If you supplied humans with the same image and asked for the same estimate I’d be curious to know the difference in results.
I tried to build a deck with my smartphone, it couldn’t drive a single nail.
Waste of energy. It’s like asking a person to estimate a non-trivial angle. Either use a model trained for that task, or don’t bother.
The point is they are advertising that these models can do it.
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.
I don’t know what ads show that, but anyone who knows the first thing about LLMs knows you don’t get the same answer twice.
I’d get this expectation 5 years ago when most people weren’t familiar with it, but come on… you don’t need to feed it an image 500 times to see that.
Technically, you can get the same answer twice from an LLM, but only when you control the full input. When a model is being run, a random seed/hash is applied to the input. If you run the model locally you could force the seed to always be the same so that you would always get the same answer for a given question.
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.
OK I wonder if there’s something wrong with the photo.
The photo:
WTF!!??
That’s like estimating the carbs in 2 slices of standard sandwich bread! Of course not all bread has the same amount of sugar, but a reasonable range based on an average should be a dead easy answer.I thought the headline sounded crazy, but try to read the article, and it actually becomes worse. I have said it many times before, these AI chatbots should not be legal, they put lives at risk.
To be fair there’s no way of knowing what the filling is, so the AI may be guessing based on that too
Nope, Claude and Gemini both guessed fewer carbs than are in the bread.
What in the picture indicates any form of filling?
What you can see is cheese, there is probably butter too, but those 2 have zero carbohydrates, so adding carbohydrates based on filling would be pure speculation.
There are no carbohydrates to see beyond the bread.
There is no evidence of any filling, as there is zero bulge in the bread.
The answer should be based on what can be seen, with a remark to that effect, and that there possibly could be more if it contains filling that isn’t visible.The AI could ask about a possible filling, instead of just making shit up with zero evidence.
To your point -
If a friend texted me the same picture and question, I would do exactly what you described. Try to give a calculated guess that wouldn’t change.
Unless I was lazy and Googled it.
Google’s carbohydrate tool says 8g, then the AI overview goes on to contradict that by saying “A standard cheese sandwich typically contains between 25 and 35g.”
Friendly reminder that LLMs don’t do math, they guess what number should come next, just like words.
It can probably link the image to the words “a photo of a sandwich on a plate”, and interpret the question as “how many calories are in a sandwich” but from there it is just guessing at the syntax of an answer, but not at finding any truth.
It knows sandwiches have calories and those tend to be 3-4 digit numbers, but also all numbers kinda look the same, so what’s to say it’s not 2, 5, or 12 digits?
Tool-powered agents can do math though. The issue is the fuzziness of it trying to guess carbs. It doesn’t know weight, ingredients, or anything other than a picture. These tools can be useful but not for this. Maybe one day but not yet.
Whoever claims an AI (LLM or agents) can do that and charging their users is lying and defrauding them.
The apps are advertising that they can do this tho. Many of them are aggressively sponsoring YouTubers who advertise you can basically just wave your phone over the food and it takes away all the “work” from traditional calorie counting apps
But the ai assumes itself infallible, at least it could ask…
That’s true, it should ask follow-up questions, or at least clarify its assumptions
They put lives at risk the same way every single product at your local home improvement store does. When you misuse a tool for a purpose it wasn’t intended and isn’t good at, you’re going to get bad results.
This is an issue for the educational system, not the legal system.
Tools at home improvement stores were made to fulfill a specific purpose. GenAI still does not have a purpose it fulfills despite having hundreds of billions of dollars invested, not to mention all the other resources it’s sucking up.
What if the packaging on every tool at home depot grossly misrepresented its capabilities and/or purpose?
This chainsaw cures cancer? Hot damn somebody call RFK!
Concrete mix goes great with pancakes, etc.
Does OpenAI claim ChatGPT is fit for those purposes? No.
The concrete itself will happily mix into your pancakes.
I think the whole point of this discussion is that the various peddlers of AI in fact do make wild claims about their capability.
My observation is that largely it’s the downstream AI consumers who repackage it irresponsibly. That said, I don’t hang on the words of Sam Altman and it’s certain they are pushing the idea that AI is more capable than it is, but mostly what I see is them saying they built this thing and it does neat stuff and it can probably do neat stuff for you, use your imagination.
I believe a lot of the folks developing these tools would be horrified at the irresponsible ways vendors and end users are using it.
Sam Altman is the face of OpenAI. He is responsible for misrepresenting the product he sells. If you’re going to sling blame around, then you had better observe the words of Sam Altman.
The thing that I think will be most impactful on that five to ten year timeframe is AI will actually discover new science.
This sick man is taken seriously in mainstream media and politics, and it’s no exaggeration to say he has blood on his hands.
As others have pointed out, this is also a problem with how they are advertising it.
If duct tape was advertised as something that you can use to hold your roof beams together, you’d have a issue with that.
And at the same time I wouldn’t say “hey fuck that, duct tape is terrible! It doesn’t hold beams together, I can’t use it to tow a trailer, it’s all just pretending to stick paper together because really every sliver of duct tape just sticks to the previous piece, etc etc” But that’s the cool thing we do on Lemmy.
The ad is bad, duct tape ain’t bad.
I have not seen OpenAI advertise ChatGPT as capable of medical diagnosis or therapy or anything like that. If you want therapy, and you can’t afford better — because I think we can agree that AI is terrible at it, then there should be a therapy app with explicit safety controls.
The problem is someone created a screwdriver which is handy for lots of screwdriver shaped purposes and someone is trying to carve a ham.
deleted by creator
People should read the top comments on Hackernews instead of anyone here, they’re more informed on the topic than Lemmy is
Better yet, download Qwen 3.5/3.6, with a “raw” notepad like Mikupad. Try it yourself:
https://huggingface.co/ubergarm/Qwen3.6-27B-GGUF
https://github.com/lmg-anon/mikupad
One might observe:
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Chat formating, and how janky the “thinking” block is.
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How words are broken up into tokens, not characters.
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How particularly funky that gets with numbers.
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Precisely how sampling “randomizes” the answers by visualizing “all possible answers” with the logprobs display.
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And, thus, precisely how and why carb counting in ChatGPT fails, yet a measly local LLM on a desktop/phone could get it right with a little tooling or adjustment.
This is exactly what OpenAI/Anthropic don’t want you to do. They want users dumb and tethered, like a cloud subscription or social media platform. Not cognizant of how tools they are peddling as magic lamps actually work. And why, and how, they’re often stupid.
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Yeah - if you’re after AI fanbois you should head over there. They’re not that bright, but if you check show and tell you can see what claude’s been ut to last two days
HN is full of techno fascists
Bruh a couple of months ago I asked it (Gemini) to check the number of characters, including spaces, in a potential game character name because I was working at the time and couldn’t stop to check my in-head count. It told me 21–I had counted 20. I thought I must have gotten distracted and miscounted. Later when I had time to actually focus on the issue it turned out AI had miscounted a 20 character string (maybe counting the null terminating character?).
AI doesn’t see individual characters, it sees tokens, with most tokens being a word or part of a word. That’s why per-character questions have such a high failure rate.
It’s it doesn’t understand the simple concept of the number of letters and spaces, it needs to be reprogrammed.
How many letters are there in 令牌? It’s a simple question right, you wouldnt need to search for it to find out would you?
It doesn’t understand anything though? It never will. It’s a probability machine. If you choose to believe its output, that’s on you. I use it as a coding assistant to get boring things done faster. Fire a prompt at claude code, grab a coffee, check out the diff. But that last step is crucial. Can’t trust AI output blindly.
The embedding layer post tokenization is not just a probability machine the way you’re suggesting it. You can argue that it is probabilistic with inferred sentiment, but too many people think it works like how text prediction on your phone does and that is just factually inaccurate.
Verify output of course, but saying “it doesn’t understand anything” and “probability machine” is a borderline erroneous short sell. At the level of tokens it “understands” relationships, and those relationships are not probabilistic, though they are fundamentally approximated based on a training corpus.
Can you explain how it’s more than probability? It’s using a neural network to guess the most likely next token, isn’t it?
You could also say that it chooses what will be the next word it will say to you. It has a few words to choose from, which it has selected in relation to the previously spoken words, your question and previous interactions (the context). The probability you’re talking about (a number) could also be seen as it’s preference among those words. I’m not sure the probability vocabulary/analogy is necessarily the best one. The best might be to not employ any analogy at all, but then you have to dig deeper into the subject to form yourself an informed opinion. This series of videos explains it better than I do : https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
