Enjoyed reading this article, thanks for taking the time to carefully write it up!
Something I wanted to flag - I'm not totally convinced that people have a good calibration to identifying AI writing from human writing, at least without any helpful priors, such as the person's normal writing style. I haven't formally looked into this, but am curious whether you (or anyone else) had found any strong evidence that convinced you otherwise.
A few reasons to back up my skepticism:
Great post! I used Anki religiously during the first few years of my undergrad but eventually fell out of the habit, mostly because making new cards became too time-consuming. (I wish I had come across advice like this back then!)
A few anecdotes from my own experience:
This article explores the potential of model editing techniques in aligning future AI systems. Initially, I was skeptical about its efficacy, especially considering the objectives of current model editing methods. I argue that merely editing "facts" isn't an adequate alignment strategy and end with suggestions for research avenues focused on alignment-centric model editing.
Thanks to Stephen Casper, Nicolas Gatien and Jason Hoelscher-Obermaier for detailed feedback on the drafts, as well as Jim Davies and Esben Kran for high level comments.
Model editing, broadly speaking, is a technique which aims to modify information stored inside of a neural network. A lot of the work done... (read 4278 more words →)
For Interpretability research, something being worked on right now are a set of tutorials which replicates results from recent papers in NNsight: https://nnsight.net/applied_tutorials/
What I find cool about this particular effort is that because the implementations are done with NNsight, it both makes it easier to adapt experiments to new models, and you can run the experiments remotely.
(Disclaimer - I work on the NDIF/NNsight project, though not on this initiative, so take my enthusiasm with a grain of salt)