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He asked AI to count carbs 27000 times. It couldn't give the same answer twice
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Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#42With mass information you could infer much more from pictures. With some sort of standard cube in the picture as well as taking a picture at an angle that emphasizes all three dimensions you could also better estimate the relative volume. It’s tractable I think, but not from a pic alone.
Yes one could potentially increase accuracy greatly. One big problem would be occlusion.
There is already a solution to this that would be very hard to beat (and one can choose to use or not use an LLM to assist): prepare food yourself and use the information provided by the manufacturer.
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#43There's an incredibly serious lack of education with how LLMs & carb-counting works. This entire article would be better suited to astrology.com than hackernews.
When I opened it up, I assumed the author would have at least attempted a calculation service, maybe even placed something like the size of the meal into an actual model, using the integration of pre-existing tools that are (slightly more) accurate. Hell - most food literally is required to have calorie information, and you can query open source data for others!
But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI?
This is akin to the instagram reels that talk to chatGPT and ask it to time how long they're run is. Except those are treated as funny jokes rather than being turned into studies.
I'd like to see this study done using any kind of actual grounding knowledge, seeing what mistakes AI makes when attempting to query ground truth from picture analysis - there would at least be an interesting result methodology in that.
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#44What 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.
And it can be made better easily. Take a picture of the nutrition label of the bread and cheese first and then feed in this picture and you should get way better results
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#45I am surprised that people believe that calories can be counted correctly from a single photo
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#46It’s just an impossible problem. Photons don’t provide sufficient information to determine calories (at least not in any way they could practically be captured). Inside that sandwich could be drenched with olive oil or it could be hollow cheese with lettuce. It’s impossible to tell.
Then the correct answer is “I can’t tell.”
Not “Here’s a random guess that I just pulled out of my ass.”
LLMs have picked up the bad habit of trying to give an answer when no answer can be given from scientists, who overall don’t say “I don’t know” nearly as often as they should.
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#47I am... unsure why anyone would think LLMs would be able to do this. They are not magic oracles. Like I think even most humans would be extremely bad at this. Like, are people actually using LLMs for this? Please do not, it won't work.
Most people are convinced LLMs can do this.
Cal AI, which claims to generate a nutritional breakdown based off a photo, has $30 million in annual recurring revenue.
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#48Finally we have a simple way to get machines to generate a truly random number
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#49skill issue
Re: He asked AI to count carbs 27000 times. It couldn't give the same answer twice
#50> You’d expect the same answer each time. It’s the same photo, the same model, the same question. But you won’t get the same answer. Not even close — and the differences are large enough to cause a hypoglycaemic emergency.
No you wouldn't, not if you have a basic understanding of how LLMs work and what "temperature" is. They are stochastic algorithms picking the next token based on a highly structured (and often very useful) coin flip.