Abhishek Rao's blog

Autofunctions: Exploring Recursive Utility in Everyday Objects Introduction In a world teeming with multifaceted objects and tools, a curious phenomenon often goes unnoticed: the ability of some objects to perform their primary function on others of their kind. I've coined a term for this fascinating concept: "Autofunctions." This blog post delves into the realm of autofunctions, exploring their presence in our daily lives and their implications in various fields. Defining Autofunctions An autofunction is when an object or tool applies its primary function to another of its kind. Think of a bag carrying other bags, a crane lifting another crane, or a software debugging tool debugging itself. This phenomenon transcends simple utility, reflecting a self-referential or recursive nature in the design and function of these objects. Everyday Examples To truly grasp the concept of autofunctions, let's look at some commonplace examples: Containers Holding Containers: This inc...

Originally published in https://ideafair.bearblog.dev/why-we-enjoy-music/ Why we enjoy music 10 Jan, 2021 M usic and ML I think the reason why we enjoy music can be explained in terms of Machine Learning (ML). Music can be seen as an audio signal that has multiple levels of patterns in it. When we listen to music our brain is trying to learn and predict this pattern. We enjoy music when the brain is finally successful at this task. There must be some innate reward function for being able to learn and predict patterns successfully in our brain. Confidence and Accuracy To go bit deeper into when exactly we enjoy music the most, we can look at our brain's confidence & accuracy of the music prediction. At first both the confidence and accuracy are low, but after listening to the tune several times our accuracy increases. At this point we are able to predict with higher accuracy, but the confidence is still low. This is my guess when the enjoyment is highest as we are building confi...

 This was originally posted on https://ideafair.bearblog.dev/ml-system-design/ on 1 Jan 2021 ML System design interview 01 Jan, 2021 This post details how a typical Machine Learning (ML) system design interview is conducted. It is quite similar to a software engineering system design interview, but there are many differences which I will list here. The candidate is given a problem and asked how they would go about designing the solution for the problem. Some example problems are: plagiarism detector for a class, YouTube video recommender, grammar correction service etc. The interviewer then expects the candidate to lead most of the discussion, occasionally bringing in what-ifs, asking clarifying questions, digging down on the details etc. My approach for solving ML design is to divide it to following sections. Sections of interview Problem clarification It is good to ask as many questions as possible, state out all of your assumptions clearly. Not doing any one of the above an...