The point you are missing is that yes, asking an LLM about these things is not at the level of advice from someone who knows their stuff. But if you dont know what you are doing and dont know enough to even know what the right things to search for are then even partialy useful advice about the thing you are trying to do is a massive help.
Personally I think calling out smug pedantry is useful, but that’s just my opinion.
Thanks, that was really necessary and greatly added to the conversation.
If you read the article it explains why the fact that it is an ordinary failure is a bad thing. Ordinary failures (like some one not installing some bolts) are not supposed to happen in high reliability systems like passenger aircraft. Failures tend to come through “extraordinary” failures where multiple factors line up in an over looked way in order to create an unexpected failure mode.
A 10 year old could tell you not installing safety bolts where they are supposed to be would make things dangerous. The fact that that is how a potentially lethal failure happened is damming.
There’s good reason that hospitals have their own backup emergency generators. A blackout absolutely would kill people.
Microsoft has Windows Defender, its in-house alternative to CrowdStrike, but because of the 2009 agreement made to avoid a European competition investigation, had allowed multiple security providers to install software at the kernel level.
Its all the EU’s fault for having the temerity to think users should be able to control their own hardware instead of us!
It doesn’t even have to be that much. Obviously these devices can do sound to text conversion, that’s how they interpret commands. That can convert hours of stored conversation to text, zip it up and send it as a few kilobytes along with the next network request it makes for a legit purpose.
Umatrix is great, you can configure it to automatically allow first party javascript, and if sites still dont work eneable bits until they do them lock those settings so the same bits will be enabled next time you’re on that site.
They have no fact repositories to rely on.
They do not possess the ability to know what is and is not correct.
They cannot check documentation or verify that a function or library or API endpoint exists, even though they will confidently create calls to them.
These three are all just the same as asking a person about them, they might know or might not but they cant right there and then check. Yes LLMs due to their nature cannot access a region marked “C# methods” or whatever, but large models do have some of that information embedded in them, if they didnt they wouldnt get correct answers anywhere near as often as they do, which for large models and common languages/frameworks is most of the time. This is before getting into retrieval augmented generation where they do have access to repositories of fact.
This is what I was complaining about in the original post I replied to, no-where have I or anyone else I’ve seen in this thread say you should rely on these models, just that they are a useful input. Yet relying on them and using them without verification is the position you and the other poster are arguing against.
They can be useful for exploration and learning, sure. But lots of people are literally just copy-pasting code from LLMs - They just do it via an “accept copilot suggestion” button instead of actual copy paste.
Sure, people use all sorts of tools badly, that’s a problem with the user not the tool (generally, I would accept poor tool design can be a factor).
I really dislike the statement of “LLMs dont know anything they are just statistical models” it’s such a thought terminating cliche that is either vacuous or wrong depending on which way you mean it. If you mean they have no information content that’s just factually wrong, clearly they do. If you mean they dont understand concepts in the same way as a person does, well yes but neither does google search and we have no problem using that as the start point of finding out about things. If you mean they can get answers wrong, its not like people are infallible either (who I assume you agree do know things).
That level of condescension (rethink your life because you are making use of a tool I dont like) really isnt productive. You seem to be thinking that using AI as a tool to help you program is equivalent to turning your brain off and just copy and pasting code snippets, it isnt. It can be a good way to explore a language or framework you aren’t familiar with (when combined with the documentation) or to figure out general potential methods of solving a problem.
Users don’t need to understand the system, all they need to know is you need to get someone to vouch for you, and if you vouch for bad people/bots you might lose your access.
https://en.m.wikipedia.org/wiki/Fei-Fei_Li
there you go, notice the bit under research where it says her imagenet project “has revolutionized the field of large-scale visual recognition.”
Side note, do you really think someone called li feifei and born in Beijing is motivated to create a tool to murder non-whites?
In a Ted Talk in April, Li further explained the field of research her startup will work on advancing, which involves algorithms capable of realistically extrapolating images and text into three-dimensional environments and acting on those predictions, using a concept known as “spatial intelligence.” This could bolster work in various fields such as robotics, augmented reality, virtual reality, and computer vision. If these capabilities continue to advance in the ambitious ways Li plans, it has the potential to transform industries like healthcare and manufacturing.
I mean that sounds a lot more interesting than 99% of the LLM work going on at the moment, and given that she lead the team that cracked the computer vision problem of recognising objects she has pedigree.
Why is everyone in this thread constantly putting meaningless @ mentions at the top of their replies?
To put a bit of context on those, 50GWh is a single medium sized power station running for 2 days. To create something that is being used around 10 million times a day all over the world.
At 10 million queries per day that puts the usage per query at 100-500 Wh, about the amount of energy used by leaving an old incandecent lightbulb on for an hour, or playing a demanding video game for about 20 minutes.
As another comparison, In the USA alone around 12,000 GWh of energy is spent in burning gasoline in vehicles every single day. So Americans driving 1% less for a single day would save more energy than creating GPT4 and the world using it for a year.
I was responding to your general statement that python is slow and so there is no point in making it faster, I agree that removing the GIL wont do much to improve the execution speed for programs making heavy use of numpy or things calling outside it.
That’s a bit suss too tbh. Did the C++ version use an existing library like Eigen too or did they implement everything from scratch?
It was written entirely from scratch which is kind of my point, a well writen python program can outperform a naive c implementation and is vastly simpler to create.
If you have the expertise and are willing to put in the effort you likely can squeze that extra bit of performance out by dropping to a lower level language, but for certain workloads you can get good performance out of python if you know what you are doing so calling it extremely slow and saying you have to move to another language if you care about performance is missleading.
Numpy is written in C.
Python is written in C too, what’s your point? I’ve seen this argument a few times and I find it bizarre that “easily able to incorporate highly optimised Fortran and C numerical routines” is somehow portrayed as a point against python.
Numpy is a defacto extension to the python standard that adds first class support for single type multi-dimensional arrays and functions for working on them. It is implemented in a mixture of python and c (about 60% python according to github) , interfaces with python’s c-api and links in specialist libraries for operations. You could write the same statement for parts of the python std-lib, is that also not python?
Its hard to not understate just how much simpler development is in numpy compared to c++, in this example here the new python version was less than 50 lines and was developed in an afternoon, the c++ version was closing in on 1000 lines over 6 files.
Nope, if you’re working on large arrays of data you can get significant speed ups using well optimised BLAS functions that are vectorised (numpy) which beats out simply written c++ operating on each array element in turn. There’s also Numba which uses LLVM to jit compile a subset of python to get compiled performance, though I didnt go to that in this case.
You could link the BLAS libraries to c++ but its significantly more work than just importing numpy from python.
As some one who runs both: no, not even close. Mac has more direct ports than Linux true, but proton vastly outweighs that. I have dozens of games that show up on steam on my mac as unplayable where as I dont have any that wont run under proton.
Five years ago you’d probably have been right, but Linux is far superior to OSX for gaming now.
(E: assuming you’re talking about an apple silicon macbook, IDK the status of proton on x86 macs maybe it works there?)