Rajadhyax

How the humble prediction method shows us the way to Generative AIThere are numerous writings about Generative AI. There are essays dedicated to its applications, ethical and moral issues, and its risk to human society. If you want to understand the technology itself, there is a range of available material from the original research papers to introductory articles and videos. Depending on your current level and interest, you can find the right resources for study.This article is written for a specific class of readers.…

Discussion on an entry level data science interview questionData Science interviews vary in their depth. Some interviews go really deep and test the candidates on their knowledge of advanced models or tricky fine-tuning. But many interviews are conducted at an entry level, trying to test the basic knowledge of the candidate. In this article we will see a question that can be discussed in such an interview. Even though the question is very simple, the discussion brings up many interesting aspects of the fundamentals of…

Closing the loopThe objective of this series is to suggest a new measure of performance for ML systems. In Part 1 and Part 2 we defined the problem and termed it as the ‘Open Loop of ML’. In this part, we will suggest a remedy and claim to have closed the loop.Image by Rajashree RajadhyaxFirst, let’s take a quick review of the first two parts:The first part pointed out that the metric of model accuracy gives a good closure to the developer, but does not indicate the utility of the system in the real world. This…

Why model accuracy is a deceptive metricThe first part of this series was about the psychology of ML developers. The ‘accuracy’ metric associated with model development provides a mental closure to the model builders. However, the accuracy metric is the outcome of the model training process, and it has little bearing on the usefulness of the model in the real world. I have proposed that the artificial closure is a major reason for the discrepancy between the number of prototypes and actual working ML systems.After the…

How a psychological effect is blocking the progress of MLOver the last few years, I have watched hundreds of students and engineers building machine learning models. I have had many opportunities to be part of the jury in project competitions of Engineering Colleges. Similarly, I have served as a judge in a number of hackathons where I saw the contestants building models and systems in 36 or 48 hours, working day and night. As part of my responsibilities, I have reviewed the work of many interns and also interviewed…