7/20/2025

this past week was the international math olympiad!! it was exciting to see the performance of people i've met in real life, and they all did very well, though i'm not a fair judge especially since i'm not that good at oly math. also, what's funny is that i thought there was no way ai would win a gold, and initial reports from matharena.ai showed that it couldn't even achieve a bronze medal... and then the day after openai just had to tell everyone that they achieved a "not very open" model that could get 35/42 (coordbashing geo lol). the proofstyle is very strange... see in this github repo, almost like the ai has developed its own way of checking itself as it proceeds down the proof. people on r/singularity cheered! it will definitely be interesting to see the future of math competitions now, but it could be just like chess, after all ai solving these problems is not really an apples to apples comparison to the students who do math contests in general, as terence tao said (mathstodon sounds cool but you need to be 18+ :()

on the other hand, there are innovations like alphagenome that directly do good for the world, no doubt. it's marketed as a research tool to help scientists with their work and understand the genetic diseases that affect so many people in the world. innovations like these don't really drive people on r/singularity crazy. instead we correlate the possibility for superintelligence with ai's ability to do things we consider as high intelligence, like writing code, planning things out, and the getting gold on the imo. which makes sense, but such innovations are nontrivial too. in fact, even tho it's about biology which is not my best subject, i can kinda understand this stuff after my hs bio class!

alignment auditing - teaching model to exploit reward model (rm) biases and hide this objective to create "hidden rm syncophancy" --> given training data + model to auditors as a game (wait this makes ai research sound rlly fun lol)

particular methods: 1. turning model against itself, forcing it to play another role like user 2. interpretability methods like sae's to figure out the troublesome features though not sure whether it was just noticing surface-level semantic similarities

on the personal sides of things, here are some updates

while people worry a lot about shortages of data to train data-hungry machine learning models, there are tools like smote to help with class imbalance issues. unfortunately i tried to use this tool and it did not help me with raising my model's accuracy :( although it might just be a skill issue. 

got shap to work woo even tho model is not the best

i did some webdev stuff for both personal and org-related things

differential geometry is fun even though it takes a lot of effort to understand things. there are a lot of results that feel very disjointed but it's probably because i haven't processed everything well enough. cool thing is the power of curvature! we are mostly focusing on plane and space curves, so no wacky things you can't imagine. we also see cool curves like the tractrix

hmm i think i want to learn a lot more things in subjects i don't touch that much on my own time like physics (in particular!), history, art, and literature so hopefully i will blog more about those areas too :)

byebye