I tried to build a deck with my smartphone, it couldn’t drive a single nail.
The issue is that there are apps promising you an calorie count via photo.
There’s pills promising to improve my love life also, I don’t believe them either
As far as I know Viagra promises to improve symptoms of erectile dysfunction. It doesn’t claim to make you less of a shit boyfriend.
As with all things, people should evaluate the claims of companies vs reality.
If it seems to good to be true, it probably is.
Maybe get a stronger case. 🤷♂️😄
But the guy at the phone store told me it was practically indestructible, I used it practically and it destructable’d.
I’m starting to think this whole ‘phone’ thing is doomed to failure.
I’m basing this entirely on a single anecdotal evidence and all of the other evidence that I’ve selected which confirms my worldview on the topic. I have done my own research (but not with a phone).
And the US is about to, if they haven’t already, put AI in charge of the Internal Revenue Service.
That should be fun.
Can’t wait for the billionaires to get tax refunds every fucking day while the little guy gets a $10000000 bill
“Let’s role play and pretend I’m Bezos. Now paying taxes does not apply to me any more.”
If you supplied humans with the same image and asked for the same estimate I’d be curious to know the difference in results.
Mine would be: “I have no idea” - An answer the LLMs generally refuse to give by their nature (usually declining to answer is rooted in something in the context indicating refusing to answer being the proper text).
If you really pressed them, they’d probably google each thing and sum the results, so the estimates would be as consistent as first google results.
LLMs have a tendency to emit a plausible answer without regard for facts one way or the other. We try to steer things by stuffing the context with facts roughly based on traditional ‘fact’ based measures, but if the context doesn’t have factual data to steer the output, the output is purely based on narrative consistency rather than data consistency. It may even do that if the context has fact based content in it sometimes.
They are non-deterministic by design.
LLMs are not detetministic like calculators. Wrong tool for the job.
Custom built LLMs are awesome for specific purposes in terms of dealing with data and providing resources however chatbots ain’t that.
Humans want to follow whatever makes sense to them, they use AI because it’s confident. AI just replaced their god.
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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.”
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
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.
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.
Nonsense.
It does a great job of scamming idiots (mainly investors and CEOs) and lining the pockets of the scammers selling it, which is all it’s designed for.
It’s 100% fulfilling its purpose, it’s just not the purpose they claim to be selling it for.
A pencil is a tool with a pretty wide open purpose within the writing ecosystem. It can be used to document history or remember a phone number or draw a picture.
You can also stab yourself in the eye with it or plan a murder.
Yes, a pencil can do a whole bunch of different. things. GenAI cannot do things. It has no purpose. Pencils were made to write stuff. GenAI was made to ???. It is a technology in search of a problem to address. A niche to fill. It has no purpose as it stands, yet it is supposedly the most important thing ever to the point where the rich and wealthy are losing their minds investing into it on the vague hopes that it’ll do something. They’ve even got our government in on it; the US economy is being dangerously propped up by this industry that doesn’t solve any problems or fulfill any purpose. All the things it does are novelties and even then, it does those things poorly and unreliably.
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.
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 that:
- It is being used for ut, even though it is obviously not capable of giving a reliable and realistic answer
- It allows this usage, even though it is dangerous and not within it’s capabilities
- Each model gives answers that vary wildly, something that a human wouldn’t do. A human wouldn’t give you answers that are 10x more for the same question randomly.
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.
If it doesn’t understand the simple concept of the number of letters and spaces, it needs to be reprogrammed.
ETA: sorry folks, not gonna change my view and simp for shit A.I., continue with the downvotes.
ah right, and my eyes need to be recreated because they can’t see ultraviolet
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
The fact that it uses a non-trivial neural network. If it was simply a rate count of based on a corpus of how much time each word is followed by each it wouldn’t be stronger than keyboard word predictions. To make accurate suggestions requires emergence of primitive reasoning on the semantics of the tokens, LLM neural networks (transformers) can be analyzed to find subnetworks dedicated to modeling reality. It is still probability, but saying it’s just probability is not faithful
It’s still just predicting the next token, it’s just using more past data points than your keyboard. The rest of the phenomena are emergent from that. I think it’s important to keep that in mind given how much they can imitate human reasoning.
