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I think the judge would know it when they see it and laugh them out of the court room.
I think the judge would know it when they see it and laugh them out of the court room.
This headline sounded familiar. The article’s from 8 months ago, folks.
I think you mean “$EDITOR”. Gotta have that variable expansion.
Am I being dense? I don’t get it.
Unix -> Linux -> Ferrix?
I don’t understand any of these analogies at all
When I can’t wrap my head around a technical document or journal article, I print it. My brain craves paper. I’m a software engineer, so believe me that I would be live inside the computer if I could.
I learned MIPS as an undergrad. Pretty neat little RISC architecture.
Get out of my office
I mean technically I could write an interpreter that assigns semantics to HTML constructs.
Aha, I didn’t realize compromising availability was sufficient for the CVE definition of security vulnerability. Projects I’ve worked on have typically excluded availability, though that may not be the norm.
And I see your point about some exploits being highly asymmetric in the attacker’s favor, compared to classic [D]DoS.
The chances of the coin flip yielding heads are roughly 50%, if coins don’t not exist.
Maybe I’m misunderstanding you, but DoS is exactly the same thing as “denial of service”.
My point is that memory leaks can only degrade availability; they are categorically distinct from security vulnerabilities.
I had to look it up to check my memory. Yup! https://about.gitlab.com/blog/2015/06/05/how-gitlab-uses-unicorn-and-unicorn-worker-killer/
I don’t think memory leaks could ever amount to a security vulnerability, but it just feels yucky. I guess I shouldn’t cast stones, I write C++ at work.
Git kinda has it? Have you seen git notes? https://git-scm.com/docs/git-notes
I used to host a Gitlab instance at work. It was dog slow so I started digging into it and discovered they had a serious memory leak in some of their “unicorns,” aka Ruby tasks. Instead of fixing the source of the leak they tacked on a “unicorn killer” that periodically killed tasks. The tasks were supposed to be atomic anyway, so this is technically fine (and maybe a good thing in the long run for correctness a la Netflix’s Chaos Monkey) but I found myself kind of disgusted by the solution. I dropped it and went for a much sparser Git repo web server.
Or maybe behind a keyed lock in the office? Not a keypad, a physical key.
In this case, the models are given part of the text from the training data and asked to predict the next word. This appears to work decently well on the pre-2023 internet as it brought us ChatGPT and friends.
This paper is claiming that when you train LLMs on output from other LLMs, it produces garbage. The problem is that the evaluation of the quality of the guess is based on the training data, not some external, intelligent judge.
I loved my 1600x1200 Viewsonic, used it till 2010 or so. The flicker wasn’t ideal, but man the colors were so much more vibrant than shitty LCD screens of the 2000s were capable of. These days, I think Apple’s fancy LCDs with HDR win on all fronts, but it took a while to get here.
The darned neural implant generation doesn’t even know how to doomscroll with their fingers. Kids these days smh no cap.