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Biologically Inspired Software Architecture for Deep Learning – Intuition Machine

#artificialintelligence

With the emergence of Deep Learning as the dominant paradigm for Artificial Intelligence based systems, one open question that seems to be neglected is "What guidelines do we have in architecting software that uses Deep Learning?" If all the innovative companies like Google are on a exponential adoption curve to incorporate Deep Learning in every thing they do, then what perhaps is the software architecture that holds this all together? The folks at Google wrote a paper (a long time ago, meaning 2014), "Machine Learning: The High-Interest Credit Card of Technical Debt" that enumerates many of the difficulties that we need to consider when building software that consists of machine learning or deep learning sub-components. Contrary to popular perception that that Deep Learning systems can be "self-driving". There is a massive ongoing maintenance cost when machine learning is used.