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
Detecting Video Piracy with Machine Learning
By Matthew Tooley & Thomas Belford, NCTA – The Internet & Television Assocation
2019
Optimizing Video Customer Experience with Machine Learning
By Mariela Fiorenzo, Claudio Righetti, María Cecilia Raggio, Fernando Ochoa & Gabriel Carro, Telecom Argentina S.A.
2019
A Machine Learning Pipeline for D3.1 Profile Management
By Maher Harb, Jude Ferreira, Dan Rice, Bryan Santangelo & Rick Spanbauer, Comcast
2019
Intel’s Vision Of Sports Immersion
By Guy Blair and Rajeeb Hazra, Intel Corporation - Intel Architecture Labs
2000
Machine Learning Applications in Cable TV Advertising: Usage and Challenges
By Srilal M Weerasinghe PhD, Charter Communications
2019
Operational Transformation Via Machine Learning
By Shamil Assylbekov, PhD. & Devin Levy, Charter Communications
2018
More Results >>