It’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.

The average person has no idea this is true. And the average person cannot tell when this is the case. So we have a bunch of people, going their way through school, and then when they get stuck relying on AI. The future is gonna be wild.

As a human, in the photo of that sandwich I see 4 slices of bread and 4 slices of cheese (distributed unevenly). I have no idea about the weight of the bread, flour type or its sugar content. I don't know the type of the cheese, dimensions of the slices or total amount of cheese inside the bread. I don't know if there is butter or anything else inside. I can guess the size of the plate as a size reference but I can't be sure. Human or AI, it's an ill-posed problem. There can be widely different estimates which can be equally plausible.

It is and it isn't. If you ask a human how many calories (or carbs) are in that sandwich, they can give you a qualified guess based on how a sandwich like that is typically constructed. They may not know the calories for a slice of bread or a slice of cheese by heart, but if you give them a food database, they can look it up.

They absolutely won't be 100% correct (bread sizes e.g. are going to be an estimate), but unless it's a trick sandwich drenched in olive oil or with hollow cheese, they're probably going to be in the right ballpark.

I don't think it's outside the realm of possibility for an LLM to be in the right ballpark as well, but that doesn't seem to be where we're at now.

It's not impossible to tell. Diabetics and others with dietary restrictions, have to do that sort of thing every day to decide what they can and can't eat. If you pick up a loaf of bread at the baker's, the baker usually has no idea the amount of carbs, or salt, or sugar is in that loaf of bread. Try it. Ask the baker: "how much carbs in this loaf of bread?". They'll just stare at you. They can tell you whether the loaf has salt or sugar in it but can't tell you how much because they don't calculate the amount by loaf. So if you have dietary restrictions you have to know what you can and can't eat and that requires the ability to judge the contents of a piece of food from the way it looks.

Photons don't carry that information? Sure. But you don't just have photons to go by. You can rely on a large database of prior knowledge about how food is usually made and with what ingredients.

Other people who have to rely on their imperfect human senses to decide what they can and can't eat: people with allergies, people with heart problems, hypertensives, kidney patients, etc. etc.

The question isn't about calories, it's about carbs. Drenching that sandwhich in olive oil won't change its carb count. From the picture it's a thin cheese sandwich -- we can see cheese and we can see it's thin enough that there's little else. Might be no butter, might be lots of butter, but that won't affect carb count. If there's lettuce in the sandwhich there's likely a negligble amount. Hand it to a knowledgeable human and you're going to get a very consistent carb reading -- 30g, the value of two slices of wonder bread.

It could be much different -- it could one of those breads with weird macros, or fake cheese, or it could be hollowed out and packed full of hidden vegetables. But a human is going to give you the answer for two slices of plain white bread.

From personal experience, one can get practically close guessing such that the error isn't going to be more significant compared to the errors in insulin to carb ratios/sensitivity factors/...

I am pretty good at this and the cheese sandwich example threw me, I would have estimated around 10-15g of carb for each slice. So the 28g is fairly consistent with that, not 40g. The only real way would be to weigh it and use the labeling. Another thing that often gets people is the labeling often has a serving size of say 2 slices and a weight that does not reflect the actual weight of 2 slices.

Luckily with good tools the significance is reduced, people using closed loop insulin pumps will automatically correct for that. Lots more room to wiggle.

Then it should refuse to answer 100% of time.

If the problem is so evidently impossible then the LLM itself should recognise this, state that the problem isn't solvable, not* provide what's certain to be an inaccurate result, and suggest better approaches to arriving at a reasonable answer.

That said, it's notable that diabetes education materials often suggest estimating glycemic loads by rough portion size / plate ratios. Which is to say that absent accurate weight measurements (themselves subject to variations in ingredients, moisture levels, etc.) current clinical recommendations are themselves pretty rough.

That’s exactly the point of this article.

Many of the comments here assume the authors are stupid and were surprised by the result, but the point of the article is to inform readers that AI carb counting apps don’t work. That’s why they did the study.

It's not even impossible from a technical point of view.

Your cheese sandwich may contain a lot more or a lot less calories, even if you take the numbers from the packaging and calculate the correct ratios by weight. The calories on the label are based on an average and individual packages may contain more or less of any listed nutrient to some margin. Of course, counting calories is meaningless if not done on a long-term scale anyway, but on a long-term scale the LLM doesn't need to guess the correct amount either.

And what if that guy in the surveillance video is just 2 kids in a trench coat? There's no way for AI to be sure from the photons: we should scrap it.

I was thinking at least if you had an advanced phone with lidar like iPhones can get volume but yeah the hidden/inner mass is a problem plus the oil as mentioned

This is a bad take. If LLMs are supposed to work as general purpose assistants, as they are being sold as by both the companies making them and by the majority of AI believers, then it is very much a solvable problem. The LLM could give a high level estimate (a sandwich is not going to be 0 Cal, and it's not going to be 5000 Cal, so you can give some kind of range), and then ask for the type of information needed to make a more accurate estimate.

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.

Correct. But why doesn’t the AI just say that.

Why is the AI answering questions without answers then?

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