Researchers at the Johns Hopkins University Applied Physics Laboratory (APL) utilized a pattern recognition in concert with a surgical process called targeted muscle reinnervation that allowed a Colorado man to control two prosthetic arms and hands. The patient had lost both arms in an electrical accident about 40 years ago. Johns Hopkins Trauma Surgeon Albert … [Read more...]
MIT Study Finds Computer Neural Networks Identify Visual Objects As Well As The Primate Brain
A new study from MIT neuroscientists has found that one of the latest generation of so-called “deep neural networks” matches the object recognition ability of the primate brain. This improved understanding of how the primate brain works could lead to better artificial intelligence and, someday, new ways to repair visual dysfunction, notes Charles Cadieu, a postdoc at the … [Read more...]
The Unabridged Chapter 1 Introduction To High Performance Parallelism Pearls
Following is the full, unabridged text of the chapter 1 introduction (written by James Reinders) to High Performance Parallelism Pearls. Thanks to Morgan Kaufmann, James Reinders, and Jim Jeffers for giving permission so TechEnablment can make this available. After reading what James wrote, you will see that summarizing the introduction would simply have left out too much … [Read more...]
Programming Deep-learning Neural Networks to Solve Tasks
Deep-learning neural networks can be programmed, or structured by a human to perform one or more complex tasks. The key requirements are the ability to (1) design the network topology and (2) lock weights in the ANN (Artificial Neural Network) during training. A powerful example of structured deep-learning comes from the 1993 Farber, et.al. paper, "Identi fication of … [Read more...]
GPUs Power Over 90% of ImageNet Deep-Learning Visual Recognition Challenge Entries
Over 90 percent of the participating teams and three of the four winners in the prestigious 2014 ImageNet Large Scale Visual Recognition Challenge used GPUs to enable their deep learning work. Deep learning is a fast-growing segment of machine learning that involves the creation of sophisticated, multi-level or “deep” neural networks. These networks enable powerful … [Read more...]
Deep-Learning Behind Microsoft Cross-Language Real-Time Skype Translator
Deep-learning lies at the heart of the Microsoft Skype translator, a near real-time speech-to-speech machine translation tool that enables voice conversations between individuals speaking different languages. The claim is that a beta version of Skype Translator will be released sometime in 2014. According to Microsoft, machine-learning of large datasets culled from social … [Read more...]
Robobrain.me
The Cornell Robo Brain (http://robobrain.me) is a large-scale learning system that attempts to learn from publicly available Internet resources, computer simulations, and real-life robot trials. The idea is to associate objects in images with text in order to correlate how they relate to human language, behavior and usage. Applications include prototyping for robotics … [Read more...]
Monetizing Image Recognition By Looking at the Background
Deep-Learning image recognition is a hot-topic. The billion dollar thought is to create a "Google" of image search (or a mesh-search engine for 3D printing and animation), but that requires rather high search fidelity. A lower-fidelity approach is to use key information provided in selfies - specifically the identity of the individual in the … [Read more...]
Deep-learning Webinar Demonstrates Handwriting Recognition and Efforts to Teach Drone to Fly Down a Wooded Path
Deep-learning is a computational expensive but rewarding method to solve many complex pattern recognition problems. The recent NVIDIA webinar by Dan Claudiu Cireșan, Senior Researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Switzerland highlighted some of the capabilities of deep-learning for image recognition problems such as handwriting recognition … [Read more...]
IBM TrueNorth a “Bee Brain” on a SyNAPSE Chip That Uses 70 mW
IBM unveiled the first neurosynaptic computer chip on August 7th that implements one million programmable neurons, 256 million programmable synapses and 46 billion synaptic operations per second per watt. The IBM announcement, published in Science in collaboration with Cornell Tech, is a step towards bringing cognitive computers to society.At 5.4 billion transistors, this fully … [Read more...]