Surprised pikachu face
Probably has to be renamed to “ClosedAI” then.
How is this going to work while OpenAI currently burns through an absolute ocean of cash to keep improving its services? Alongside this, a good software engineer or applied scientist can make close to $1m a year. While I do think professionals should earn what their value is to an employer, OpenAI still loses a ton of money.
As someone that works in AI, I think most of us know it’s full of people trying to make a quick buck while investors will stupidly throw money at it. OpenAI is ultimately the figurehead of this market though, because at least the big companies can prop their AI offerings with the money they make from shopping, cloud, ads, etc. The second OpenAI looks weak and needs money, the vultures will slice off a piece and we’ll see the AI market reduce to a wimper - just enough for tech to focus on the next grift.
About the only AI company currently alive that I’m sure will survive is CivitAI. Huggingface probably, too. Both are, in the end, in the datacenter business. Huggingface has exposure to VC BS in their client base, they might be in trouble if a significant number suddenly go belly-up but if they have any sense they’ll simply not overextend. And, well, they, too, can switch to cat pictures.
Yeah some of my team members use hf and it really does represent a convenience (basically a GitHub for models), but I’m sure to be clear we can’t rely on them alone. I don’t trust any company to exist or not be bought out and enshittified in 3 years.
As long as the shareholders are happy.
I’m partial to:
Much open, very organic, very demure, so mindful.
How exactly does one “outgrow” “AGI for the benefit of all humanity?
OpenAI Charter https://openai.com/charter
Our primary fiduciary duty is to humanity. We anticipate needing to marshal substantial resources to fulfill our mission, but will always diligently act to minimize conflicts of interest among our employees and stakeholders that could compromise broad benefit.
Great read
Almost like Sam Altman is just another run of the mill tech bro scam guy.
I don’t think he is a “tech bro scam guy”, i think he is worse like he is smart and has a documented track record of lying. Unlike other tech bros, he actually knows the capability /limits of his products and he still lies and makes it out to be something it’s not.
I hope OpenAI is going to serve as a radicalising example to all the engineers, who fell for the “ethical guy/company” rhetoric, that the minority-controlled corporate structures they’re used to cannot withstand the push for profit. I hope this will make more of them choose majority-controlled structures for their startups and demand unions in existing corpos.
I mean, I was already radicalized in that respect, but it’s definitely reaffirming that radicalization.
But also: I fuckin told you so. This progression was so blindingly obvious from the get-go.
OpenAI on that enshittification speedrun any% no-glitch!
Honestly though, they’re skipping right past the “be good to users to get them to lock in” step. They can’t even use the platform capitalism playbook because it costs too much to run AI platforms. Shit is egregiously expensive and doesn’t deliver sufficient return to justify the cost. At this point I’m ~80% certain that AI is going to be a dead tech fad by the end of this decade because the economics just don’t work now that the free money era has ended.
It will fall through much faster than that. I’m thinking two years, tops.
If your username is any prediction then it will be consumed by Lemmy… 🎶downtown🎶
reminder, there are localy ran LLMs. Right now is a vital time for open source to fight against closed source in the AI arms race.
At the same time, the trouble with local LLMs is that they’re very resource heavy. Your average household computer isn’t going to be able to run one with much usability or speed.
Phi 3 can run on pretty low specs (requires 4gb RAM) and has relatively good output
Which, you know, is fine. Maybe if people had an idea of how much power is required to run them, they would think twice before using a gigawatt to output a poem about farts, and perhaps even wonder how OpenAI can offer that for free. Btw, a 7b model should run ok on any PC with at least 16GB of RAM and a modern processor/GPU.
Okay but what problem does that solve? Is the solution setting up our own spambots to fill forums with arguments counter to their bullshit spambots? I don’t see how an LLM improves literally anything ever in any circumstance.
It definitely improves my experience coding in unfamiliar languages. So there’s your counter example.
improves my experience coding in unfamiliar languages
Alan Perlis said “A programming language that doesn’t change the way you think is not worth learning.”
So… if you code in another language without actually “getting it”, solely having a usable result, what is actually the point of changing languages?
I have a job to do. And I understand the other language conceptually, I am just rusty on the syntax.
Also the chat feature is invaluable. I can highlight a piece of code and ask what it does, and copilot explains it.
Exactly. I see AI as a tool to automate the boring parts, if you try to automate the hard parts, you’re going to have a bad time.
Take the time to learn the tools you use thoroughly, and then you can turn to AI to make your use of those tools more efficient. If I’m learning woodworking, for example, I’m going to learn to use hand tools first before using power tools, but there’s no way I’m sticking to hand tools when producing a lot of things. Programming isn’t any different, I’ll learn the language and its idioms as deeply as I can, and only then will I turn to things like AI to spit out boilerplate to work from.
Mind explaining a bit your workflow at the moment?
I’m not sure how to succinctly do that.
When I learn a new language, I:
- go through whatever tutorial is provided by the language developers - for Rust, that’s The Rust Programming Language, for Go, it’s Tour of Go and Effective Go
- build something - for Go, this was a website, and for Rust it was a Tauri app (basically a website); it should be substantial enough to exercise the things I would normally do with the language, but not so big that I won’t finish
- read through substantial portions of the standard library - if this is minimal (e.g. in Rust), read through some high profile projects
- repeat 2 & 3 until I feel confident I understand the idioms of the language
I generally avoid setting up editor tooling until I’ve at least run through step 3, because things like code completion can distract from the learning process IMO.
Some books I’ve really enjoyed (i.e. where 1 doesn’t exist):
- The C Programming Language - by Brian Kernighan and Dennis Richie
- Programming in Lua - by Roberto Ierusalimschy
- Learn You a Haskell for Great Good - by Miran Lipovača (available free online)
But regardless of the form it takes, I appreciate a really thorough introduction to the language, followed by some experimentation, and then topped off with some solid, practical code examples. I generally allow myself about 2 weeks before expecting to write anything resembling production code.
These days, I feel confident in a dozen or so programming languages (I really like learning new languages), and I find that thoroughly learning each has made me a better programmer.
Thanks for that, was quite interesting and I agree that completion too early (even… in general) can be distracting.
I did mean about AI though, how you manage to integrate it in your workflow to “automate the boring parts” as I’m curious which parts are “boring” for you and which tools you actual use, and how, to solve the problem. How in particular you are able to estimate if it can be automated with AI, how long it might take, how often you are correct about that bet, how you store and possibly share past attempts to automate, etc.
From all the studies available, LLMs increased the rate at which low skilled workers complete tasks. They also lower accuracy, so expect some of the tasks to be done incorrectly.
If your metric for “improves” is being a better low skill drone forever then yes I’m sure it’s helping you. Here is a novel idea, maybe learn the language from a reliable source instead of taking the word of a bullshit generator at face value?
Here’s an idea, maybe start with curiosity about how someone is getting value out of it? It’s possible you don’t know everything about other people’s experiences.
It’s something being shoved down our throats every second of every day and I’ve seen enough to know I don’t like it. Curiosity was satiated a long ass time ago. It’s just a bigger power draw than Cryptocurrency but somehow magically even less value.
You seem unnecessarily hostile about this. If you don’t like LLM just move on.
This is exactly why this sub about technology is better off without business news. You’re just reacting to something you hate and directing that at others.
But answer the question maybe
Also, my “hate” was very clearly directed towards LLMs and not a “person”.
FWIW I did try a lot (LLMs, code, generative AI for images, 3D models) in a lot of ways (CLI, Web based, chat bot) both locally and using APIs.
I don’t use any on a daily basis. I find it exciting that we can theoretically do a lot “more” automatically but… so far the results have not been worth the efforts. Sadly some of the best use cases are exactly what you highlighted, i.e low effort engagement for spam. Overall I find that either working with a professional (script writer, 3D modeler, dev, designer, etc) is a lot more rewarding but also more efficient which itself makes it cheaper.
For use cases where customization helps while quality does matter much due to scale, i.e spam, then LLMs and related tools are amazing.
PS: I’d love to hear the opinion of a spammer actually, maybe they also think it’s not that efficient either.
I have personally found generative-text LLMs quite good for creating titles. As an example, I have a few hundred tweets that I’m trying to put into a file, and I’ll use an LLM to create a human-readable name for them. It’s much better than a lot of the other summarisation mechanisms (like BERT) I’ve tried with it, but it’s still not perfect, because the model tends to output the same thing in slightly different words each time, so repeat runs will often result in the same thing with a different title.
But, that is also a fairly limited use case.
Another good resource to help people find models https://llm.extractum.io
Or just straight up install https://ollama.com
I like Ollama, and recommend it to tinker, but I admit this “LLM Explorer” is quite neat thanks to sections like “LLMs Fit 16GB VRAM”
Ollama just works but it doesn’t help to pick which model best fits your needs.
pick which model best fits your needs.
What is the need I have to put the effort in to install all this locally. Websites win in terms of convenience.
I don’t think I understand your point, are you saying there is no benefit in running locally and that Websites or APIs are more convenient?
I want to work on my stuff in peace and in private without worrying about a company grabbing my stuff and using it for themselves and to give/sell it to other outfits, including the government. “If you have nothing to hide…” is bullshit and needs to die.
Them investors got to get paid!
Wild for a company that’s never made a profit
These companies do not make profit in paper but have already made millions for others.
It’s all smoke and mirrors
Oh it’s made plenty for Nvidia.
OpenAI: It’s not fair to charge us to use copywriten works.
Also OpenAI: Also you have to pay us for using them.
copyrighted*
The fact that Silicon Valley interests effortlessly shrugged off the non-profit board’s attempt to hit the kill switch last year, and now are preparing to take the company commercial despite the deliberate design otherwise, becomes much more interesting when you consider the theory that corporations are a form of artificial superintelligence.
If the AI idealists can’t stand up to basic forces of capitalism, how do they expect to control an actually dangerous AGI?
I kinda liked the text you linked. Here’s a quote.
There are also structural changes that can be made to corporations to realign their values system with human welfare. Corporate charters can be amended to optimize for a triple bottom line of social, environmental, and financial outcomes (the so-called “triple Ps” of people, planet, and profit.)
This reminds me of what we are trying to do where I live. The hard thing is this requires a lot of work and it doesn’t just go against the corporate agenda; it goes against the normal lifestyle most everyone around us lives. It has made me want to quit sometimes.
But then again, true life is in true living among real people and real things, not in daydreaming of better days.
You give them far too much credit to assume this specific company will ever achieve anything even close to AGI.
We don’t know what they aren’t showing us. GPT was only one strand of research
If they had something better, don’t you think they’d be putting it out front and center? This is akin to all those conspiracy theorists claiming they have proof to back their claims but they just can’t show it to you right now but it’s definitely coming at some indeterminate time in the future.
If the AI idealists can’t stand up to basic forces of capitalism, how do they expect to control an actually dangerous AGI?
My guess is they don’t expect to. I guess that that is one of the reasons they seem to not care about out of control climate change; burn it all down before it all literally burns down.
Yeah, the people leading the “AGI will save us” are the same as super church pastors.
They don’t believe it, they just want their bank account limitless before they go into oblivion.
Just want to point out that it absolutely is possible to train an AI that will keep track of its sources for inspiration and can attribute those when it makes a response.
Meaning creators could be compensated for their parts of AI generated stuff, if anyone wanted to.
Doesn’t Phind do this already? I haven’t used it much but I remember it showing its sources for answers of code-related stuff
I use Phind solving computer problems. It does cite the sources it uses. At least for distro and general Linux issues. So far, it’s been a very good resource when I’ve needed it.
Other than citing the entire training data set, how would this be possible?
The entire training set isn’t used in each permutation. Your keywords are building the samples based on metadata tags tied back to the original images.
If you ask for “Iron Man in a cowboy hat”, the toolset will reach for some catalog of Iron Man images and some catalog of cowboy hat images and some catalog of person-in-cowboy-hat images, when looking for a basis of comparison as it renders the image.
These would be the images attributed to the output.
Do you have a source for this? This sounds like fine-tuning a model, which doesn’t prevent data from the original training set from influencing the output. The method you described would only work if the AI is trained from scratch on only images of iron man and cowboy hats. And I don’t think that’s how any of these models work.
Face is looking increasingly compromised and distorted every photo.
