Aneesh Sathe

In his substack post today, Venkatesh Rao wrote about reading and writing in the age of LLMs as playing and making toys respectively. In one part he writes about how the dopamine feedback loop from writing drove his switch from engineering to writing. For him, writing has ludic, play-like, qualities. I have made almost all my “career” decisions as a function of play. I originally started off with a deep love of plants, how to grow them and their impact on the world. I was convinced I was going to have a lot of fun. I did have some. My wonderful undergrad professor literally hand held me …

Years ago, maybe a decade even, I fell in love with this software called Scrivener. I could never justify buying it because I didn’t actually write. But having that software would represent a little bit of the identity I would like to have, a writer. The Fourth of July long weekend gave me a running start. The plan was to write every day for a month. If I did, I would buy Scrivener. This was going quite well, then I couldn’t write for two days.

This post is a sloptraption, my silk thread in the CloisterWeb. The post was made with the help of NotebookLM. You can chat with the essay and the sources here: XAI NotebookLM Chat I. Executive Summary # The field of Explainable AI (XAI) aims to make AI systems more transparent and understandable, fostering trust and enabling informed human-AI collaboration, particularly in high-stakes decision-making. Despite significant research efforts, XAI faces fundamental challenges, including a lack of standardized definitions and evaluation frameworks, and a tendency to prioritize technical …

They look really juicy, she said. I was sitting in a small room with a faint chemical smell, doing one my first customer interviews. There is a sweet spot between going too deep and asserting a position. Good AI has to be just explainable enough to satisfy the user without overwhelming them with information. Luckily, I wasn’t new to the problem. Coming from a microscopy and bio background with a strong inclination towards image analysis I had picked up deep learning as a way to be lazy in lab. Why bother figuring out features of interest when you can have a computer do it for you, was my …

By 2015, I had heard of Bayesian Stats but didn’t bother to go deeper into it. After all, significance stars, and p-values worked fine. I started to explore Bayesian Statistics when considering small sample sizes in biological experiments. How much can you say when you are comparing means of 6 or even 60 observations? This is the nature work at the edge of knowledge. Not knowing what to expect is normal. Multiple possible routes to a seen a result is normal. Not knowing how to pick the route to the observed result is also normal. Yet, our statistics fails to capture this reality and the …