The Case for Explainable AI (XAI)

Modern machine learning architectures are growing increasingly sophisticated in pursuit of superior performance, often leveraging black box-style architectures which offer computational advantages at the expense of model interpretability.

• Artificial Neural Networks offer significant performance benefits compared to other methodologies, but often at the expense of interpretability
• Problems and controversies arising from the use and reliance on black box algorithms have given rise to increasing calls for more transparent prediction technologies
• Hybrid architectures attempt to solve the problem of performance and explainability sitting in tension with one another
• Current approaches to enhancing the interpretability of AI models focus on either building inherently explainable prediction engines or conducting post-hoc analysis
• The research and development seeking to provide more transparency in this regard is referred to as Explainable AI (XAI)

Modern machine learning architectures are growing increasingly sophisticated in pursuit of superior performance, often leveraging black box-style architectures which offer computational advantages at the expense of model interpretability. 

A number of companies have already been caught on the wrong side of this “performance-explainability trade off”.

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In this video, we'll give an Introduction to Support Vector Machines. we'll implement Support Vector Machines using SciKit-Learn Library!

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We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.