Machine Learning: The Past, Present and the Future (2016)

By Narayan Srinivasa, Intel Corporation

Machine learning algorithms of the past were designed to capture the learning capabilities of the brain but with a high level of abstraction of its learning mechanisms. These abstractions resulted in shallow learning models that relied on hand crafted feature extraction on a problem specific basis with limited practical applications. The deep learning models of today represent the next generation of machine learning algorithms that can be trained from raw data using multiple processing layers due to novel modifications to the learning architecture compared to shallow learners. This development combined with the availability of raw data to train these models and the availability of fast affordable computers have enabled a great surge in its utility for many applications including video and audio pattern recognition. By exploiting the fundamentally different computing architecture and mechanisms prevalent in the brain, we believe that the next generation of machine learning called neuromorphic computing will advance the state-of-the-art in this field. In particular, it has the potential to realize energy efficient learning machines that could support a wide range of applications including internet of things, sensor processing, cybersecurity, robotics, mobile devices, diagnostics and prognostics and exoscale computing systems.

By clicking the "Download Paper" button, you are agreeing to our terms and conditions.

Similar Papers

Toward Automated Intelligent Resource Optimization for vCMTS Using Machine Learning
By Kieran Mulqueen, Michael O’Hanlon, Marcin Spoczynski, Brendan Ryan, Thijs Metsch, Leonard Feehan & Ruth Quinn, Intel
2018
Simplifying Field Operations Using Machine Learning
By Sanjay Dorairaj, Bernard Burg & Nicholas Pinckernell, Comcast Corporation; Chris Bastian, SCTE
2017
Applications of Machine Learning in Cable Access Networks
By Karthik Sundaresan, Nicolas Metts, Greg White, Albert Cabellos-Aparicio, CableLabs
2016
Network Capacity and Machine Learning
By Dr. Claudio Righetti, Emilia Gibellini, Florencia De Arca, Carlos Germán Carreño Romano, Mariela Fiorenzo, Gabriel Carro & Fernando Rodrigo Ochoa, Cablevisión S.A.
2017
Operational Transformation Via Machine Learning
By Shamil Assylbekov, PhD. & Devin Levy, Charter Communications
2018
Embracing Service Delivery Changes with Machine Learning
By Andrew Sundelin, Guavus, Inc.
2018
Intel’s Vision Of Sports Immersion
By Guy Blair and Rajeeb Hazra, Intel Corporation - Intel Architecture Labs
2000
Using AI to Improve the Customer Experience: A Virtual Assistant Chatbot
By Bernard Burg, Fan Liu, Abel Villca Roque, Sunil Srinivasa, Ryan March & Tianwen Chen, Comcast
2018
Time Teletext - Present And Future
By Pedro Barros, Barros & Associates Ltd. & John Lopinto, Time Video Information Services Inc.
1983
Fiber Optics Broadband Systems Present and Future
By Mircho A. Davidov, Catel Telecommunications Inc.
1987
More Results >>