714 karma · joined September 19, 2016
[ my public key: https://keybase.io/pkage; my proof: https://keybase.io/pkage/sigs/H5CBCtp_dXAcg78KYbrbG1uqmBaPgylN1Bfm166k1UY ]
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 :)
Lockdown project during early COVID, I tried to be as close as possible to the original windows 98 functionality.
[0]: https://yjs.dev/ [1]: https://automerge.org/
[0] https://beej.us/guide/bgnet/ [1] https://beej.us/guide/bggit/
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.
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.
[0]: https://plaid.com/
- Lack of multi-GPU support,
- some other weird bugs related to autograd which i never fully figured out,
- and the killer one: none of my coauthors used Julia, so I decided to just go with PyTorch.
PyTorch has been just fine, and it's nice to not have to reinvent to wheel for every new model architecture.
One feature request: you could likely fully remove the need for an NPM package by accepting POST requests from forms, and staple the API key in with either the URL or an <input type="hidden" value="key"/>—is that in the roadmap?
I've settled on just using Poetry for most things, and then using pip in a venv to install the pyproject.toml file from Poetry either in a Dockerfile or directly on a cluster. That's worked fairly well so far, even with torch/cuda (and the mess of CUDA versioning) and from macOS to Linux.
I think uv/rye is a good next step, Poetry can be a bit slow as well at times.
Again, looking at the graph of emissions at Google on pg 31, it's clear that the increase is linear after a dip 2019-2020 and the report identifies supply chain issues as a major source of emissions in addition to datacenter electricity costs—again, notably not specifically calling out their AI training/inference costs as a reason for their increase. The report does identify AI as a challenge going forwards, however that's not the same as saying it's exclusively responsible for the last 5 years of emissions.
Source: work in the industry
[0] https://runestoneserverascholer.readthedocs.io/en/latest/ind...