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Proceedings 2017, 1(3), 186; doi:10.3390/IS4SI-2017-04025

Cognitive Computing Architectures for Machine (Deep) Learning at Scale

CEO and Founder, SCUTI AI, Palo Alto, CA 94306, USA
Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.
Published: 9 June 2017
Download PDF [467 KB, uploaded 18 July 2017]

Abstract

The paper reviews existing models for organizing information for machine learning systems in heterogeneous computing environments. In this context, we focus on structured knowledge representations as they have played a key role in enabling machine learning at scale. The paper highlights recent case studies where knowledge structures when combined with the knowledge of the distributed computation graph have accelerated machine-learning applications by 10 times or more. We extend these concepts to the design of Cognitive Distributed Learning Systems to resolve critical bottlenecks in real-time machine learning applications such as Predictive Analytics and Recommender Systems.
Keywords: machine learning; cognitive computing; distributed computing; knowledge structures; heterogeneous computing machine learning; cognitive computing; distributed computing; knowledge structures; heterogeneous computing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mittal, S. Cognitive Computing Architectures for Machine (Deep) Learning at Scale. Proceedings 2017, 1, 186.

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