Deep Learning Limitations and Flaws

Volume 2 - Issue 2

Bahman Zohuri¹* and Masoud Moghaddam²

Received: January 21, 2020;   Published: January 29, 2020

DOI: 10.32474/MAMS.2020.02.000138

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Abstract

With today’s growing interest toward Artificial Intelligence (AI) and its augmentation as part of integrated business from banking to eCommerce, medical applications and others, we are getting more and more dependency on AI in our day to day operations. However, the most sophisticated AI or Super AI (SAI) still needs to rely on its two other integrated sub-sets of components namely, Machine Learning (ML) and Deep Learning (DL). However, there certain limitation and flaws that exists within DL component of AI or SAI that will cause and error to grow way beyond control and will impact its main master component namely AI and SAI for its final processing of data and information in a trusted way of process of the precise decision making and prediction of event including any forecasting as part of its Use Case (UC) and Service Level Agreement (SLA) assigned as task to the AI or SAI. This article points few of these limitations and flaws that presently are concerns of the scientists and engineers behind the artificial intelligence technologies momentum. These sorts of limitation and flaws also would impact the Business Resilience System (BRS) from perspective of resiliency built into your daily business operations that is also pointed it out in this article.

Keywords: Resilience System; Business Intelligence; Artificial Intelligence; Super Artificial Intelligence; Image Processing; Cyber Security; Decision Making in Real Time; Machine Learning; Deep Learning

Abstract| Introduction| Deep Learning Limitation| Local Generalization Versus Generalization| Summary| References|