51–60 of 329 posts
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.
I 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.
Truth is the LLM is good at making intelligent decisions. But in order to make intelligent decision, you need context.
If you give proper context -> ask the LLM -> get almost perfect result every time.
Anything else is rolling dice, a very special type of dice, but dice anyhow. Not magic.
1. Even if the task is unreasonable, it is good to showcase that the LLM will perform poorly - warning not to be used for diabetes.
2. As it is a probabilistic model, the approach was to execute it multiple times and look at the distribution. They also tried to minimize variance: "All at the lowest randomness setting these models offer.", the post mentions. Yet the variance of the responses is surprising.
3. A multimodal LLM should be in general able to discriminate between crema catalana and a cheese sandwich, and provide a textual, uncalculated range of how much calories the item has (internet is full with tables for calorie counting and things such as this https://fitia.app/calories-nutritional-information/cheese-sandwich-1205647).
4. It is not clear that the "expose" surprised / outraged style is just a communication vehicle or if the author really thought that e.g. LLMs could be hypothetically able to provide confidence estimates.I 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.
As far as consumers know, LLMs can identify the towns pictures were taken (without metadata), can summarize entire movies, generate clips of your kid flying a rocket to the moon, can translate images from any language imaginable, but somehow they cannot estimate the calories in a cheese sandwich.
The supposed professional posting about an LLM deleting their prod database for their non-existent company asked the AI to explain itself. That's the level of LLM knowledge you should expect from most people that actually work with these tools.
There'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 - m…
Funny thing is 4o did look up calories but I guess it was too good for this world
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.
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.
They’re algorithms and they were designed this way.
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.
Why assume trick ingredients?
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.
Then it should refuse to answer 100% of time.
I used LLMs to count calories, but not based on photos, I mean I also did that, but primarily I fed in my exact ingredients and then used weights to get calorie estimates. Was it always correct? Certainly not. But it helped me lose 30kg of weight since keeping even some track of calories was so much easier with LLM than any app I had used before. Also of course it didn’t matter if I was exactly on point since it wasn…
Seems that in this case a traditional approach would be more precise and more environmentally efficient to get to the same results.
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.
Why is the AI answering questions without answers then?