by Staff Writers
Redwood City CA (SPX) Jun 22, 2017
The exponential increase in the use of connected real-time sensors to surface streaming data in the age of the Internet of Things presents significant challenges and opportunities for the emerging field of streaming analytics. Detection of anomalies in streaming data, in particular, has becoming an increasingly important application across a large number of industries for critical use cases - ranging from preventative maintenance to fraud prevention, fault detection, and systems monitoring. But the real-time nature of streaming data has presented challenges for applying classic AI and machine learning techniques to date.
Neuroscience and machine intelligence researchers at Numenta Inc. have demonstrated how a novel anomaly detection algorithm, based on their theory of how the brain works, can tackle the problem with a technique that meets the requirements of streaming data by processing data in real-time and offering continuous, online detection without supervision - while simultaneously making predictions. The technique is based on an online sequence memory algorithm called Hierarchical Temporal Memory (HTM).
Numenta researchers have described the technique in a new peer-reviewed paper, "Unsupervised real-time anomaly detection for streaming data",* published in a special issue of Neurocomputing.
In the new paper, the researchers also present the results of using the Numenta Anomaly Benchmark (NAB), a benchmark containing real-world data streams with labeled anomalies. NAB, an open-source benchmark and tool designed to help data researchers evaluate the effectiveness of algorithms for anomaly detection in streaming, real-time applications, was first presented in 2015 during the IEEE Conference on Machine Learning and Applications. NAB provides a first-of-its-kind controlled open-source environment for testing a wide range of anomaly detection algorithms on streaming data. Numenta offers the open standard benchmark for the research community to use, add to, and even draw inspiration from for new, innovative techniques.
"While many anomaly detection approaches exist for time-series data, the majority of methods are limited and apply statistical techniques that are computationally lightweight for streaming analytics. The versatile properties of HTM, which are patterned after the principles of how the brain works, make it well suited for streaming anomaly detection," said Numenta Research VP Subutai Ahmad.
"We are bridging the gap between neuroscience and AI by using brain function as a guide to solving machine learning problems and designing more intelligent systems," added Ahmad.
The release of the latest technical paper in Neurocomputing, which Ahmad co-authored with Numenta researchers Alexander Lavin, Scott Purdy and Zuha Agha, is in keeping with Numenta's open research philosophy. Numenta researchers' previously published peer-reviewed works published in the journals Frontiers and Neural Computation, among others.
Numenta Article "What Intelligent Machines Need to Learn from the Neocortex" Appears in IEEE Spectrum
Numenta's work at the intersection of neuroscience and AI is also featured in the current issue of IEEE Spectrum magazine. The special issue on worldwide efforts to understand the human brain to use the knowledge to build next-generation computers features a by-lined article by Numenta co-founder Jeff Hawkins.
In the article, Hawkins argues why understanding the brain is critical for building intelligent machines. Hawkins writes: "Although machine-learning techniques such as deep neural networks have recently made impressive gains, they are still a world away from being intelligent, from being able to understand and act in the world the way we do. The only example of intelligence, of the ability to learn from the world, to plan and to execute, is the brain. Therefore, we must understand the principles underlying human intelligence and use them to guide us in the development of truly intelligent machines."
For the complete IEEE article go here.
Boston MA (SPX) Jun 16, 2017
"Deep Learning" computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical form ... read more
Krause Taylor Associates
All about the robots on Earth and beyond!
|The content herein, unless otherwise known to be public domain, are Copyright 1995-2017 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. Privacy Statement|