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Mushroom hunting with LLMs: what can go wrong?
21–30 of 81 posts
Re: Mushroom hunting with LLMs: what can go wrong?
#22Re: Mushroom hunting with LLMs: what can go wrong?
#23I think the more important question is: have people really run out of interesting flavors at their local stores and restaurants, that they are so obsessed with eating things they find growing on the ground? I’ll never understand mushroom culture.
* Foraging is very satisfying to do
* Foraging allows you to participate in your cultural practices (although sadly many people in the US are long-cut off from such cultural knowledge)
* You can get fresher food foraging
* There absolutely are interesting varieties that aren't sold because they are hard to farm/transport/store etc.
* Foraged food is free
Re: Mushroom hunting with LLMs: what can go wrong?
#24So even if it's wrong, a short verification usually reveals why it was wrong. I haven't had a situation yet where it was catastrophically wrong (i.e. the mushroom it called out looks nothing like the mushroom imaged). It also will generally warn of lookalikes.
I also use it a lot for general plant ID, and it's really impressive there too, with much lower stakes (I'm not eating those).
Re: Mushroom hunting with LLMs: what can go wrong?
#25However it's very obvious that you shouldn't rely on them to decide what to put in your body. All those apps that I have seen (I guess the responsible ones?) will warn you not to do it. That's not what it's for.
Re: Mushroom hunting with LLMs: what can go wrong?
#26Re: Mushroom hunting with LLMs: what can go wrong?
#27Giving the impossibly high standards people here have for identifying a mushroom, it’s a wonder that they would eat a mushroom at all. You may not trust an LLM, but how would you even trust yourself? Who would you ever trust?
Re: Mushroom hunting with LLMs: what can go wrong?
#28Is it possible to reliably (>99%) identify mushrooms from a single image, with any method? All image datasets have an intrinsic minimum error that comes from the limit of information in the data, regardless of what model or method you use to analyze it. You might not be able to tell apart two similar-looking mushroom species just by looking.