Weekly QuEST Discussion Topics and News, 8 July

This week our colleague, Ryan K, will provide lead us in a discussion of topological approaches to big data as an alternative to some of the deep learning approaches we’ve covered recently in our meetings.

The implementation of machine learning and deep learning approaches to multiple data types is providing increased insights into multivariate and multimodal data. Although inclusion of machine learning and deep learning approaches has dramatically enhanced the speed of data to decision processes, there are multiple drawbacks that include “black box” and “hidden layers” that obfuscate how these learning approaches draw conclusions. In addition, as the world changes, these analytic methods are often brittle to the inclusion of emergent or unannotated data. One potential alternative is the extension of topological data analysis into a real-time, deep learning, autonomous solution network for data exploitation. In this application, black-boxes and hidden layers are replaced by a continuous framework of topological solutions that are each individually addressable, are informatically registered to disseminate annotation across the solution network, provide a rich contextual visualization for data exploration, and contextually incorporate emergent data in near real-time. By creating a deep learning analytical approach that implements topological data analysis as the analytic backbone, underlying methodologies can be created to autonomously formulate hypotheses across the network. To realize this, fundamental questions must be addressed for full implementation that include mathematically optimizing topological projections across parameter spaces, connecting topological nodes in an ecological model for optimized computational power and ontological tracking, comparing real-time updated topological nodes to a hard-coded digital twin which preserves historical knowledge, and automating network feature analysis across the topological network for prompting analyst review. Incorporation of the topological data analytic backbone with ingestion, curation, transformation, and other visualization components can provide a deeper learning competency that can redefine autonomous learning systems, artificial intelligence, and human machine teaming.

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