrough in predictive vision by training an algorithm using 600 hundred hours of YouTube videos. By searching for patterns and recognizable objects like hands and faces, the algorithm was able to predict human interactions such as hugging, kissing, shaking hands or high fiving. The research is set to be presented this week at the International Conference on Computer Vision and Pattern Recognition (CVPR). "Humans automatically learn to anticipate actions through experience, which is what made us interested in trying to imbue computers with the same sort of common sense," said MIT PhD student and the paper's first author Carl Vondrick.

AS DOOMSDAY SCENARIOS go, it does not sound terribly frightening. The "paperclip maximiser" is a thought experiment proposed by Nick Bostrom, a philosopher at Oxford University. Imagine an artificial intelligence, he says, which decides to amass as many paperclips as possible. It devotes all its energy to acquiring paperclips, and to improving itself so that it can get paperclips in new ways, while resisting any attempt to divert it from this goal. Eventually it "starts transforming first all of Earth and then increasing portions of space into paperclip manufacturing facilities". This apparently silly scenario is intended to make the serious point that AIs need not have human-like motives or psyches.

The benefits of machine learning continue to trickle down to smartphones and gadgets, and chipmaker Qualcomm wants to help speed up the process. The company is launching a new software development kit for its "machine intelligence platform" Zeroth. This SDK will make it easier for companies to run deep learning programs directly on devices like smartphones and drones -- if they're powered by one of Qualcomm's chips, of course. Right now, you're probably using all sorts of deep learning programs you don't know about. Companies like Google and Facebook use this sort of software for things like image and voice recognition, but usually, this process happens in the cloud, with the results beamed to your phone.

Gensim is deep learning toolkit implemented in python programming language intended for handling large text collections, using efficient algorithms.

Today at ICML 2016, NVIDIA announced its latest Deep Learning SDK updates, including DIGITS 4, cuDNN 5.1 (CUDA Deep Neural Network Library) and the new GPU Inference Engine. NVIDIA GPU Inference Engine (GIE) is a high-performance deep learning inference solution for production environments. Power efficiency and speed of response are two key metrics for deployed deep learning applications, because they directly affect the user experience and the cost of the service provided. GIE automatically optimizes trained neural networks for run-time performance, delivering up to 16x higher performance per watt on a Tesla M4 GPU compared to the CPU-only systems commonly used for inference today. Figure 1 shows GIE inference performance per watt of the relatively complex GoogLeNet running on a Tesla M4. GIE can deliver 20 Images/s/Watt on the simpler AlexNet benchmark.

Predicting what will happen in the future is challenging. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory developed an algorithm that can predict whether two individuals will hug, kiss, shake hands or slap five in the next scene. Using a Tesla K40 GPU with the cuDNN-accelerated Caffe deep learning framework, the researchers trained their network on 600 hours of prime-time television shows including The Office and Desperate Housewives. When predicting which of the four actions the person would perform one second later, the algorithm correctly predicted the action more than 43 percent of the time – and humans who have been watching TV for years were only able to predict the next action with 71 percent accuracy. In their second study, the algorithm was shown frames from a video and asked it to predict what object will appear five seconds later.