27 ms·

> Unsurprisingly of course while it said this is not a proven treatment, it did not have the reasoning skills to suggest that It has nothing to do with reasoni

> Unsurprisingly of course while it said this is not a proven treatment, it did not have the reasoning skills to suggest that It has nothing to do with reasoning skill and everything to do with the data on which it was trained. If you didn't know what cyanide does to a human and someone asked you about chugging a pint of cyanide, you might not warn them about the dangers either.

steveBK123 3y ago
I think it's been trained on enough data that if it had reasoning skills, one should expect it to know humans need oxygen and cyanide is bad. However, I don't think these models really have reasoning skills as a human would understand them. It has a lot of safeguards coded around it, so it has strong opinions on politically sensitive topics, "both sides" a ton of topics, and is happily lead around its own programming with simple A->B->C prompt sequences. I mean how many prompts lead to 5 paragraph solutions ending in "Ultimately, whether __ or not depends on how you define __ and the context in which the question is being asked. It can be a matter of personal interpretation and perspective." So many prompts result in inanely stupid "idiot savant" type answers if they weren't hardcoded already. (Pound of feathers vs pound of bricks is hardcoded obviously), but these all got dumb responses - "what has more caffeine a pound of grinds or a pound of beans" "what has more caffeine a single shot of espresso or a latte with one shot" "is water wet" then "is rain wet" and finally "is water or rain wetter"
- naasking 3y ago
  Sometimes output doesn't make sense because the only "sense" LLMs have is the digital word. You can only infer so much information from this. Multimodal LLMs, where semantics are inferred across multiple sensory modalities, have already proven to be significantly better at making sense.

steveBK123 3y ago

I think it's been trained on enough data that if it had reasoning skills, one should expect it to know humans need oxygen and cyanide is bad. However, I don't think these models really have reasoning skills as a human would understand them. It has a lot of safeguards coded around it, so it has strong opinions on politically sensitive topics, "both sides" a ton of topics, and is happily lead around its own programming with simple A->B->C prompt sequences. I mean how many prompts lead to 5 paragraph solutions ending in "Ultimately, whether __ or not depends on how you define __ and the context in which the question is being asked. It can be a matter of personal interpretation and perspective." So many prompts result in inanely stupid "idiot savant" type answers if they weren't hardcoded already. (Pound of feathers vs pound of bricks is hardcoded obviously), but these all got dumb responses - "what has more caffeine a pound of grinds or a pound of beans" "what has more caffeine a single shot of espresso or a latte with one shot" "is water wet" then "is rain wet" and finally "is water or rain wetter"

naasking 3y ago
Sometimes output doesn't make sense because the only "sense" LLMs have is the digital word. You can only infer so much information from this. Multimodal LLMs, where semantics are inferred across multiple sensory modalities, have already proven to be significantly better at making sense.