Enterprises collect large amounts of data and analyze it to obtain a competitive advantage. Some are using machine learning techniques to create predictive applications for fraud detection, demand forecasting, click prediction, and other data-intensive analyses. Recent advancements in machine learning make it possible to go even further, bringing deep learning within reach of developers everywhere. Now, computer vision, speech recognition, natural language processing, and audio recognition applications are being developed to give enterprises a competitive advantage. Processing large amounts of data for deep learning requires large amounts of computational power.

In the last couple of years, we have examined how deep learning shops are thinking about hardware. From GPU acceleration, to CPU-only approaches, and of course, FPGAs, custom ASICs, and other devices, there are a range of options--but these are still early days. The algorithmic platforms for deep learning are still evolving and it is incumbent on hardware to keep up. Accordingly, we have been seeing more benchmarking efforts of various approaches from the research community. This week yielded a new benchmark effort comparing various deep learning frameworks on a short list of CPU and GPU options.

Geoffrey Hinton is a pioneer in the field of artificial neural networks and co-published the first paper on the backpropagation algorithm for training multilayer perceptron networks. He may have started the introduction of the phrasing "deep" to describe the development of large artificial neural networks. He co-authored a paper in 2006 titled "A Fast Learning Algorithm for Deep Belief Nets" in which they describe an approach to training "deep" (as in a many layered network) of restricted Boltzmann machines. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. This paper and the related paper Geoff co-authored titled "Deep Boltzmann Machines" on an undirected deep network were well received by the community (now cited many hundreds of times) because they were successful examples of greedy layer-wise training of networks, allowing many more layers in feedforward networks.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

DEDHAM, MA--(Marketwired - Sep 7, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced that a leading multinational financial services firm has selected its Artificial Intelligence platform (RAGE AI) to drive improved results for its investment customers by using artificial intelligence to discover signals captured in a wide variety of data sources with Rage's innovative deep learning capabilities. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology as it incorporates proprietary linguistics-based machine learning innovations to understand market developments in the context of individual companies and interpret those signals as a human would. After demonstrating via historical back-testing that the Rage AI platform repeatedly delivered returns in excess of what the firm's quantitative team was able to produce, Rage's solution was integrated in order to drive significant lift in the returns generated for the firm's clients. In fact, Rage has repeatedly shown that its deep background in computational linguistics and Natural Language Understanding can systematically discover Alpha by forming assessments of a company's financial projections that effectively predict future performance for businesses such as Wal-Mart (attached), where Rage AI predicted an upward trend in stock price months in advance. The RAGE AI platform does this by continuously interpreting unstructured content from over 100,000 sources and translating it into valuable intelligence.

The development of the most advanced driver assistance systems (ADAS) in the industry should be based on integrated and open platforms. A complete solution is required for development, simulation, prototyping, and implementation to enable smarter, more sophisticated ADAS, and to pave the way for the autonomous car. This article summarizes the current status of DNN-based deep learning architectures built on top of a supercomputer on wheels, which are integrated in platforms to drive the future of autonomous vehicles. Deep learning is the most popular approach to develop AI. It is a way to enable machines to recognize and understand the world they are intended to operate in.

Bill Belichick doesn't have to worry. But the New England Patriots head coach and his NFL counterparts could one day consider AI as essential to their jobs as running practices or watching game tape. "AI will revolutionize sports," said Alan Fern, a computer science professor at Oregon State University who is using game videos and AI to teach computers to understand and coach football. If Fern is successful, teams would boost their brainpower with a new type of assistant coach – one powered by GPUs and a branch of AI called deep learning. It could supply strategic insights that might elude even the savviest coach.

This is a short follow-up to our last blog post about Hashpipe, our experiment in machine learning. In our early research for that project, we experimented with a convolutional neural network for image classification, using ConvNetJS. We understand that's pretty technical language. In a nutshell, the idea here was to see if we could teach a machine to recognize specific objects. Through those experiments we used deep learning to classify images into one of two buckets: pictures of Porsches and pictures of burgers.

Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today's biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the public. Researchers have been playing with the idea of deep learning for decades, but it has only blossomed in recent years. With companies like Facebook and Google pouring funds and resources into research, consumers are finally seeing the results of deep learning for themselves.

DEDHAM, MA–(Marketwired – Sep 7, 2016) – Rage Frameworks, a provider of knowledge-based automation technology and services, today announced that a leading multinational financial services firm has selected its Artificial Intelligence platform (RAGE AI) to drive improved results for its investment customers by using artificial intelligence to discover signals captured in a wide variety of data sources with Rage's innovative deep learning capabilities. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology as it incorporates proprietary linguistics-based machine learning innovations to understand market developments in the context of individual companies and interpret those signals as a human would. After demonstrating via historical back-testing that the Rage AI platform repeatedly delivered returns in excess of what the firm's quantitative team was able to produce, Rage's solution was integrated in order to drive significant lift in the returns generated for the firm's clients. In fact, Rage has repeatedly shown that its deep background in computational linguistics and Natural Language Understanding can systematically discover Alpha by forming assessments of a company's financial projections that effectively predict future performance for businesses such as Wal-Mart (attached), where Rage AI predicted an upward trend in stock price months in advance. The RAGE AI platform does this by continuously interpreting unstructured content from over 100,000 sources and translating it into valuable intelligence.