Hybrid AI for IoT Actionable Insights & Real-Time Data-Driven Networks

Hugo Latapie, Mina Gabriel, Ramana Kompella
Proceedings of the Third International Workshop on Self-Supervised Learning, PMLR 192:127-131, 2022.

Abstract

Significant increases in industry requirements for network bandwidth are seen year upon year. The exponential growth in streaming data is matched by an increase in the use of machine learning and deep learning to glean actionable – ideally real-time – insights from these data. However, approaches based on artificial neural networks (ANNs) are often insufficient in terms of functionality, flexibility, accuracy, explainability, and robustness. The demand for new model development and continual updating and retraining is outstripping the model generation capacity of data scientists and others in the field. This gap between supply and demand for real-time data driven insights continues to grow. In this paper we introduce a hybrid AI solution which adds several elements into the ML/DL mix, specifically a new self-supervised learning mechanism, a knowledge model engineered to include support for machine generated ontologies as well as traditional human-generated ontologies, and interfaces to symbolic AI systems such as OpenNARS, AERA, ONA, and OpenCog, among other elements. Our hybrid AI system enables self-supervised learning of machine-generated ontologies from millions of time series, to provide real-time data-driven insights for large-scale deployments including data centers and enterprise networks. We also apply the same hybrid AI to video analytics use cases. Our preliminary results across all the use cases we have attempted to-date are promising although more work is needed to fully characterize both the benefits and limitations of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v192-latapie22a, title = {Hybrid {AI} for {IoT} Actionable Insights & Real-Time Data-Driven Networks}, author = {Latapie, Hugo and Gabriel, Mina and Kompella, Ramana}, booktitle = {Proceedings of the Third International Workshop on Self-Supervised Learning}, pages = {127--131}, year = {2022}, editor = {Thórisson, Kristinn R.}, volume = {192}, series = {Proceedings of Machine Learning Research}, month = {28--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v192/latapie22a/latapie22a.pdf}, url = {https://proceedings.mlr.press/v192/latapie22a.html}, abstract = {Significant increases in industry requirements for network bandwidth are seen year upon year. The exponential growth in streaming data is matched by an increase in the use of machine learning and deep learning to glean actionable – ideally real-time – insights from these data. However, approaches based on artificial neural networks (ANNs) are often insufficient in terms of functionality, flexibility, accuracy, explainability, and robustness. The demand for new model development and continual updating and retraining is outstripping the model generation capacity of data scientists and others in the field. This gap between supply and demand for real-time data driven insights continues to grow. In this paper we introduce a hybrid AI solution which adds several elements into the ML/DL mix, specifically a new self-supervised learning mechanism, a knowledge model engineered to include support for machine generated ontologies as well as traditional human-generated ontologies, and interfaces to symbolic AI systems such as OpenNARS, AERA, ONA, and OpenCog, among other elements. Our hybrid AI system enables self-supervised learning of machine-generated ontologies from millions of time series, to provide real-time data-driven insights for large-scale deployments including data centers and enterprise networks. We also apply the same hybrid AI to video analytics use cases. Our preliminary results across all the use cases we have attempted to-date are promising although more work is needed to fully characterize both the benefits and limitations of our approach. } }
Endnote
%0 Conference Paper %T Hybrid AI for IoT Actionable Insights & Real-Time Data-Driven Networks %A Hugo Latapie %A Mina Gabriel %A Ramana Kompella %B Proceedings of the Third International Workshop on Self-Supervised Learning %C Proceedings of Machine Learning Research %D 2022 %E Kristinn R. Thórisson %F pmlr-v192-latapie22a %I PMLR %P 127--131 %U https://proceedings.mlr.press/v192/latapie22a.html %V 192 %X Significant increases in industry requirements for network bandwidth are seen year upon year. The exponential growth in streaming data is matched by an increase in the use of machine learning and deep learning to glean actionable – ideally real-time – insights from these data. However, approaches based on artificial neural networks (ANNs) are often insufficient in terms of functionality, flexibility, accuracy, explainability, and robustness. The demand for new model development and continual updating and retraining is outstripping the model generation capacity of data scientists and others in the field. This gap between supply and demand for real-time data driven insights continues to grow. In this paper we introduce a hybrid AI solution which adds several elements into the ML/DL mix, specifically a new self-supervised learning mechanism, a knowledge model engineered to include support for machine generated ontologies as well as traditional human-generated ontologies, and interfaces to symbolic AI systems such as OpenNARS, AERA, ONA, and OpenCog, among other elements. Our hybrid AI system enables self-supervised learning of machine-generated ontologies from millions of time series, to provide real-time data-driven insights for large-scale deployments including data centers and enterprise networks. We also apply the same hybrid AI to video analytics use cases. Our preliminary results across all the use cases we have attempted to-date are promising although more work is needed to fully characterize both the benefits and limitations of our approach.
APA
Latapie, H., Gabriel, M. & Kompella, R.. (2022). Hybrid AI for IoT Actionable Insights & Real-Time Data-Driven Networks. Proceedings of the Third International Workshop on Self-Supervised Learning, in Proceedings of Machine Learning Research 192:127-131 Available from https://proceedings.mlr.press/v192/latapie22a.html.

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