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Cake day: June 5th, 2023

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  • People developing local models generally have to know what they’re doing on some level, and I’d hope they understand what their model is and isn’t appropriate for by the time they have it up and running.

    Don’t get me wrong, I think LLMs can be useful in some scenarios, and can be a worthwhile jumping off point for someone who doesn’t know where to start. My concern is with the cultural issues and expectations/hype surrounding “AI”. With how the tech is marketed, it’s pretty clear that the end goal is for someone to use the product as a virtual assistant endpoint for as much information (and interaction) as it’s possible to shoehorn through.

    Addendum: local models can help with this issue, as they’re on one’s own hardware, but still need to be deployed and used with reasonable expectations: that it is a fallible aggregation tool, not to be taken as an authority in any way, shape, or form.


  • On the whole, maybe LLMs do make these subjects more accessible in a way that’s a net-positive, but there are a lot of monied interests that make positive, transparent design choices unlikely. The companies that create and tweak these generalized models want to make a return in the long run. Consequently, they have deliberately made their products speak in authoritative, neutral tones to make them seem more correct, unbiased and trustworthy to people.

    The problem is that LLMs ‘hallucinate’ details as an unavoidable consequence of their design. People can tell untruths as well, but if a person lies or misspeaks about a scientific study, they can be called out on it. An LLM cannot be held accountable in the same way, as it’s essentially a complex statistical prediction algorithm. Non-savvy users can easily be fed misinfo straight from the tap, and bad actors can easily generate correct-sounding misinformation to deliberately try and sway others.

    ChatGPT completely fabricating authors, titles, and even (fake) links to studies is a known problem. Far too often, unsuspecting users take its output at face value and believe it to be correct because it sounds correct. This is bad, and part of the issue is marketing these models as though they’re intelligent. They’re very good at generating plausible responses, but this should never be construed as them being good at generating correct ones.





  • Ashelyn@lemmy.blahaj.zonetoTrans@lemmy.blahaj.zoneQuestion
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    1 month ago

    I feel like there’s a point to be made about the historical use of the term transracial, which described the experiences of people who were adopted into families of a different race than oneself as a child. In these cases, even though there may be superficial differences present, the culture the person was raised into is that of the adoptive family, and as a result it’s what they tend to identify with based on lived experience.

    I think there’s a difference between idolizing a different race or its culture thereof, and having formative experiences as a member of that community, even if there were indicators that marked you as an “other” both to those within and without. I don’t know if it’s really worthwhile to tell people how they can and can’t feel about their identity in this way, but there are definitely nuances that make racial identity experiences distinct from gender identity experiences.


  • I believe the wording is “drinks which burn the throat” which naturally means:

    • ❌ Coffee
    • ❌ Alcoholic drinks
    • ❌ Coca Cola
    • ❌ Hot tea
    • ❌ Chai lattes
    • ✅ Sprite, other non-caffeinated soft drinks
    • ✅ Hot Chocolate
    • ✅ Kombucha
    • ✅ Energy drinks???
    • ✅ Herbal Tea (even while hot, but mostly if you’re sick as a home remedy)

    Most of the focus is interpreted as “contains caffeine and/or alcohol” but the wording is vague enough that it leaves for a lot of weird wiggle room people try to argue (based on convenience usually). It’s quite silly