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(LLM) A language model built for the public good

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  • Honestly they are pretty good for research too. You can’t imagine the amount of obscure shit that my ChatGPT has surfaced when I bounce ideas on it. But yea it’s terrible in finished products, I think everyone knows that and in a year or two if they don’t improve I expect we will be back to shoving it behind the scenes as had been done before ChatGPT. It’s for the best.

    That's not research. That's simply surfacing tidbits it found on the net the happen to be true

    .I've asked many questions of many llms in my chosen areas of interest and modest expertise , seeking more than basic knowledge( which it often surprisingly lacks ) it always has at least one error. Often so subtle it goes on noticed until it's too late.

  • Gigantic hater of all things LLM or "AI" here.

    The only genuine contribution I can think of that LLMs have made to society is their translation capabilities. So even I can see how a fully open source model with "multilingual fluency in over 1,000 languages" could be potentially useful.

    And even if it is all a scam, if this prevents people from sending money to China or the US as they are falling for the scam, I guess that's also a good thing.

    Could I find something to hate about it? Oh yeah, most certainly! 🙂

    i hear there are cool advances in medicine, engineering and such. i imagine techbros have an exponentially bigger budget, though.

  • I'm sure the community will find something to hate about this as well, since this isn't an article about an LLM failing at something.

    According to the article, they've even addressed my environmental concerns. Since it's created by universities, I don't think we'll even have this shoved down our throats all the time.

    I doubt whether it will be more useful than any other general LLM so far but hate it? Nah.

  • i hear there are cool advances in medicine, engineering and such. i imagine techbros have an exponentially bigger budget, though.

    Usually when I see this it's using other machine learning approaches than LLM, and the researchers behind it are usually very careful not to use the term AI, as they are fully aware that this is not what they are doing.

    There's huge potential in machine learning, but LLMs are very little more than bullshit generators, and generative AI is theft producing soulless garbage. LLMs are widely employed because they look impressive, but for anything that requires substance machine learning methods that have been around for years tend to perform better.

    If you can identify cancer in x-rays using machine learning that's awesome, but that's very seperate from the AI hype machine that is currently running wild.

  • Usually when I see this it's using other machine learning approaches than LLM, and the researchers behind it are usually very careful not to use the term AI, as they are fully aware that this is not what they are doing.

    There's huge potential in machine learning, but LLMs are very little more than bullshit generators, and generative AI is theft producing soulless garbage. LLMs are widely employed because they look impressive, but for anything that requires substance machine learning methods that have been around for years tend to perform better.

    If you can identify cancer in x-rays using machine learning that's awesome, but that's very seperate from the AI hype machine that is currently running wild.

    to be fair, the LLMs they use for chatbots and stolen pics generator are not AI either.

  • That's not research. That's simply surfacing tidbits it found on the net the happen to be true

    .I've asked many questions of many llms in my chosen areas of interest and modest expertise , seeking more than basic knowledge( which it often surprisingly lacks ) it always has at least one error. Often so subtle it goes on noticed until it's too late.

    So what you’re saying is that it’s good for research, because you can’t research what you don’t know about.

    It’s good for giving starting points which is exactly what I meant.

    Next time I’ll write a dissertation with hyper specifics because it seems it’s necessary every time LLMs are involved as there’s always someone looking to nitpick the statements.

  • to be fair, the LLMs they use for chatbots and stolen pics generator are not AI either.

    Yeah, I just find it to be a great rule of thumb. Those who understand what they are doing will be aware that they are not dealing with AI, those who jump to label it as such are usually bullshit artists.

  • So what you’re saying is that it’s good for research, because you can’t research what you don’t know about.

    It’s good for giving starting points which is exactly what I meant.

    Next time I’ll write a dissertation with hyper specifics because it seems it’s necessary every time LLMs are involved as there’s always someone looking to nitpick the statements.

    No you rude fuck.

    If i ask a simple question about a subject, let's say foraging as a I do that a lot. And it's wrong, it's friggin wrong.

    I'll ask about a specific plant. Full disclosure this is one of my things. 40 years at it. Ok? No big stretch to think I know a thing or two.

    So I ask about let's say, Japanese barberry. An invasive plant that is hated by many and rightly so at times. The question is , is it edible?

    The answer given was no. The truth is the opposite. It is edible. Hell there's recipes online for barberry jam. Now don't go just eating them though. Smart to test one or two leaves to see if an individual is allergic. That's not part of the answer, that's foraging 101. But I digress.
    The a.i was wrong and then argued about it until I pulled up all of the evidence. The a.i then admitted it was wrong, but who cares? It's not alive. Winning an argument with a.i is like beating oneself at poker.

    Another example
    I'll ask about intervals in music ( guitar teacher as my main profession now, as my passion for 48 years). It got the major scale intervals wrong.

    I asked ask if yogurt can replace eggs as a binding agent to one of them (can't remember which, apologies ) and it said no. That's a friggin home ec tip that's been around for at least a century

    People who give dissertations don't brag about it. Especially to make a point in a thread. It only makes one seem like a person who isn't confident in what they're saying so they drop a line that they feel will impress others. It doesn't

    Others experience is as important, vital and real as yours, regarding the answers given by a.i , but you'll brush it off because you feel that some how you have more insight than others. You dont. You just have more time to pour through a.i's mistakes to massage it to getting something close to what you want. That shows an abundance of time available. Which means you aren't doing the things I'm talking about.

    Or it means this is something you do for your job and it works for those specific needs . Which is fine, but your needs are not the world's. My need have been poorly met by that tool you spouse. Much like a rake won't help a guy digging a hole, a.i is the wrong tool for most jobs.

    Which means your opinion of my evaluation of a. I results is skewed because you don't value others experience, no matter how intelligent you are. And that is a sign of ignorance.

    I wish you a good day

  • No you rude fuck.

    If i ask a simple question about a subject, let's say foraging as a I do that a lot. And it's wrong, it's friggin wrong.

    I'll ask about a specific plant. Full disclosure this is one of my things. 40 years at it. Ok? No big stretch to think I know a thing or two.

    So I ask about let's say, Japanese barberry. An invasive plant that is hated by many and rightly so at times. The question is , is it edible?

    The answer given was no. The truth is the opposite. It is edible. Hell there's recipes online for barberry jam. Now don't go just eating them though. Smart to test one or two leaves to see if an individual is allergic. That's not part of the answer, that's foraging 101. But I digress.
    The a.i was wrong and then argued about it until I pulled up all of the evidence. The a.i then admitted it was wrong, but who cares? It's not alive. Winning an argument with a.i is like beating oneself at poker.

    Another example
    I'll ask about intervals in music ( guitar teacher as my main profession now, as my passion for 48 years). It got the major scale intervals wrong.

    I asked ask if yogurt can replace eggs as a binding agent to one of them (can't remember which, apologies ) and it said no. That's a friggin home ec tip that's been around for at least a century

    People who give dissertations don't brag about it. Especially to make a point in a thread. It only makes one seem like a person who isn't confident in what they're saying so they drop a line that they feel will impress others. It doesn't

    Others experience is as important, vital and real as yours, regarding the answers given by a.i , but you'll brush it off because you feel that some how you have more insight than others. You dont. You just have more time to pour through a.i's mistakes to massage it to getting something close to what you want. That shows an abundance of time available. Which means you aren't doing the things I'm talking about.

    Or it means this is something you do for your job and it works for those specific needs . Which is fine, but your needs are not the world's. My need have been poorly met by that tool you spouse. Much like a rake won't help a guy digging a hole, a.i is the wrong tool for most jobs.

    Which means your opinion of my evaluation of a. I results is skewed because you don't value others experience, no matter how intelligent you are. And that is a sign of ignorance.

    I wish you a good day

    I use perplexity as my main go to if i want to use an LLM, since they have access to a wide scale of models. It was correct in the cases you mentioned. It's a tool focused on correctness of information and I've had it hallucinate a lot less than other tools.

    Give it a shot if you're looking for one that focuses on correctness of information. It searches the Web and then feeds the results into the model you choose.

    You can also tell it to only use academic papers, social discussions, or SEC filings.

  • Usually when I see this it's using other machine learning approaches than LLM, and the researchers behind it are usually very careful not to use the term AI, as they are fully aware that this is not what they are doing.

    There's huge potential in machine learning, but LLMs are very little more than bullshit generators, and generative AI is theft producing soulless garbage. LLMs are widely employed because they look impressive, but for anything that requires substance machine learning methods that have been around for years tend to perform better.

    If you can identify cancer in x-rays using machine learning that's awesome, but that's very seperate from the AI hype machine that is currently running wild.

    Machine learning is a subset of the AI branch of computer science. I agree that the pop culture definition of AI is different than the computer science one, but the computer science one is still valid.

  • Machine learning is a subset of the AI branch of computer science. I agree that the pop culture definition of AI is different than the computer science one, but the computer science one is still valid.

    Large language models and "generative AI" such as Stable Diffusion, Midjourney, and DALL-E are all just machine learning models. We do not currently have a real "AI branch" of computer science, we have a branch of machine learning that poses as AI.

    No matter how good a machine gets at recognizing and predicting patterns, it will not constitute AI, as intelligence is different from pattern recognition and prediction. Even if LLMs can sometimes appear to be reasoning, they importantly are not.

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    But but we need to power our virtual idiot with more energy than entire countries use :((
  • Misogyny and Violent Extremism: Can Big Tech Fix the Glitch?

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    It is interesting that you are not answering my point... Good work
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    meanwhile i set a wait and save so i have time to finish getting ready and uber tells me it's already arrived.
  • Founder of 23andMe buys back company out of bankruptcy auction

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    Come on up to Canada, we still got that garlic bomb. I can still taste the one from last week
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    Yes I did, on page 243: It was employed in the Philosophical Transactions by the Dutch astronomer N. Cruquius; ÷ is found in Hübsch and Crusius. It was used very frequently as the symbol for subtraction and ``minus´´ in the Maandelykse Mathematische Liefbebbery, Purmerende (1754-69)
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    Got it, at that point (extremely high voltage) you'd need suppression at the panel. Which I would hope people have inline, but not expect like an LVD.
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    Eh, I kinda like the ephemeral nature of most tiktoks, having things go viral within a group of like 10,000 people, to the extent that if you're tangentially connected to the group, you and everyone you know has seen it, but nobody outside that group ever sees and it vanishes into the ether like a month later makes it a little more personal.
  • Microsoft's AI Secretly Copying All Your Private Messages

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    Forgive me for not explaining better. Here are the terms potentially needing explanation. Provisioning in this case is initial system setup, the kind of stuff you would do manually after a fresh install, but usually implies a regimented and repeatable process. Virtual Machine (VM) snapshots are like a save state in a game, and are often used to reset a virtual machine to a particular known-working condition. Preboot Execution Environment (PXE, aka ‘network boot’) is a network adapter feature that lets you boot a physical machine from a hosted network image rather than the usual installation on locally attached storage. It’s probably tucked away in your BIOS settings, but many computers have the feature since it’s a common requirement in commercial deployments. As with the VM snapshot described above, a PXE image is typically a known-working state that resets on each boot. Non-virtualized means not using hardware virtualization, and I meant specifically not running inside a virtual machine. Local-only means without a network or just not booting from a network-hosted image. Telemetry refers to data collecting functionality. Most software has it. Windows has a lot. Telemetry isn’t necessarily bad since it can, for example, help reveal and resolve bugs and usability problems, but it is easily (and has often been) abused by data-hungry corporations like MS, so disabling it is an advisable precaution. MS = Microsoft OSS = Open Source Software Group policies are administrative settings in Windows that control standards (for stuff like security, power management, licensing, file system and settings access, etc.) for user groups on a machine or network. Most users stick with the defaults but you can edit these yourself for a greater degree of control. Docker lets you run software inside “containers” to isolate them from the rest of the environment, exposing and/or virtualizing just the resources they need to run, and Compose is a related tool for defining one or more of these containers, how they interact, etc. To my knowledge there is no one-to-one equivalent for Windows. Obviously, many of these concepts relate to IT work, as are the use-cases I had in mind, but the software is simple enough for the average user if you just pick one of the premade playbooks. (The Atlas playbook is popular among gamers, for example.) Edit: added explanations for docker and telemetry