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Joined 1 year ago
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Cake day: June 5th, 2023

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  • I linked it because I recall it having a lot of cogent points and being relevant, and because I don’t remember off the top of my head the specific allegations, I didn’t want to dig through a two hour video I’ve already seen at the exact moment of writing because I only had so much time and research to dedicate to a Lemmy comment. It’s valid to be annoyed by a long video linked as an argument, but my comment was a “too long didn’t watch” version of it… that actually left out some details like the founder also being a fucking eugenicist.

    I also use an adblocker, and the vid has some opinions obviously but was mostly going over evidence, recordings, and related allegations.

    You don’t have to watch it if you don’t want to. I linked it as a secondary source. While primary sources are preferable and it might have been a good idea to do the legwork myself, I wanted something posted quick to maybe make people think twice on the “donate to TST” call to action in the initial comment.





  • The device wouldn’t necessarily have to be constantly streaming the audio to a central server. If it’s capable of hearing wake up words like “Ok Google” it’s capable of listening for other phrases and having onboard processing to relay back the results much more compressed. Whether or not this is common practice is another matter, and yes the algorithms are scary good even without eavesdropping.






  • I mean, even if they’re broadly unenforceable, companies include them anyways as a means of intimidation. This FTC decision basically puts up a giant neon sign telling everyone “yeah this isn’t legal” which makes it pretty cut and dry. Big companies thrive on ambiguity because that’s where an expensive lawyer comes in to argue the case whichever way; they will have a much harder time doing that now.






  • That’s fair. I think fundamentally a false positive/negative isn’t that much different. Pretty much all tests—especially those dealing with real world conditions—are heuristic, as are all LLMs by necessity of the design. Hallucination is a pretty specific term given to AI as an attempt to assign agency to a system that doesn’t actually have any (by implying it’s crazy and making stuff up instead of a black box with deterministic inputs and outputs spitting out something factually wrong but with a similar format to what is trained on). I feel like the nature of any tool where “you can’t trust this to be entirely accurate” should have an umbrella term that encompasses both types of providing inaccurate info under certain conditions.

    I suppose the difference is that AI is a lot more likely to randomly go off, whereas a blood test is likelier to provide repeated false positives for the same person with their unique biology? There’s also the fact that most medical tests represent a true/false dichotomy or lookup table, whereas an LLM is given the entire bounds of language.

    Would an AI clustering algorithm (say, K-means for instance) giving an inaccurate diagnosis be a false positive/negative or a hallucination? These models can be programmed on a sliding scale and I feel like there’s definitely an area where the line could get pretty blurry.



  • I mean, AI is used in fraud detection pretty often; when it hits a false positive (which happens frequently on a population-level basis), is that not a hallucination of some sort? Obviously LLMs can go off the rails much further because it’s readable text, but any machine learning model will occasionally spit out really bad guesses almost any person could have done better with. (To be fair, humans are highly capable of really bad guesses too).




  • Perhaps they are bad examples, but my point was more that I think those ecosystems thrive in spite of the company that owns the upstream at this point more than because of it. They did tremendously useful work getting the projects off the ground but it ostensibly seems like they get in the way more often than not; that said, I haven’t done any open source work on either of the two. I’d be interested to hear your take, I could be pretty far off the mark.

    Honestly my main examples I’d point to right now are situations like manifest V3 and Android nitpicks like the recent Bluetooth 2-tap change; don’t get me wrong, they are easy to fork and have thriving ecosystems in terms of volunteer dedication, but those forks still primarily targeted towards technical users (with some exceptions) and companies selling devices like the Freedom Phone (and other, actually neat, useful, properly privacy focused devices which is awesome!). By far, however, most users are on the upstream branch due to “default choice” psychology and have to deal with the bullshit that’s increasingly integrated into the proprietary elements that Google seems to be making harder and harder to separate from the open source ones. I suppose that’s why education and getting the word out are all the more important though.

    Could be the sensationalist end of the tech news cycle getting me spun up on an overall inaccurate view of things.

    There is also the point I have to raise that security update support is always a very valuable asset that can be worth dealing with some downsides to get ahold of. I’m hoping a lot of those can be pulled into open source projects on more of a piecemeal basis where applicable?

    I’d be happy to be proven wrong about my rudimentary assessment. I have enough things to be doomer about and honestly it would be nice to have one or two fewer!