It's a social-media-level of fact checking, that is to say, you feel something is right but have no clue if it actually is. If you had a better source for a fact, you'd quote that source rather than the LLM.
Just do the research, and you don't have to qualify it. "GPT said that Don Knuth said..." Just verify that Don said it, and report the real fact! And if something turns out to be too difficult to fact check, that's still valuable information.
Yeah, the Steam HW survey shows that 16:9 resolutions form a majority (60%+) of their users with 1080p + 4K, so it makes sense as a default design choice for a company that only wants to target one ratio.
As a former user of 16:10, I feel your pain, though.
"There are no shortcuts, you have to put in the work." Spoken like someone who doesn't use an SRS system, then. They're actually extremely hard to use, because the focus is on feeding you the toughest possible version of every recalled card. Part of why people quit using them is because it's mentally exhausting!
Anecdotally, English/History/Communications professors are confirming cheaters with them because they find it easy to identify false information. The red flags are so obvious that the checker tools are just a formality: student papers now have fake URLs and fake citations. Students will boldly submit college papers which have paragraphs about nonexistent characters, or make false claims about what characters did in a story.
The e-mail correspondence goes like this: "Hello Professor, I'd like to meet to discuss my failing grade. I didn't know that using ChatGPT was bad, can I have some points back or rewrite my essay?"
I quite like the sociological definition for several reasons. Rather than trying to pin down precise criteria, you simple can ask people "Are you a mathematician? Are you a theoretical computer scientist?" And once someone has gone through that filter, everything that follows is opinion and also a historical snapshot of what people felt the field contained at the time. It provides future theoreticians and students a way to orient themselves on a map which is only partially drawn.
The definition of "theoretical physics" might have rapidly changed between 1900, 1920, 1940, and 1950--but certainly people who called themselves theoreticians remained mostly unchanged. Analyzing how everyone's definitions were changing gives a wealth of information about when and where breakthroughs were happening. 1919 and 1945 come to mind as such examples of when a theoretical field changed as a result of experiments [1][2].
Back to computing: Dijkstra told the story of attempting to put "Programmer" as his profession on his marriage certificate in 1957, and was rejected [3]. Clearly there are both pros and cons of using the sociological definition of a field. We all know programming existed before 1957, but the perception of it as a profession was so foreign that it wasn't allowed on an official document. It would've been impossible, apparently, where he lived to ponder about "What is programming?" if no one could BE a programmer. For that reason, we should probably be flexible and always be willing to discuss different definitions for every field so that we gain the benefits from multiple lines of reasoning.
I've seen math PhDs mess up addition and subtraction on a whiteboard, though.
Beating the 99th percentile human at any subject should not be difficult when the LLM training is equivalent to living thousands of lifetimes spent reading and nearly memorizing every book ever written on every university subject.
The fact that it only just barely beats humans feels hollow to me.
For those who've seen it, imagine if at end of Groundhog Day everyone in the crowd went, "Wow, he's slightly better than average at piano!"
Reminds me of this talk [0] led by CB Bailey, a top answerer on StackOverflow for the tag 'git' [1].
They create commits from scratch from the command line--manually creating each /.git/ file with shell commands and a text editor. Really fun talk. Would highly recommend it for people who were planning on learning about git internals at some point.
[0] "How does Git actually work? - CB Bailey & Andy Balaam [ACCU 2019]"
In the event that this is added to the standard library, I'm going to be really curious to see what a "hello world" project/example would look like.
I went so far as to find the commit where David Chase added for loopvar on Mar 6, 2023 (github: golang/go/commit/c20d959) to try to design my own hello world with x/tools/cmd/bisect, but I'm out of my depth.
The hash tree is a great visualization. I wouldn't have grasped the importance of the hash suffix until I saw the tree. Awesome stuff.
When simulating or modeling how long something will take, (0 hours, 1 hours, 2 hours...) you never use a normal distribution because of the potential for negative values in the left tail. You would rule that out by default. The title itself implies that the author has this misconception.
> exactly the opposite of what we should expect out of programmers.
I mean this only jokingly: Who gets to decide what programmers are supposed to be like? You?
As someone who tries on a new language every year or two, I love using languages with formatters. Worrying about how many spaces to indent with or other arbitrary things is the opposite of what this particular programmer likes to worry about--I want to jump right in and solve problems with a language, and let the formatter make the code look like it came from a textbook.
I was reading over a student's code and he couldn't tell me his own variable names, couldn't even find one when I referred to it conceptually ("Where is the variable that holds your cache?".... no answer at all, not even a guess. Just, "I don't know.")
Having used GPT all year myself, I quit using it to generate new code for me for the most part. Back to StackOverflow, books, and of course reading boring documentation/manuals. I'm not closed off to the idea, just that I'm very worried it could atrophy certain skills.