because it's likely that a lot more of the training data is in python than in rust, so coding models are less likely to mess up python code? just based on PL popularity stats e.g. https://madnight.github.io/githut/#/pull_requests/2024/1 if the training data is crawled from real codebases then there's gonna be more python than anything else.
in my personal experience, the one time I tried to do something in rust, opus flailed for several feedback cycles and I finally had to relent and do substantial guiding/intervention. which was not great bc I have no idea how to write rust either.
- Plan mode -> answer questions/make corrections, continue planning
- Some of us don't do full yolo mode all the time, then tool approvals or code reviews are required, nice to do a quick review and decide if you need to go back to your computer or not
- Letting claude spin or handle a long-running task outside of normal work hours and being able to check in intermittently to see if something crashed
I'm not sure people outside of Greater Boston would care, but those of us who do live there probably find it exceedingly strange that this occurred in Brookline of all places.
Well, currently we have a ton of Congresspeople who are primarily motivated by their "good financial sense" (for obvious reasons e.g. this study). So, I think we could do with a few more Congresspeople with less financial sense and more genuine motivation to improve the lives of their constituents.
Maybe this is a nitpick but CoNLL NER is not a "challenging task". Even pre-LLM systems were getting >90 F1 on that as far back as 2016.
Also, just in case people want to lit review further on this topic: they call their method "programmatic data curation" but I believe this approach is also called model distillation and/or student-teacher training.
I believe many high-quality embedding models are still based on BERT, even recent ones, so I don't think it's entirely fair to characterize it as "deprecated".
Is it actually more feasible now? Do LLMs actually make this problem easier to solve?
Because I have a hard time believing they can actually extract time increments and higher-level tasks from log data without a ton of pre/post-processing. But then the problem is just as much work as it was 5 years ago when you might have been using plain old BERT.
I suggest going through the exercise of seeing whether this is true quantitatively. Get a business-relevant NER dataset together (not CoNLL, preferably something that your boss or customers would care about), run it against Mistral/etc, look at the P/R/F1 scores, and ask "does this solve the problem that I want to solve with NER". If the answer is 'yes', and you could do all those things without reading the book or other NLP educational sources, then yeah you're right, job's finished.
Why do people pretend that alignment of AI is the important problem to solve, rather than alignment of the companies that run AI products with the wellbeing of humanity?
You are a teenager who needs oral contraceptives because you are sexually active. You don't want your parents to find out. Since you're a teenager, you have a few constraints:
- you have no car, how do you get to your doctor's appointment without asking parents for a ride?
- you are a minor, do you have any guarantee that your doctor won't tell your parents? You can't risk them finding out, they are very conservative
- you may not have ever made a doctor appointment for yourself before, maybe don't have access to insurance information etc
Planned Parenthood provides BCPs at a price you can afford with your teenager job (also guarantees privacy) but the closest one is hours away...
Certain brands (e.g. SkinnyPop) advertise their bags as "chemical-free" (SkinnyPop claims their's is free of PFOAS). Can anyone help me understand/verify these kinds of claims?
Does anyone know of a good piece of writing about what has made TSMC so successful, what makes their management so good, etc.? Seems like an operational exemplar I'd like to learn more about.
wrt your Spanish example: grammatical gender adds information redundancy to make it easier to process spoken language (e.g. helps with reference resolution). This redundancy enables Spanish speakers to speak at a relatively fast rate without incurring perception errors. English has fewer words but a slower speech rate. It's an optimization problem.
The speech rate issue isn't as obvious if you're only looking at text, but I'd argue/speculate that lossless speech as a language evolutionary constraint has implications for learnability.
tl;dr there is no communication tax, languages are basically equivalent wrt to information rate, they just solved the optimization problem of compactness vs speech rate differently
But there are tons of languages (not just CJK languages) that use either compounding or combos of root + prefix/suffix/infix to express what would be multiple words in English. E.g. German 'Schadenfreude'. Its actually way more useful to tokenize this as separate parts because e.g. 'Freude' might be part of a lot of other "words" as well. So you can share that token across a lot of words, thereby keeping the vocab compact.