Content I Consume 10/5/2024
Past weeks editions can be found here
It’s been two months since the last update! As I write this update, I’m sitting on a bench outside MIT’s Stata center. Stata designed by Frank Gehry is a divisive building. When it was first built the roof constantly leaked and MIT famously sued Gehry. If you take a look at the picture I linked above, I think you’d expect the building to have some issues.
The weather in Cambridge is nice this time of the year and I feel a sense of urgency to enjoy the outdoors while I can. My senior year at MIT is underway and I’m approaching my first deadlines for PhD applications. I enjoy the writing, but it’s draining. Adding applications on top of courses and research leaves little room for regular life. Even with this extra work, the semester has been great. So many exciting things going on.
Physics
Paper: Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
In this work, they teach large ML models to perform manipulation mathematical expressions which arise during quantum field theory calculations. I think this is a great first step towards ML/AI as a tool to perform theory calculations. In terms of the physics, they are calculating something called scattering amplitudes. If we simplify particle collisions, they can be described as,
A set of particles come together
An interaction between the particles occurs
Some set of particles come out of the interaction, not necessarily the same types or numbers of particles that came in
If we fix process (1) and (3), we are really saying that I want to put some fixed set of particles in and get some fixed set of particles out, how likely is this to happen. Scatter amplitudes provide a quantitative description this step (2). The difficulties arise because there are infinitely many ways an interaction could occur. Through quantum field theories, we can determine the relative importance of each of these infinite possibilities.
If we just look at say the top 3 most important ways the interaction could occur we would get a pretty good picture of what happens. However, say we want to test our theory against what experimentalists measure. Sometimes these measurements go up to 10 decimal places. So we need to consider as many possible ways the interaction could occur as a we can. This is where the difficulty arises. There can easily be 10,000+ terms in these calculations. This work develops ML models to aid in manipulating these many term calculations and can hopefully help theorists perform calculation at higher precision.
The training method is interesting. It treats manipulating mathematical expressions as a “translation” task. They find success, but I’m often suspicious when our approach to ML seems overly based on natural language processing and training large language models (like ChatGPT).
This paper is particularly exciting because it was a concerted effort by several prominent researchers in theory (Lance Dixon), experimental physics (Kyle Cranmer), and pure ML (Francois Charton). It seems there’s some real interest in this direction. I’m quite excited by this effort and hope to work on related projects in PhD. I think there’s much work to be done in developing these new tools.
Miscellaneous
Article: MIT class of 2028 to have fewer Black, Latino students after affirmative action ruling
The MIT class of 2028 was the first year of admissions after the SCOTUS ruling outlawing race-based affirmative action. Black and Latino enrolment dropped quite dramatically. I was expecting this drop in minority enrolment, but I was hoping it would come in conjunction with increased enrolment of low-income students. However, this doesn’t seem to be the case.
It’s quite an interesting time to be on campus and see the changing demographics play out in my own communities. These changes are large enough that it’s easy to see even in dorms and walking through campus. Only the coming years will reveal what the MIT community will look like.
