If you’ve worked in data science for AI a while, you’ve probably seen a lot of standard business data (login, view, click, purchase, etc.), and got sick of training vision models on slightly different images, or fine-tuning an LLM to spit out just the right text.

Among all the cognitive biases out there, survivorship bias has got to be one of my favorites. It feels so unnatural at first glance, yet it totally makes sense once it’s explained to you. It’s horribly pervasive in our daily lives and creates a remarkably bad feedback loop. For that reason, I wanted to talk a bit about it today and look at the underlying math that defines it.

Computex 2026 was another amazing experience, bringing together thousands of vendors across the whole technology spectrum. Much remained from 2025, but the overall focus of the event had definitely shifted from the previous year.

Recently I lost my Spotify subscription, and stubborn as I am, I decided to try surviving without it. My default response is to just find playlists on YouTube and listen to those. After vibing to some music for a while I realised that it might be AI generated. Sure enough, looking closer I found that all the music from the channel I liked was AI-generated, along with the video background. Quite impressive. Given I had access to some compute I thought it might be fun to experiment with creating something similar myself. The remainder of this post entails my experiment in creating a study music video.

In machine learning, a loss function is a measure of how well a given algorithm approximates the desired output given a certain set of inputs. Loss functions are an important part of the machine learning process as they not only indicate how well the algorithm works, but also provide insights into the model’s performance and allow for optimization of the model’s parameters.

I came across a fun game on social media recently, the rules are as follows: