Wave Computing was founded with the vision of delivering deep learning computers with game-changing computational performance and energy efficiency. Their objective is to enable businesses to analyze complex data in real-time with more accurate results through a fluid discovery and improvement in Deep Neural Network (DNN) development and training with our family of computers. Wave developed a novel Dataflow Processing Unit (DPU) architecture as part of a strategy to natively support a new wave of dataflow model based deep learning frameworks such as Google's TensorFlow and Microsoft's CNTK. Wave's family of deep learning computers achieves its best-in-class DNN training and inference performance through its native support of dataflow model based deep learning frameworks, its CPU-less high bandwidth shared memory architecture, and DPU's 16,000 parallel processing elements power and massive memory bandwidth. This results in a family of computers that delivers more than 10x improvement in compute performance for DNN training and more than 100x improvement in performance for DNN inference.

Artificial intelligence has never been as present -- or as cool -- as it is today. And, after years on the periphery, deep learning has become the most successful and most popular machine learning method around. DL algorithms can now identify objects better than most humans, outperform doctors at diagnosing diseases, and beat grandmasters at their own board game. In the last year alone, Google DeepMind's AlphaGo defeated one of the world's greatest Go player -- a feat most experts guessed would take another decade at least. Some of the finest minds in AI are at the Re•Work Deep Learning Summit in London this week to discuss the entrenched challenges and emerging solutions to artificial intelligence through deep learning. Researchers from Google, Apple, Microsoft, Oxford, and Cambridge (to name a few) are in attendance or giving talks.

General artificial intelligence, a machine that is capable of human-level expertise in multiple tasks, was the hot topic during the morning of the Rework Deep Learning Summit in London yesterday, with two of the UK's best AI companies Google DeepMind and Swifkey weighing in on the advances being made, and how far we are from a truly human AI. In his seminal piece about DeepMind for Wired magazine in June 2015, David Rowan wrote: "[DeepMind] showed that their artificial agent had learned to play 49 Atari 2600 video games when given only minimal background information. The deep Q-network had mastered everything from a martial-arts game to boxing and 3D car-racing games, often outscoring a professional (human) games tester." What this obfuscated was that the deep neural network was learning how to master each game one at a time. The same neural network couldn't, for example, flick between two different games and maintain its skill like a human would.

These changes are called "Industry 4.0" or the fourth industrial revolution. It is an industrial revolution that uses artificial intelligence and robots in such a way that manufacturing plants will become unmanned and a majority of office jobs will be made unnecessary. In March, an AI player of the board game go, developed by Google and named AlphaGo, defeated the world's leading professional go player 4 games to 1. The pro lost the first three games, and although he won the fourth, he was defeated in the fifth round. The decisive factor that led to the victory for AlphaGo was its "deep learning" capability.

The concept of artificial intelligence has been around for decades; Alan Turing first speculated that machines could one day think like humans back in the 1950s. But it's the combination of research breakthroughs, the wider availability of big data, and advances in graphics processing unit (GPU) technology that has ignited the AI explosion taking place today. When Google DeepMind's AlphaGo system beat South Korean champion Lee Se-dol at the ancient Chinese game Go in March 2016, it marked a turning point in AI's place in the public consciousness. Given that there are more possible Go positions than there are atoms in the universe, researchers had predicted it would be years before AI could become sophisticated enough to beat a human. AlphaGo used a form of AI called "deep learning" to master Go.

LONDON, UK / ACCESSWIRE / September 22, 2016 / Almax Analytics, a software platform delivering actionable insights, announced today they have emerged from stealth (development) with a customer ready product for the Financial Markets. Almax debuted in January 2016 as an Innovative Company to Watch by KPMG Luxembourg and quickly built demand and investor interest, thereby closing the seed round April 2016 to develop this first to market technology. "We are excited to present our product to the market and have worked hard to deliver this technology," says Balazs Klemm, Chief Executive and Founder. "On behalf of the company I need to thank our loyal supporters and the Almax team for their commitment and patience. We look forward to growing the business and already engaged in Series A financing discussions."

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

A deep learning platform that actually sells to the customer. This article outlines a successful sales automation platform that uses deep learning to drive customers down an order flow. Most sales processes are mostly linear and have a flow. A good sales agent knows how to guide the customer down this path gracefully. If the customer is deviating from the flow the sales agent knows how to sell the product and continue on with the order flow.

As if quantum computing wasn't mind-bending enough, one of D-Wave Systems' founders is now pursuing another futuristic idea: using artificial intelligence and high-tech exoskeleton suits to allow humans--and, at least according to one description of the technology, monkeys, too--to control and train an army of intelligent robots. Geordie Rose is a co-founder and chief technology officer of D-Wave, the Canadian company selling machines that it claims exploit quantum mechanical effects to solve certain problems hundreds of millions times faster than traditional computers. Now an IEEE Spectrum investigation has discovered that Rose is also CEO of Kindred Systems (aka Kindred AI), a stealthy startup he founded with others in 2014 dedicated to delivering advanced teleoperated and autonomous robots. The goal is making programming robots faster and less costly–and possibly revolutionize the world of work. Kindred has so far received well over 10 million in funding, according to Data Collective, the venture capital firm that led one of the rounds.

With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense.