> 42.9 units of insulin from a single photo. That’s not a rounding error. That’s a potential fatality.
Shit like this is why you shouldn't involve AI output in your writing process. It's especially ironic in an article about LLMs being unreliable... but it's pointless when the pre-print seems just fine at least to my eyes.
probabilistic != deterministic
It's funny how with AI this comic is basically reversed: https://xkcd.com/1425/
Is this about AI?
1. If I feed the exact same image in, it does not deterministically give me the exact same result every time.
2. Or is this about calories, because even if a package label says "200 Calories", if you were to measure every package, each one would all be different. 198,199,200,201,202. Plus/Minus a pretty big range.
>>> answered own question. " It’s the same photo, the same model, the same question. But you won’t get the same answer"
I’ll save you a click: ‘Llms can’t perform direct calorimetry through a photo of a meal. Llms can’t even perform basic atomic spectroscopy’ in other news…
To me, someone without a full understanding of the AI systems, it seems like the problem is most strongly influenced by image classification. The next logical step in this research is to remove image classification from the loop, since it's a confounding factor.
ASI/AGI reached kap
I asked an AI to guess how much a picture of a rock weighed 500 times… But it does propose an interesting idea. Which is burn after labelling. (maybe it could be really good at this)
skill issue
Bullshit machine can't even do bullshit job?
LLMs going to llm.
Tomorrow on HN, "water is wet."
random number generator returns random numbers on each call. more news at 11
Slop
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What a dumb article. The picture of the sandwich is essentially just a picture of bread. You can’t see what’s inside. A human wouldn’t be able to tell you. These are essentially AI hit pieces.
Does this surprise anyone?
I mean these models are inherently probabilistic.
If you run enough samples you'll get results matching the learned probability distribution, the more you sample the higher the chances that you'll land on an unlikely response.