Machine Learning

Initially, I wanted this post to focus solely on metrics in machine learning. However, the concept of metrics is far more universal, and it doesn’t make sense to treat it as an isolated problem. This is more of a philosophical post, the ultimate goal is to make you think and reflect. We live surrounded by metrics: grades from teachers, performance reviews from employers, publication counts in academia, FLOPs in computing, ELO in chess, salary, IQ for intelligence, movie ratings on IMDB, book ratings on Goodreads, stars/reviews on Amazon, election results in democracies, F1 score in machine learning, GDP for countries, EBITDA in finance, likes/followers on Instagram, time spent on TikTok for content recommendation algorithms, etc. ...

Gaussian Process Regression is one of the most elegant and theoretically rich algorithms in machine learning. With this post, I want to celebrate the mathematical beauty underlying Gaussian Processes. I will divide this post into two sections: theory and practice, accompanied by code examples. One of the key advantages of Gaussian Processes compared to Deep Learning methods is that they inherently provide interpretability (through confidence intervals and uncertainty estimation). They also offer excellent extrapolation properties, as we will see, and a way to incorporate knowledge about the structure of the data into the model. However, these benefits come at a cost. The algorithm has a wide variety of hyperparameters that are difficult to configure; for instance, kernel selection alone is challenging. Understanding and having a good intuition for the inner workings of this algorithm (and the data) is key to making the most of it. ...

A few days ago, this paper was published in Nature [1] claiming a huge improvement in LLM inference using analog computing: “Our architecture reduces attention latency and energy consumption by up to two and four orders of magnitude” This could mean we may soon run LLMs on devices no bigger than smartphones while consuming less power than a light bulb. After taking a deep dive, I believe this might turn out to be one of the most influential results of 2025. ...