• 14 Posts
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Cake day: March 22nd, 2024

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  • There may be thought in a sense.

    A analogy might be a static biological “brain” custom grown to predict a list of possible next words in a block of text. It’s thinking, sorta. Maybe it could acknowledge itself in a mirror. That doesn’t mean it’s self aware, though: It’s an unchanging organ.

    And if one wants to go down the rabbit hole of “well there are different types of sentience, lines blur,” yada yada, with the end point of that being to treat things like they are…

    All ML models are static tools.

    For now.








  • Machine learning has been a field for years, as others said, yeah, but Wikipedia would be a better expansion of the topic. In a nutshell, it’s largely about predicting outputs based on trained input examples.

    It doesn’t have to be text. For example, astronmers use it to find certain kinds of objects in raw data feeds. Object recognition (identifying things in pictures with little bounding boxes) is an old art at this point. Series prediction models are a thing, languagetool uses a tiny model to detect commonly confused words for grammar checking. And yes, image hashing is another, though not entirely machine learning based. IDK what Tineye does in their backend, but there are some more “oldschool” approaches using more traditional programming techniques, generating signatures for images that can be easily compared in a huge database.

    You’ve probably run ML models in photo editors, your TV, your phone (voice recognition), desktop video players or something else without even knowing it. They’re tools.

    Seperately, image similarity metrics (like lpips or SSIM) that measure the difference between two images as a number (where, say, 1 would be a perfect match and 0 totally unrelated) are common components in machine learning pipelines. These are not usually machine learning based, barring a few execptions like VMAF (which Netflix developed for video).

    Text embedding models do the same with text. They are ML models.

    LLMs (aka models designed to predict the next ‘word’ in a block of text, one at a time, as we know them) in particular have an interesting history, going back to (If I even remember the name correctly) BERT in Google’s labs. There were also tiny LLMS people did run on personal GPUs before ChatGPT was ever a thing, like the infamous Pygmalion 6B roleplaying bot, a finetune of GPT-J 6B. They were primitive and dumb, but it felt like witchcraft back then (before AI Bros marketers poisoned the well).








  • This is so stupid.

    To me, “AI” in a car would be like highlighting pedestrians in a HUD, or alerting you if an unknown person messes with the car, or maybe adjusting mood lighting based on context. Or safety features.

    …Not a chatbot.

    I’m more “pro” (locally hostable, task specific) machine learning than like 99% of Lemmy, but I find the corporate obsession with cloud instruct textbots bizarre. It would be like every food corp living and breathing succulents. Cacti are neat, but they don’t need to be strapped to every chip bag, every takeout, every pack of forks.


  • A lot, but less than you’d think! Basically a RTX 3090/threadripper system with a lot of RAM (192GB?)

    With this framework, specifically: https://github.com/ikawrakow/ik_llama.cpp?tab=readme-ov-file

    The “dense” part of the model can stay on the GPU while the experts can be offloaded to the CPU, and the whole thing can be quantized to ~3 bits average, instead of 8 bits like the full model.


    That’s just a hack for personal use, though. The intended way to run it is on a couple of H100 boxes, and to serve it to many, many, many users at once. LLMs run more efficiently when they serve in parallel. Eg generating tokens for 4 users isn’t much slower than generating them for 2, and Deepseek explicitly architected it to be really fast at scale. It is “lightweight” in a sense.


    …But if you have a “sane” system, it’s indeed a bit large. The best I can run on my 24GB vram system are 32B - 49B dense models (like Qwen 3 or nemotron), or 70B mixture of experts (like the new Hunyuan 70B).


  • DeepSeek, now that is a filtered LLM.

    The web version has a strict filter that cuts it off. Not sure about API access, but raw Deepseek 671B is actually pretty open. Especially with the right prompting.

    There are also finetunes that specifically remove China-specific refusals. Note that Microsoft actually added saftey training to “improve its risk profile”:

    https://huggingface.co/microsoft/MAI-DS-R1

    https://huggingface.co/perplexity-ai/r1-1776

    That’s the virtue of being an open weights LLM. Over filtering is not a problem, one can tweak it to do whatever you want.


    Grok losing the guardrails means it will be distilled internet speech deprived of decency and empathy.

    Instruct LLMs aren’t trained on raw data.

    It wouldn’t be talking like this if it was just trained on randomized, augmented conversations, or even mostly Twitter data. They cherry picked “anti woke” data to placate Musk real quick, and the result effectively drove the model crazy. It has all the signatures of a bad finetune: specific overused phrases, common obsessions, going off-topic, and so on.


    …Not that I don’t agree with you in principle. Twitter is a terrible source for data, heh.