Eh... I think he puts the LLM down for his own ego's sake (as would I!). Curl may, next to the Linux kernel, be one of the most heavily audited codebases in existence. The LLM found something he and thousands of others missed. It's not unimpressive.
The claim has never been that the new model could not do impressive things. The claim is that the new model is not the existential crisis Anthropic’s initial announcement post made it out to be.
It's touching to hear Anthony Bourdain reminiscing about laundry. The point about a laundry maid giving me back time better spent engineering is a practical dilemma.
You will be subject to mandatory facial recognition technology with long-term storage, though. The US may certainly be the worst but Europe is also going in an authoritarian direction.
In "why not both" news, both sides are pursuing increasingly aggressive immigration enforcement, which is imposed on everyone who goes through an airport.
(Intra-Schengen flights lets you avoid most of this, but the heavier enforcement on extra-Schengen is the tradeoff)
You can't compare context to memory. Context is simply all the text the LLM can use to generate a likely continuation. Imagine you're a relationship expert and I'm asking you for relationship advice. You don't know me so the best you can give me is "be yourself!" or "be confident!". It doesn't matter how good you are---lack of information about me is your limit. Now imagine you have a complete view of my dating history, including in-depth reviews from ex-girlfriends and whatnot. You could come up with some sharp and very fine-tuned advice just for me. Or maybe it still would be "be yourself!" cause dating advice is pseudoscience but you get my point.
The only test that has worked 100% of the time for me is to read the candidate's code. Two hours is enough to precisely estimate the candidate's qualities as a software developer. I never understood why companies waste time with tests and quizzes because since it is so easy for me it should be just as easy for other software developers too. Of course, a candidate may be a jerk or unfit for other reasons, but ranking them on a software developer hot-or-not scale is not very difficult.
The automated ones don't care, but it absolutely matters for the informal credit assignment process that actually runs academia.
I really wish we had a better way to "name" papers. Big clinical trials often have an acronym (often hilariously forced: "CXCessoR4"). That takes the emphasis off (one) lead author but it's implausibly hard to make up one for every research paper.
the one where i think of a particular piece of work, and i know who did it, then tell a student "oh, see if $author's group published anything else about this."
i'm not using software for this if this is off the top of my head, and it's the sort of thing that, at scale, hurts the forgotten author and their students
There’s a cute study demonstrating this effect by comparing career success in economics and psychology.
The author lists for economics papers are traditionally alphabetized, so more of your output will be known by your name if it occurs early in the alphabet. Abbie Ableson gets lots of mentions as "Ableson et al." while Zhang Zhu will almost always be relegated to the "et al". If name recognition matters, you’d expect successful academic economists to be clustered at the beginning of the alphabet—-and this appears to be true.
I don’t know that everyone would label it like that, but it’s inarguably true that success in academia comes from your reputation/name recognition.
Metrics are often attempts to formalize this but they’re not how most people actually make decisions: nobody is inviting seminar speakers or choosing collaborators because they have a high h-index. If anything, it goes the other way: name recognition gets you invited to speak or collaborate, which makes more people aware of your work, which boosts metrics.
That is false. The first thing everyone (at least everyone in CS---IDK about other fields) looks at are h-indexes, impact factors, number of papers per year, university rankings, and similar metrics. Researchers are most definitely selecting collaborators with a high h-index.
Cmon…We’re saying that a certain style of reference gives her less credit than might be due. Not none at all.
One paper doesn’t make a career (she wrote many dozens), it’s not always cited weirdly, and even if it is, some people may remember the coauthors (as they should).
But since you mention lived experience, I’ll add that I’ve certainly been asked if I’m "even aware" of results from co-authored papers where my name was listed second—-and I don’t think this is very uncommon experience.
It maps (-inf, inf) to (0, inf) in about as nice a way as you could expect (addition turns into multiplication). When you want to constrain a value to be positive, parameterizing it with exp is usually a good option.
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