4 ms·

That's kind of a different thing. Making the best AI possible for recognizing mushrooms and trying to produce the most factually correct answers is different th

That's kind of a different thing. Making the best AI possible for recognizing mushrooms and trying to produce the most factually correct answers is different than letting AIs run rampant, generating fiction. Also, I'd want some kind of verification before using the mushroom app. Who made the app? Do they have people on their team familiar with mushrooms? Botanists or whatever you call them. Some random dude with free time and access to maybe 10 species of mushrooms in his backyard.. even if his intent is good, is still dangerous.

chx 2y ago
LLMs can not produce factual answers. That's not a property of them. What people call "hallucination" is the only thing they can do. Even if it occasionally happens to deliver a correct results that's just the broken clock being right twice a day. The typical ones sound authorative and so people believe it's authorative, this is well documented in Thinking Fast & Slow. As Aza Raskin put it, they hit a zeroday in human cognition. But that doesn't change the fundamentals of it, only the perception of the output which we need to consciously combat. https://hachyderm.io/@inthehands/112006855076082650 https://hachyderm.io/@inthehands/112006855076082650 > You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to. > Alas, that does not remotely resemble how people are pitching this technology.
- 8n4vidtmkvmk 2y ago
  I wasn't referring specifically to LLMs when talking about the mushrooms. You could do something more sophisticated. I'm not an expert on it, but you could probably compare the images against a known, validated dataset to give you a baseline and then use some fancy AI algorithm to hone in on the best match, or do it the other way around -- let the AI take a first guess and then validate that its not hallucinating. They're already trying to use AI for breast cancer scans and what not, I don't see why we can't do it for mushrooms.

chx 2y ago

LLMs can not produce factual answers. That's not a property of them. What people call "hallucination" is the only thing they can do. Even if it occasionally happens to deliver a correct results that's just the broken clock being right twice a day. The typical ones sound authorative and so people believe it's authorative, this is well documented in Thinking Fast & Slow. As Aza Raskin put it, they hit a zeroday in human cognition. But that doesn't change the fundamentals of it, only the perception of the output which we need to consciously combat. https://hachyderm.io/@inthehands/112006855076082650 https://hachyderm.io/@inthehands/112006855076082650 > You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to. > Alas, that does not remotely resemble how people are pitching this technology.

8n4vidtmkvmk 2y ago
I wasn't referring specifically to LLMs when talking about the mushrooms. You could do something more sophisticated. I'm not an expert on it, but you could probably compare the images against a known, validated dataset to give you a baseline and then use some fancy AI algorithm to hone in on the best match, or do it the other way around -- let the AI take a first guess and then validate that its not hallucinating. They're already trying to use AI for breast cancer scans and what not, I don't see why we can't do it for mushrooms.