Скачать или смотреть Demystifying ‘Explainable AI’ By Malay Kumar Chief Architect at Manthan | Analytics India Magazine

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Artificial Intelligence (AI) is now being embraced across a broad range of industries such as retail, manufacturing, education, construction, law enforcement, finance, and healthcare. AI is fast becoming integral to our daily lives - from image to facial recognition systems, machine learning powered predictive and prescriptive analytics, hyper-personalized systems, conversational applications, autonomous vehicles, identification of symptoms across diseases - the applications are numerous. With such a heavy reliance on the capabilities of AI, the need to trust these AI systems with all aspects of decision making is becoming critical. The predictions and prescriptions churned out by AI enabled systems are having a tremendous impact on how we view and experience life, death, and personal wellness. This is especially true of AI systems used in healthcare, driverless cars, or even drones deployed during warfare. However, most of us have little visibility and knowledge on how AI systems make the decisions they do. In the absence of this clarity, it is even more difficult to comprehend how the results are being applied and consumed across various fields. Many of the techniques and algorithms used for machine learning are either virtually opaque, or defy easy examination. This is largely true for most of the popular algorithms currently in use; specifically, deep learning neural network approaches. Fortunately for us, there is an aspect of AI, called Explainable AI, which can direct computer systems to operate as expected, and generate transparent explanations for decisions they make. In the future we will need to focus more on the Explainable AI component in order to further build our trust on AI systems that are used in decision-making. In this presentation, we will explore various algorithms, and techniques, that support ease of comprehension, and interpretability, of these machine learning models.

Malay Kumar is Chief Architect at Manthan. He has envisioned and designed Manthan Maya, which
is an AI-powered advisor that helps run business operations by answering business queries, using
natural language through voice or text. His charter is to drive the science to scale to enterprise-class
levels, and provide automated and impactful insights. Malay also heads the R&D and Data Science
initiatives at Manthan.
Prior to Manthan, he worked with IBM and SolutionNet. Malay has more than 20 years of
experience in the IT industry. He holds a Master’s degree in Computer Applications from the
University of Madras.