Alexis, I just spoke with a few of my colleagues who were on the panel, who said "these inaccurate notes were not reviewed, endorsed" and are missing significant context.
In particular, the large % infected is not a forgone conclusion; coordinated action now can help.
Joshua Batson
2,139 posts
trying to understand evolved systems (🖥 and 🧬)
interpretability research @anthropicai
formerly @czbiohub, @mit math
Oakland, CA
Joined February 2012
- Replying to @thebasepoint and @AlexisMadrigalIn China, Korea, and Singapore, we have seen that 1) this virus is containable, at least in the short term, and 2) that 1) is true in diverse population structures, political systems, using different primary response methods. Good take on response:
- Great post "So you want to work in mechanistic interpretability" about skills to develop and resources to use, whether you're coming more from research or engineering. (link in thread)
- Single-Cell RNA-seq data is notoriously noisy. There are dozens of methods for cleaning it, but how do you know when they’re working right? How clean is too clean? Presenting...Molecular Cross-Validation biorxiv.org/content/10.110…
GIF - In writing this paper, there were countless features we thought might be bugs. After careful inspection, ~all of them revealed surprising and subtle model properties. To me this capacity for surprise is the true test of a new technique. This thread is about my favorite finding.The fact that most individual neurons are uninterpretable presents a serious roadblock to a mechanistic understanding of language models. We demonstrate a method for decomposing groups of neurons into interpretable features with the potential to move past that roadblock.
- Replying to @thebasepoint and @alexismadrigalHospitals are sensibly preparing for incoming cases by clearing floors, postponing procedures, setting up intake stations to evaluate people w/ symptoms (the vast majority of which still don't have COVID19).
- I'm happy to share Noise2Self, a framework for blind-denoising high dimensional measurements. We calibrate classical image denoisers and train deep neural nets; the same idea works on matrices of single-cell gene expression. Find out how: arxiv.org/abs/1901.11365 w/ @loicaroyer
GIF - Replying to @thebasepoint and @alexismadrigalWhat's missing is additional logistical resources for those institutions (who can requisition a gym?) and support for its staff (school's canceled, doctors have kids), together with financial support for the many whose livelihoods are going to be severely disrupted.
- Replying to @thebasepoint and @alexismadrigalLeadership to coordinate btw hospitals, public health depts, testing labs, cities, and schools is sorely needed, and to set clearer guidelines so everyone isn't trying to figure this out for themselves. A few governors are stepping up (@GovInslee especially). Hoping for more.
- I want to talk about a toy model for reasoning about what viral genomics can and cannot tell us about #SARSCoV2 transmission. Suppose viral isolates from two people have *identical* genotypes. How many transmission events separate them? 1/13
- One of our major blindspots in the last circuits paper was attention -- we could see what information attention moved, but not *why* it moved it. Today, the team is sharing an update interpreting attention patterns using features.
- Our recent circuit work has two steps: 1. Sparsely approximate the transformer model 2. Generate attribution graphs for prompts using those approximations We're releasing code for step 2, and using building on open-weights versions of step 1. But soon....Our interpretability team recently released research that traced the thoughts of a large language model. Now we’re open-sourcing the method. Researchers can generate “attribution graphs” like those in our study, and explore them interactively.
- Hi genomics/covid twitter! We are gearing up to do a lot of COVID19 mNGS of nasal + oral swabs, with goals of getting high-quality whole genomes and detecting secondary pathogens. Looking for advice on best practices for assembly and qc of genomes. Can you share your workflows?
- Replying to @thebasepointIt turns out the letters vs digits features were driven by *properties of the tokenizer*. Even on random alphanumeric strings, the tokenizer leaks information about future tokens! If you see a "7" the next token can't be an "8" because it would have been tokenized as "78".





