Well I did not attend to the fullest of my attention today so far, but I found Irene's talk interesting. Add to that the fact that I don't really know some of the speakers lol. But here are some pictures! I am not attending the celestial talk ongoing right now. For obvious reasons.
Strings 2025: Day 3 Amplitudes
Strings 2025: Day 2 Conclusion
The last talks of Day 2 of Strings 2025 have been concluded. These were nice but I missed the bulk of Hee-Cheol Kim's talk on 6D $(1, 0)$ SUGRA talk. Tomorrow will be awesome as well, and I will continue my career as a Strings paparazzi.
Strings 2025: Day 2 CFTs
Here are the post-lunch session pictures I took. I must say I tuned out for some of them, but these were interesting nonetheless. Particularly the modular invariance talk that I did not take enough notes on. The next ones will be after the coffee break and will be the last for Day 2 of Strings 2025.
Strings 2025: Day 2 Algebras
Today's Strings sessions were kicked off by Ed Witten's lecture on algebras, followed by Hong Liu, Nima Lashkari and Chris Fewster. Being the Strings 2025 paparazzi I am, I have took some pictures (i.e. screenshots). I will update with more as the day progresses.
Strings 2025: Day 1 Dark
I just took this absolutely hilarious picture of Day 1 review + panel discussion session on condensed matter stuffs where the hep-th folks are looking completely depressed seeing condensed matter people talk about gravity:
Machine Learning is just Statistical Mechanics with better marketing
I wrote a short article on machine learning that I wished to submit for a magazine that Aayush and friends are editing. It is not much and is frankly very superficial and fast, but it does contain some things I wanted to say before, such as about the discarding of mathematical ``pedantics" by a lot of the machine learning community. Also, the next edition of Bulk Physics, Algebras and All That will be on Calabi-Yau manifolds and will include a discussion on the calculation of the Hodge numbers and the neural network model used to calculate them.
Machine Learning is just Statistical Mechanics with better marketing



















































