I used this to build my personal site: https://ka.ge . It's a pixel-perfect (and functional) recreation of the windows 98 desktop environment in under a MB.
As with all text-only AI detection schemes, I am concerned about the accuracy of the detection. I'm skeptical of the methodology, specifically the final join of the three detector scores---how can you be sure that that final step does not introduce any biases? There's no source available, so it's difficult to tell exactly how this works or reproduce the research.
I've also uploaded text samples from my own (unreleased) research from pre-LLM era, and it's seemingly scoring pretty high on the LLM-detection scores. On other papers, nearly every sentence is highlighted as red "machine-leaning," but that does not impact the score? Additionally, there are dramatic differences between the scores for identical text with and without LaTeX formatting, despite the fact that it should not matter.
The takeaway from this should be "it is difficult to detect generated text and we should be careful about accepting results simply because they confirm a hypothesis."
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Relatedly, the text above scores as highly machine-written, despite the fact that I just wrote it with my human hands, I promise :)
I switched from iTerm2 because at the time (possibly still), iTerm2 had a performance bug where large amounts of underlined text would cause the terminal to slow down noticeably. Wezterm works perfectly, and I appreciate the .lua configuration over iTerm2's mess of menus.
Does not appear to work under Firefox, getting a bunch of CORS-related errors (header ‘user-agent’ is not allowed according to header ‘Access-Control-Allow-Headers’ from CORS preflight response) on the /graphql endpoint.
Yes, that's an http connection to the Docker Engine api on localhost failing due to the same issue—the docker engine cam't negotiate with the Docker Hub to get the new image and is passing the error back through the local api to your updater process.
There's native ad blocking on iOS and has been for a while—I've found that to significantly enhance the usability of the device. I use Wipr[0], other options are available.
Not OP, but common solutions in this space represent the state as conflict-free replicated data types (CRDTs). Some popular browser-based libraries for that are Y.js[0] and Automerge[1].
I remember reading the excellent Beej's Guide to Network Programming[0] and Beej's Guide to Unix IPC[1] as a teenager, which were incredibly approachable while still having depth—fantastic reads both and very influential on the programmer I ended up being.
For me, that's manual—I make sure everything I've cited is in the .bib file, and move papers into another collection if they end up not being relevant for a particular paper.
This is probably an automatable task, but it's generally not too much of a burden to do manually. I add a 'needs-review' tag to papers I've identified in a literature review, and categorize it when I get around to reading it.
Same here, except that I gather all the bibliography entries into a Zotero collection organized by paper and only export that to the latex workspace rather than using JabRef. Works a treat and keeps the size of the .bib file small.
There is no concrete concern past "models that can simulate thinking are scary." The risk has always been connecting models to systems which are safety critical, but for some reason the discourse around this issue has been more influenced by Terminator than OSHA.
As a researcher in the field, I believe there's no risk beyond overconfident automation---and we already have analogous legislation for automations, for example in what criteria are allowable and not allowable when deciding whether an individual is eligible for a loan.
If you're willing to hold your nose a little bit, Plaid[0] might be worth looking into. They have (had?) a testing tier which is more than enough for personal uses (100 linked accounts).
LIME and other post-hoc explanatory techniques (deepshap, etc.) only give an explanation for a singular inference, but aren't helpful for the model as a whole. In other words, you can make a reasonable guess as to why a specific prediction was made but you have no idea how the model will behave in the general case, even on similar inputs.
[ my public key: https://keybase.io/pkage; my proof: https://keybase.io/pkage/sigs/H5CBCtp_dXAcg78KYbrbG1uqmBaPgylN1Bfm166k1UY ]
meet.hn/city/us-Atlanta