No, they asked one guy to do a joint publish conditioned on leaving the other collaborator out, with veiled threats. The joint publish part smells awfully like admission of guilt given there’s absolutely no reason to do it if you believe you independently arrived at the result using only public prior work. The leaving out collaborator part is outright academic malpractice. Disclosure: I was an academic once.
This "quadratic-time performance" is incredibly disingenuous. First, it's doing n operations that are each O(n), so it's more like "can have linear time performance, but done n times so I can give you a scary title".
Edit: A charitable take is constructing a set/dict from a list is indeed a common operation so it's worthwhile to think about its complexity, but it's not really one of the standard operations when discussing the performance of a hashset/hashmap, so really shouldn't be this handwavy.
And instead of attacking some straw man "It is indeed widely believed that ..." claim (widely believed by who?), why not attack what's literally on docs.python.org? https://docs.python.org/3/library/time-complexity.html:
> dict
> The times listed for dict objects are average-case times, as they assume the hash function for the objects is sufficiently robust to make collisions uncommon. They also assume the keys are well-distributed among the set of possible keys. In the worst case, when every key hashes to the same value, each of the O(1) operations below instead takes O(n) time. They also assume that hashing and comparing a key is O(1). For more detail on the implementation, see How are dictionaries implemented in CPython?.
> ...
> set, frozenset
> See dict as the set and frozenset implementations are similar, and the same caveats apply. In the worst case, O(1) operations instead take O(n) time, and operations that look up every element degrade accordingly.
You explicitly construct a list of ints that are all multiples of sys.hash_info.modulus and hence all hash to 0, no shit you get that well documented O(n) behavior.
The discussion of CPU cache is good though, so why hide that behind this clickbait.
That's the surprising part. It's pretty obviously spit out by /frontend-design, the tells are too obvious because Claude has built me too many of these, but /frontend-design by default will give you both a dark and a light theme and make it adapt to system theme with no prompting at all, so why would someone build a lazy stock /frontend-design but override that specifically?
That OpenAI setting helps, but there is a better way to do it. To completely opt-out of training, submit a request via the OpenAI privacy portal.
Visit this website https://privacy.openai.com/policies/en/ , click "Make a Privacy Request", choose "Do not train on my content", and complete the form. That submits a formal objection to training on your data, as required by GDPR/your local legislation.
Haha. I am 100% these companies will ignore this if they choose to. Just as they played fast and loose with copyright rules.
They would do it, the say “ah sorry chaps, impossible to extract it from the dataset by now, anyway we anonymized it so can’t tell what’s what, and we can’t risk losing to China. Oh look - did you see Superman fly outside?”.
This form is legally binding and has more legal weight than just clicking a toggle. If they still train on my data, they can get sued, and I'll get a payout.
The world is pretty pissed at the u.s. and seeking sovereign models and ai. Richest or not if the companies are wise they will try a bit not to piss off the entire rest of the world. At this point many countries are willing to cut off the u.s. even if they take a short or medium term hit.
As legaly binding as all the data the license and ToS of which they ignored when scrapping - before trying to sell it back with an eternal subscription to their plagiarism machine ?
Showing that a novel mathematical approach was in your prompts shortly before the model “proposes” that approach to relative amateurs would be ideal, if only we had a case like that…
It’s possible that they didn’t train on it, and the approach was derived by the LLM.
What’s a problem is that they haven’t outright denied it. That could be caution and them doing their due diligence first, it could be that it was intentional and they didn’t expect to get caught, or it could be because they have no way of knowing themselves.
This looks incorrect. OpenAI have flat out said that there's no need of doing this and both ways are equivalent
> We respect our users' choice whether to use their data to “improve our models for everyone” regardless of where they express that choice. Users can opt out in the in-app settings or indeed also in our privacy portal. They do not need to opt out in both places, and we will make this clearer in our Help Center.
Even if that's true right now, if one of the options is legally binding and the other is "we promise not to eat your data... for now", the first option is still better.
so what option do you leave them with? they are giving both alternatives and clearly stating both do the same thing underneath. there's literally nothing else they could have done here.
I don’t know what model codex uses for session summarization (I use Pro subscription, no third party models), but I get Chinese summaries from time to time, when the only Chinese that could have appeared in the session would be an i18n strings file that it may or may not have loaded. Very puzzling. Last happened yesterday.
Yeah, super ethical lab earnestly believing AI has 10% chance to kill all humans within a decade and doing utmost to protect humanity is partnering with MIC and Palantir in particular. Sounds about right.
Before you tell me about how the CEO has taken a principled stand: on record, he had no problem using it against 95%+ of humanity outside the U.S., and was only against fully automatic AI killing machines, and only citing the technical reality of then-current gen tech, so one should read that as human-rubber-stamped AI killing machines are totally fine with him.
You don't need to kill all humans, you just need to get better than them at zero sum games like resource extraction, and outcompete them. Then, you protect what's legally yours.
AI labs are working very hard at making them good at making them richer, and at making them respect the property rights of corporations.
One related problem I see is the pipeline for producing working mathematicians seems to have been completely and irreversibly decimated. What’s the point of doing a long and arduous PhD when all PhD level research problems that used to take months to years can be solved by far less talented people with $100/$1000/$10,000 to spare? How do you even select people into your program (this part is likely hypothetical, classical talent selection probably still works at the moment, but what about in a couple years)?
Disclosure: I did a theoretical physics PhD, but got admitted to quite a few top math programs back when I was applying to math and physics programs simultaneously. If you asked me whether I’d do a PhD today I’d say why bother.
Here most local STEM PhDs try to get into finance.
This is largely due to lack of funding for science and poor opportunities for PhDs. Why be a poorly paid postdoc when Jane Street is offering a million USD signup bonus ?
PhDs from poorer overseas do try to get related jobs here, mainly to be able to get a permanent resident visa.
> Why be a poorly paid postdoc when Jane Street is offering a million USD signup bonus?
One reason is despising that line of work. Quant firms were pummeling my @prestigious.edu inbox throughout my PhD and I fucking hated those parasites. Well, jokes on me if AI shatters my current career.
Btw a former classmate Caroline Ellison who went to Jane Street eventually landed in prison, lol.
prestigious.edu isn’t Stanford; went to a different school for PhD. And the prison bit is just something funny about Jane Street (that sort of vindicates my view of these people) that I edited in later, didn’t think much about it.
Sounds like https://github.com/gastownhall/beads to me. I prefer working solo, single-threaded on components to stay on top of agentic work, so it didn’t end up being a force multiplier for my workflows (and actually kinda got in the way from time to time due to me rebasing too much), but I can see its value when you have many agents working simultaneously.
Yes, crack open an intro to macroeconomics textbook and it has to discuss total factor productivity (technological progress) in the Cobb-Douglas production function, and its short run and long run effects on wages, or it's quite easy to connect the dots if it doesn't discuss that relationship directly. You'll get medium run too if you go a little beyond classical. It's funny someone claims to be writing economics papers but refuses to spend 100 hours, maybe less, to learn the fundamentals of macroeconomics as understood by everyone working in a related field.
> I simply could not have written this piece. I myself have no formal economics background
No shit, me neither, I read a couple macroeconomics textbooks at the age of 30 and now I wouldn't make arrogant and obviously wrong claims like the above. And I don't have the hubris to publish a paper.
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