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Cake day: June 15th, 2023

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  • But any 50 watt chip will get absolutely destroyed by a 500 watt gpu

    If you are memory-bound (and since OP’s talking about 192GB, it’s pretty safe to assume they are), then it’s hard to make a direct comparison here.

    You’d need 8 high-end consumer GPUs to get 192GB. Not only is that insanely expensive to buy and run, but you won’t even be able to support it on a standard residential electrical circuit, or any consumer-level motherboard. Even 4 GPUs (which would be great for 70B models) would cost more than a Mac.

    The speed advantage you get from discrete GPUs rapidly disappears as your memory requirements exceed VRAM capacity. Partial offloading to GPU is better than nothing, but if we’re talking about standard PC hardware, it’s not going to be as fast as Apple Silicon for anything that requires a lot of memory.

    This might change in the near future as AMD and Intel catch up to Apple Silicon in terms of memory bandwidth and integrated NPU performance. Then you can sidestep the Apple tax, and perhaps you will be able to pair a discrete GPU and get a meaningful performance boost even with larger models.


  • This will be highly platform-dependent, and also dependent on your threat model.

    On PC laptops, you should probably enable Secure Boot (if it’s not enabled by default), and password-protect your BIOS. On Macs you can disable booting from external media (I think that’s even the default now, but not totally sure). You should definitely enable full-disk encryption – that’s FileVault on Mac and Bitlocker on Windows.

    On Apple devices, you can enable USB Restricted Mode, which will protect against some attacks with USB cables or devices.

    Apple devices also have lockdown mode, which restricts or disables a whole bunch of functionality in an effort to reduce your attack surface against a variety of sophisticated attacks.

    If you’re worried about hardware hacks, then on a laptop you’d want to apply some tamper-evident stickers or something similar, so if an evil maid opens it up and tampers with the hardware, at least you’ll know something fishy happened, so you can go drop your laptop in an active volcano or something.

    If you use any external devices, like a keyboard, mouse, hard drive, whatever…well…how paranoid are you? I’m going to be honest: there is a near 0% chance I would even notice if someone replaced my charging cables or peripheral cables with malicious ones. I wouldn’t even notice if someone plugged in a USB keylogger between my desktop PC and my keyboard, because I only look at the back of my PC once in a blue moon. Digital security begins with physical security.

    On the software side, make sure you’re the only one with admin rights, and ideally you shouldn’t even log into admin accounts on a day-to-day basis.




  • I guess the idea is that the models themselves are not infringing copyright, but the training process DID. Some of the big players have admitted to using pirated material in training data. The rest obviously did even if they haven’t admitted it.

    While language models have the capacity to produce infringing output, I don’t think the models themselves are infringing (though there are probably exceptions). I mean, gzip can reproduce infringing material too with the correct input. If producing infringing work requires both the algorithm AND specific, intentional user input, then I don’t think you should put the blame solely on the algorithm.

    Either way, I don’t think existing legal frameworks are suitable to answer these questions, so I think it’s more important to think about what the law should be rather than what it currently is.

    I remember stories about the RIAA suing individuals for many thousands of dollars per mp3 they downloaded. If you applied that logic to OpenAI — maximum fine for every individual work used — it’d instantly bankrupt them. Honestly, I’d love to see it. But I don’t think any copyright holder has the balls to try that against someone who can afford lawyers. They’re just bullies.



  • Thanks for the info. I was not aware that Bluesky had public, shareable block lists. That is indeed a great feature.

    For anyone else like me who was not aware, I found this site with an index of a lot of public block lists: https://blueskydirectory.com/lists . I was not able to load some of them, but others did load successfully. Maybe some were deleted or are not public? I’m not sure.

    I’ve never been heavily invested in microblogging, so my first-hand experience is limited and mostly academic. I have accounts on Mastodon and Bluesky, though. I would not have realized this feature was available in Bluesky if you hadn’t mentioned it and I didn’t find that index site in a web search. It doesn’t seem easily discoverable within Bluesky’s own UI.

    Edit: I agree, of course, that there is a larger systemic problem at the society level. I recently read this excellent piece (very long but worth it!) that talks a bit about how that relates to social media: https://www.wrecka.ge/against-the-dark-forest/ . Here’s a relevant excerpt:

    If this truly is the case—if the only way to improve our public internet is to convert all humans one by one to a state of greater enlightenment—then a full retreat into the bushes is the only reasonable course.

    But it isn’t the case. Because yes, the existence of dipshits is indeed unfixable, but building arrays of Dipshit Accelerators that allow a small number of bad actors to build destructive empires defended by Dipshit Armies is a choice. The refusal to genuinely remodel that machinery when its harms first appear is another choice. Mega-platform executives, themselves frequently dipshits, who make these choices, lie about them to governments and ordinary people, and refuse to materially alter them.



  • I’d rather have something like a “code grammar checker” that highlights potential errors for my examination rather than something that generates code from scratch itself

    Agreed. The other good use case I’ve found is as a faster reference for simple things. LLMs are absolutely great for one-liners and generating troublesome (but logically simple) things like complex xpath queries. But I still haven’t seen one generate a good script of even moderate complexity without hand-holding. In some cases I’ve been able to get usable output with a few shots, saving me a bit of time compared to if I’d written the whole darned thing from scratch.

    I’ve found LLMs very useful for coding, but they aren’t replacing my actual coding, per se. They replace looking things up, like through man pages, language references, or StackOverflow. Something like ffmpeg, for example, has a million options and it is always a little annoying to sift through the docs manually when I just need to do one specific task.

    I’m sure it’ll happen sooner or later. I’m not naive enough to claim that “computers will never be able to do $THING” anymore. I’ll say “not in the next year”, though.


  • Yeah, I’m not too mad about this. It’s a good idea, but without legal weight behind it, it ultimately failed. Ideally GDPR and similar regulations would provide something similar, so I can set my preference once and every site would be required to respect it. That would be much better that the current situation, which is that I am forced to navigate every asshole site’s custom cookie notice. Each one’s a little different, and some of them break certain browser configurations. It’s a UX nightmare. This is probably by design — annoy users into submission. Because nobody in their right mind would ever click “allow” if it were not the easier choice.


  • Just marketing nonsense. There are three ways to present AI features:

    1. A generational improvement on things that have been available for 20+ years. This is not sexy and does not make for good advertising. For example: grammar checking, natural-speech processing (Siri), automatic photo tagging/sorting.

    2. A new type of usage that nobody cares about because they’ve lived without it just fine up to now.

    3. Straight-up lie to people about what it can do, using just enough weasel words to keep yourself out of jail.



  • In theory, an “AI PC” (please imagine giant eye-rolls along with the scare quotes) has the hardware to run models locally instead of shunting stuff off to OpenAI or Anthropic for processing. So in theory, it’s more private and secure than similar functionality on a “traditional PC”.

    In practice…wtf knows what Windows is doing anyway? Or what it will do with the next OS update? Same for macOS. On the Mac side, Apple keeps talking about their local AI and private cloud AI, and yet they’re still partnering with OpenAI for ChatGPT integration. I don’t want to use anything that even has the capability to send my shit to OpenAI, for the same reason I don’t like to put poison in my fridge no matter how clearly labelled it might be.




  • We find that the MTEs are biased, signif-icantly favoring White-associated names in 85.1% of casesand female-associated names in only 11.1% of case

    If you’re planning to use LLMs for anything along these lines, you should filter out irrelevant details like names before any evaluation step. Honestly, humans should do the same, but it’s impractical. This is, ironically, something LLMs are very well suited for.

    Of course, that doesn’t mean off-the-shelf tools are actually doing that, and there are other potential issues as well, such as biases around cities, schools, or any non-personal info on a resume that might correlate with race/gender/etc.

    I think there’s great potential for LLMs to reduce bias compared to humans, but half-assed implementations are currently the norm, so be careful.