I just cannot imagine any task you can do in excel that isn’t easier to do with Python/Pandas. The simplest manipulations of an excel sheet pretty much require you to chain an ungodly list of arcane commands that are completely unreadable, and god forbid you need to work with data from several workbooks at the same time…
You are neglecting the cost-benefit of temporarily jumping to the wrong conclusion while waiting for more conclusive evidence though. Not doing anything because evidence that this is bad is too thin, and being wrong, can have severe long-term consequences. Restricting tiktok and later finding out that it has no detrimental effects has essentially zero negative consequences. We have a word for this principle in my native language - that if you are in doubt about whether something can have severe negative consequences, you are cautious about it until you can conclude with relative certainty that it is safe, rather than the other way around, which would be what you are suggesting: Treating something as safe until you have conclusive evidence that it is not, at which point a lot of damage may already be done.
So what you’re saying is: We have a small sample of unreliable evidence that this thing may be absolutely detrimental to the developing brain. Thus, we should assume it’s fine until we have more reliable evidence. Did I get that right?
I’ve found chatgpt reasonably good for one thing: Generating regex-patterns. I don’t know regex for shit, but if I ask for a pattern described with words, I get a working pattern 9/10 times. It’s also a very easy use-case to double check.
I was thinking something similar: If you have the computer write in a formal language, designed in such a way that it is impossible to make an incorrect statement, I guess it could be possible to get somewhere with this
We tried the “trade your skills for something you need”. In every surviving society it eventually lead to the development of a currency (not hard to see why), which requires/leads to regulation, which requires enforcement, aaaand you’re back at a modern society. I’m all for more regulation to reduce economic and social differences in society, but the people that are talking about abolishing governments and currencies need to pick up a history book and follow their ideas to their natural conclusion.
“Controlling speech” is a hallmark of authoritarian governments, be they far-left or far-right, there are plenty of historical examples of both.
Looking at a half circle and guessing that the “missing part” is a full circle is as much of a blind guess as you can get. You have exactly zero evidence that there is another half circle present. The missing part could be anything, from nothing to any shape that incorporates a half circle. And you would be guessing without any evidence whatsoever as to which of those things it is. That’s blind guessing.
Extrapolating into regions without prior data with a non-predictive model is blind guessing. If it wasn’t, the model would be predictive, which generative AI is not, is not intended to be, and has not been claimed to be.
I 100 % agree on your primary point. I still want to point out that a detail in a 4k picture that takes up a few pixels will likely be invisible to the naked eye unless you zoom. “Digital zoom” without interpolation is literally just that: Enlarging the picture so that you can see details that take up too few pixels for you to discern them clearly at normal scaling.
No computer algorithm can accurately reconstruct data that was never there in the first place.
What you are showing is (presumably) a modified visualisation of existing data. That is: given a photo which known lighting and lens distortion, we can use math to display the data (lighting, lens distortion, and input registered by the camera) in a plethora of different ways. You can invert all the colours if you like. It’s still the same underlying data. Modifying how strongly certain hues are shown, or correcting for known distortion are just techniques to visualise the data in a clearer way.
“Generative AI” is essentially just non-predictive extrapolation based on some data set, which is a completely different ball game, as you’re essentially making a blind guess at what could be there, based on an existing data set.
Hehe, I absolutely agree… for reference, High Sierra is v10.13, released in 2017. I’m now running v13, released 2022. They moved from v10.15 to v11 in 2020, when the arm chips were released.
My old MacBook could probably run 10.15 just fine, but I don’t have any good reason to update it, as it’s only purpose now is to compile distributables for other old machines.
Also: I really dislike that they’ve been pushing non-backwards compatible major releases so hard since 2020. I’m not updating my OS because I can’t be bothered to break shit, it shouldn’t be like that…
I believe brew dropped support for a high Sierra just a couple years back (2022 I think) but as of now my 2012 MacBook Pro is still chugging along whenever I need to compile or test something for x86 and can’t be bothered to cross-compile from my new MacBook :)
Theres plenty of cases where I would like to do some large calculation that can potentially give a NaN at many intermediate steps. I prefer to check for the NaN at the end of the calculation, rather than have a bunch of checks in every intermediate step.
How I handle the failed calculation is rarely dependent on which intermediate step gave a NaN.
This feels like people want to take away a tool that makes development in the engineering world a whole lot easier because “null bad”, or because they can’t see the use of multiplying 1e27 with 1e-30.
Honest question from someone using C++, though not for systems- or embedded stuff, just for object oriented models that gotta go fast: Why is C++ not in the same ballpark, and not an alternative?
Exactly! Then you agree that because chatgpt can be coerced into spitting out raw, unmodified data, distributing it is a violation of copyright. Glad we’re on the same page.
You should look up the term “rhetorical question” by the way.
If I scrape a bunch of data, put it in a database, and then make that database queryable only using obscure, arcane prompts: Is that a derivative work permitted under fair use?
Because if you can get chatgpt to spit out raw training data with the right prompt, it can essentially be used as a database of copyrighted stuff that is very difficult to query.
First of all no: Training a model and selling the model is demonstrably equivalent to re-distributing the raw data.
Secondly: What about all the copyleft work in there? That work is specifically licensed such that nobody can use the work to create a non-free derivative, which is exactly what openAI has done.
Since you seem to know a lot about this: I would think that at some point the purely physical size of a device is prohibitive of using shared cache, just because the distance from a cpu to the cache can’t be too big. Do you know when this comes into play, if it does? Also, having written some multithreaded computational software, I’ve found that there’s typically (for the stuff I do) a limit to how many cores I can efficiently make use of, before the overhead of opening and closing threads eats the advantage of sharing the work between cores. What kind of “everyday” server stuff is efficiently making use of ≈300 cores? It’s clearly some set of tasks that can be done independently of one another, but do you know more specifically what kind of things people need this many cores on a server for?
Oh, I definitely get that the major appeal of excel is a close to non-existent barrier to entry. I mean, an elementary school kid can learn the basics(1) of using excel within a day. And yes, there are definitely programs out there that have excel as their only interface :/ I was really referring to the case where you have the option to do something “from scratch”, i.e. not relying on previously developed programs in the excel sheet.
(1) I’m aware that you can do complex stuff in excel, the point is that the barrier to entry is ridiculously low, which is a compliment.