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Joined 3 years ago
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Cake day: July 18th, 2023

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  • I mean, it’s either American (OpenAI, Google, Anthropic) Billionaires or Chinese (Alibaba, Z. AI) Billionaires making the models.

    And even if you find a model not built by the billionaire class, you still require datacenters to train the models.

    And even then, the data required to build these models must take from millions of artists/writers/etc.

    I’m not saying you have to hate AI, but saying you can run a model locally doesn’t change who created it and how they created it.










  • My response was only in regard to the speed in which something like IoT devices can operate.

    You’re right that goals/directions of LLMs can be persisted anywhere, but a swarm or fleet of AI running on IoT devices will be severly hampered by compute (based on current models). Which was the main point I was making, in that there is only so much you can do to distribute their processing. Meaning you can parallelize the number of models, but can’t speed them up beyond a certain limit. This is based on the fact that some form of a layer and it’s weights must exist within the memory of a single device.

    The downside to this limitation is that it is temporary, eventually IoT devices will probably be powerful enough, or the worms could just attack phones/laptops and easily get the compute they need. I think the real risk of IoT devices is low security mixed with low “supervision” (no one would easily notice their IoT device is doing extra processing).




  • It reminds me of when Netflix was big and I told everyone they’d have commercials eventually. Sure you can still buy a plan without commercials, but I suspect that eventually, when money gets really tight for them, will go away.

    Same thing here. Right now passkeys are “open”, but who is to say my bank won’t require a Apple/Google managed passkey in the future. Hell, I can’t run the bank app without running a Apple/Google approved apk.



  • No its about traditional physics based models vs Machine Learning models (or AI). Google’s model isn’t using LLMs, but still uses deep learning models to predict the weather. Per the article:

    At that point, many of the physics-based computer models, which rely on mathematical equations describing how the atmosphere works to simulate future weather conditions, were vacillating between different projections but generally forecasting a weaker storm that would take a different track than the DeepMind and other AI models were showing.

    The original commentors point is valid in that we just call everything AI right now. While it’s not a terrible term for ML models, the general public largely associates AI with LLMs which is a misnomer.




  • Yeah, there are implicit and explicit biases at every stage of these models. The training data they collect, how they clean and use their training data, the actual training methodology, the prompts to literally alter the final output.

    That’s not to mention the main things LLM aren’t aware of, which is who are you as a person. An LLM doesn’t know nor can it understand your life experiences and how that shapes your opinions on who is best to represent you. Handing over cognition like this is basically throwing away your vote as you’ve forfeited one of the major methods you have to influence your government.


  • Glad to hear I’m not the only person who has had some difficulties. And yeah, you are right that it should be fine for hosting services, which is why I mentioned my specific use cases.

    However, I think calling out lack of support is always a good thing as you never know what support you may need down the road. I definitely have run into some issues due to their supported OS I’m running and its surprisingly out of date for a board that only released a couple years ago.