Artificial Intelligence / Machine Learning Poka — Yoking your ML Model Fail proofing your AI model -Dr Srinivas Padmanabhuni, CTO , testAIng.com A very popular notion in quality management is the notion of Poka-Yoke. It is invented in Japan for ensuring quality. Poka-Yoke means ‘mistake-proofing’ or more literally — avoiding (yokeru) inadvertent errors (poka). In context of our daily lives there are
Artificial Intelligence / Machine Learning 10 Tests for your AI/ML/DL model CTO, testAIng.com In recent past there has been a spate of accidents involving AI and Machine learning models in practice and deployment. Much so that there is an active database of all such accidents being chronicled (https://incidentdatabase.ai/ ). At a time when AI is making strides in radical
Artificial Intelligence / Machine Learning Why current testing processes in AI/ML are not enough? The current notions of quality assurance and testing in AI/ML pipelines is based on the idea of validation using a random set-aside set of data on which the model is tested and metrics computed thereof. Metrics like accuracy on the random set-aside data set termed ambiguously as test data,
Artificial Intelligence / Machine Learning Why we need to let go of our programming instinct in ML based AI? In the modern world of software we are used to the paradigm of software engineering. The discipline of software engineering is predicated upon following a rigid regimen of quality programming, with deployment of expert coders and programmers to enable building of systems. Hence, a basic necessity often stressed is that
Artificial Intelligence / Machine Learning Why AI is required to have explainability? In today s world of machine learning dominated Artificial Intelligence applications, there is a renewed push for the agenda of explainability. The triggers for explainability could be multifold: * A comfort feeling of knowing what you are handing over control to * A knowledge of the influence of the precise input component
