it is remarkable how the majority of these awardees are in venues with minimal viewership/readership.

1. Many LORs were slop even before AI 2. The amount of mental effort you need to write a comprehensive LOR, if you actually know the candidate, is not astronomical, especially if you have minimal experience writing these letters. 3. One should never disclose online that they are putting confidential communications through pangram

In academia, the early bird generally does not catch the worm, when it comes to submitting something with a deadline. Submitting close to the deadline (with sufficient buffer to allow for portal problems) has no disadvantages, and sometimes has advantages (e.g. being able to include products/awards that materialize just before the deadline).

You would think that the emails sent to professors to submit LORs for grad school have the application deadline on them, and you would be wrong.

At freshman orientation 2002, Stanford touted Carly Fiorina (medieval history major) as their pinnacle inspiring alumna (this was before she ran HP into the ground)

It is important to maintain a healthy fear of being roped into a new committee when deciding whether to micromanage colleagues.

People with deep expertise in a specific niche are very sensitive to the uncanny valley when AI generated text is not obvious slop but also does not make a strong scientific case (while cosmetically looking like it says something specific). People in an adjacent field consistently read these AI-generated documents more favorably. It’s fascinating seeing this play out in panels.

Sensitivity to AI slop is sort of like how when you have deep expertise on a topic and you hear someone you thought was smart opining on it, and you realize they are not that smart. Scientific writing is a skill that is acquired during training, crucial for organizing and justifying your supposedly novel ideas. Some people could read a few dozen research papers in their field, feel the vibe, and write something in a similar style instinctively. Others got to the same place with many iterations of practice and feedback, or with some formal training. When you have that sensitivity to how scientific writing is supposed to read, AI generated text is very jarring and exhausting to read through. There are a lot of copes about Pangram, but if you use AI to clean up your grammar, shorten the text, or translate from your native language to English, it will reflect minimum AI content, and will also not be jarring to an expert human reader. It is not fair to demand human time to evaluate something you did not write yourself. If the output is a proposal, how can you execute the research if you can’t articulate the research? When search engines came along, it did not obviate the need to memorize; it only made it clear that if you don’t know anything, you don’t know anything, even if you can theoretically look it up (but how do you know what to look up). Similarly, the possibility of cosmetically outsourcing your thinking does not diminish the value of having your own thoughts (especially if your job is having novel ideas).

My department is doing a search in experimental condensed matter! recruit.ucdavis.edu/JPF07916

Important concepts that I learned only in materials science courses, not solid state physics: point groups and space groups, all the tea on dislocations, mechanical properties of solids beyond one lecture, actual curriculum related to liquid crystals beyond appropriating the terminology.

A path-dependent journey

I tried to sign up for the Claude Team plan for Scientists last week, and they could not verify me as a PI based on my website and google scholar (so much for AGI). I tried again with my website, my department website, my orcid, and my NSF grant--approved! Was it the extra info, or the bad publicity from the failed psyop that happened between the two attempts?