and concentrating on the top few factors and counterfactuals — Explainable AI is hard Explainable AI — or “xAI” as it is called sometimes — is hard — both figuratively and computationally. Figuratively because the scope, purpose, and effectiveness of explainability are often hard to anticipate precisely and formalize in a problem definition or a user case study. …

The concept of data drift is illustrated visually in various shapes and forms. — A short introduction to data drift Machine learning (ML) models, at least in their present incarnations, are little more than powerful inductive machines. Their lofty goal is to go from a small example set to a broader inductive generalization. Naturally, for successful (and dependable) functioning, they need to depend on the critical assumption — the input…

We point out a few less-mentioned and underrated features and inter-relationships to keep in mind while designing the best hardware combination for our AI workload — Software-dictated-hardware Software is eating the world and Artificial Intelligence (AI)/ Machine Learning (ML) is eating the software. More and more business leaders are coming to the same conclusion that every business, irrespective of its end product or service, must integrate AI tools and techniques into its business processes and roadmap planning…

A lightweight Python package can give you a lot of insights into your regression problems — Why insights are needed? First thing first. Why is linear regression important? Linear regression is the fundamental technique, which is rooted strongly in the time-tested theory of statistical learning and inference, and powers all the regression-based algorithms used in the modern data science pipelines. …

Some ideas for helping smart city initiatives, based on AI perception and citizen data sourcing — AI for smart cities Artificial Intelligence (AI) and Machine Learning (ML) tools and techniques are already being applied to smart city and neighborhood projects around the world. Specific application areas are only limited by the imagination of the public and city planners, such as, traffic management,

A step-by-step guide to building a digital twin example of an electronic switch (transistor) with Python — What is a digital twin? IBM defines a digital twin as follows “A digital twin is a virtual model designed to accurately reflect a physical object”. They go on to describe how the main enabling factors for creating a digital twin are the sensors that gather data and the processing system that inserts the data…

Outliers in the data are ubiquitous, and they can mess up your regression problem. Try Hubber regressor to tackle this problem. — What’s the problem? Let’s say you have a dataset with two features X1 and X2, on which you are performing linear regression. However, some noise/outliers got introduced in the dataset. Say, the y-value outliers are exceptionally low as compared to what they should be. How does that look like?

Linear relationships are not all a correlation analysis can reveal. We discuss rank-based correlation that is more generalized and powerful. — Beyond linear relationship It’s a common data science interview question. Say, y = x² + noise, you plot y vs. x and try to determine the correlation coefficient. What will it be? Most interviewers won’t give you credit if you say anything other than zero. …