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Food and Generative AI

engineering.hellofresh.com

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Re: Food and Generative AI

#71

Post by famouswaffles »

uoaei wrote: post #68
famouswaffles wrote: post #63

Earlier quoted context omitted.

You misunderstand what a language model is. What a human can or cannot do is frankly irrelevant. A GPT (Generative pretrained Transformer) is not a simulator nor an imitator. It is a predictor . Predictors try to reverse engineer the computation that could have led to a certain output so they don't just grok what is explicitly stated in a dataset but also what is implied by its structure. As an example, In a language…

I don't think the commenter's understanding of LLMs is relevant here. It's what the authors of TFA think LLMs are for and, arguably more importantly, what kinds of problems (nails) they think about when presented with the idea of applying LLMs (hammer). It's a bit ridiculous that this is the best they can come up with, and I don't really think the idea is worth defending, especially by telling other people that they…

His/Her primary assertion is that it is impossible for a LLM to generate novel recipes without physically tasting it first. His/her assertion is wrong and one easily corrected by understanding a LLM is and does.

It's not a perfect predictor so it's not impeccable for all conceivable situations but GPT-4 can already generate novel recipes that taste nice. This is not some far flung science fiction ability.

Re: Food and Generative AI

#72

Post by [deleted] »

famouswaffles wrote: post #63
burkaman wrote: post #54

Earlier quoted context omitted.

I don't agree, even a human chef with decades of experience in all the tools and ingredients they're using is not going to get every recipe right on the first try. I'm not saying an AI recipe will never be good, but it is going to make mistakes sometimes. If you're wondering whether a task is theoretically possible for an AI, I think a good rule of thumb is to ask "could a human domain expert do this given a few days…

You misunderstand what a language model is. What a human can or cannot do is frankly irrelevant. A GPT (Generative pretrained Transformer) is not a simulator nor an imitator. It is a predictor . Predictors try to reverse engineer the computation that could have led to a certain output so they don't just grok what is explicitly stated in a dataset but also what is implied by its structure. As an example, In a language…

[deleted]

Re: Food and Generative AI

#73

Post by reidjs »

Unrelated to the article, but I have had great success using LLMs to generate recipe. Here's how it goes: "I have a ton of onions, what can I make" (LLM provides like 4 options) "Ok, give me a recipe for French Onion Soup" (LLM provides recipe) "I don't want to use my oven for the bread, can I use my air fryer?" (LLM provides an alternative approach using the air fryer instead)

Thanks LLM! That was way better than some awful google recipe SEO spam nonsense.

Re: Food and Generative AI

#74

Post by NeoTar »

chefandy wrote: post #58

It's kind of funny-- I'm a technical visual artist, classically trained (former) chef, and spent a long time as a web developer in an environment where people breathlessly spoke about the possibility of LLMs. (Auto-correcting OCR was my pet wish that I never fulfilled.) It's been... interesting seeing layman's usually naive and mistaken takes on generative AI performing these functions. The difficult part of recipe d…

> and use volumetric measurements instead of weights

Sorry to pick up on one sentence of an interesting long comment, but this is a pet peeve on mine when having to rely on American recipes. There seems to be an obsession on using volumetric measurements for things which really should be done in mass units - flour, sugar, sometimes even ingrediants like grated cheese.

European recipes in my experience tend to use most 'natural' units - mass for solids, volume for liquids - but you still get the silly culturally specific units sometimes - '1 medium onion' ... well how big is that? Would two small onions be too much? Or half a large onion?

Re: Food and Generative AI

#75

Post by ska »

smith7018 wrote: post #70
famouswaffles wrote: post #39

Earlier quoted context omitted.

>Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. It's not because this necessary information is already implicit in the sum of all the data it is trained on.

My friend has a restrictive diet for health reasons (can't eat certain greens, no gluten, etc) and decided to use ChatGPT to come up with a dessert for her dinner party. It was honestly one of the worst things I've ever tried. She verified that the recipe wasn't regurgitated from another source so it was all ChatGPT's doing.

This isn't really surprising. The source material it's trained on isn't particularly good, and interpolating between them at all generally doesn't work well.

Re: Food and Generative AI

#76

Post by famouswaffles »

smith7018 wrote: post #70
famouswaffles wrote: post #39

Earlier quoted context omitted.

>Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. It's not because this necessary information is already implicit in the sum of all the data it is trained on.

My friend has a restrictive diet for health reasons (can't eat certain greens, no gluten, etc) and decided to use ChatGPT to come up with a dessert for her dinner party. It was honestly one of the worst things I've ever tried. She verified that the recipe wasn't regurgitated from another source so it was all ChatGPT's doing.

That just means you used a predictor that wasn't good enough (was this at least 4 ? or 3.5 ?) not that a GPT-X would need to physically taste recipes to generate novel recipes that tasted good.

GPT-2 was mostly an incoherent babbling mess but that didn't mean a better predictor couldn't be coherent.

GPT-3 could not play chess at all but that didn't mean a better predictor couldn't play chess (3.5-turbo-instruct)

Taste is implicit in recipes so a good enough predictor has to model it somehow to succeed, no physical experimentation necessary.

Re: Food and Generative AI

#77

Post by gffrd »

TeMPOraL wrote: post #61
gffrd wrote: post #46

Earlier quoted context omitted.

Are you thinking "Rather than having a cook compose recipes with 1000 variations based on permutations of allergies, just produce one recipe with intended outcome, and let an AI figure out the variants"? How do you train an AI on taste? Would a chef state what's allowable for a recipe? "This one you can sub the soy out, but this one falls apart without it"

> How do you train an AI on taste? You keep shoveling piles of recipes at it, and as long as most of the recipes are positively tasteful (as opposed to just randomly generated, or worse, engineered to suck out of spite), the AI should eventually pick up on taste in general.

One thing I’m curious about …

Having done a lot of user research, sometimes the most important things to a person aren’t stated, or even consciously known.

If I’m shopping for chocolate chip cookies, they’re a proxy for another need: hunger, yes, but also emotions: wanting to feel taken care of, missing a place, etc. If chocolate chip cookies are unavailable, I’m not now considering snickerdoodles or shopping chocolate bars; I’m shopping for the feel: maybe it’s pie, maybe soup.

I’m interested to see if the AIs we build will have the ability to identify this, so when someone says “I want a steak frites” the AI doesn’t strictly recommend steak recipes—“they’re asking for steak”—but realized the constellation of what the asker is actually asking for—they’re asking for a recipe that reminds them of when they lived in that apartment in that city X years ago.

It will be fun to see what’s possible …

Re: Food and Generative AI

#78

Post by ska »

famouswaffles wrote: post #71
uoaei wrote: post #68

Earlier quoted context omitted.

I don't think the commenter's understanding of LLMs is relevant here. It's what the authors of TFA think LLMs are for and, arguably more importantly, what kinds of problems (nails) they think about when presented with the idea of applying LLMs (hammer). It's a bit ridiculous that this is the best they can come up with, and I don't really think the idea is worth defending, especially by telling other people that they…

His/Her primary assertion is that it is impossible for a LLM to generate novel recipes without physically tasting it first. His/her assertion is wrong and one easily corrected by understanding a LLM is and does. It's not a perfect predictor so it's not impeccable for all conceivable situations but GPT-4 can already generate novel recipes that taste nice. This is not some far flung science fiction ability.

This is true. On the other hand, the recipes aren't typically good, and generating novel ones isn't really what people mostly want to do... so the value seems limited.

Re: Food and Generative AI

#79

Post by burkaman »

famouswaffles wrote: post #63
burkaman wrote: post #54

Earlier quoted context omitted.

I don't agree, even a human chef with decades of experience in all the tools and ingredients they're using is not going to get every recipe right on the first try. I'm not saying an AI recipe will never be good, but it is going to make mistakes sometimes. If you're wondering whether a task is theoretically possible for an AI, I think a good rule of thumb is to ask "could a human domain expert do this given a few days…

You misunderstand what a language model is. What a human can or cannot do is frankly irrelevant. A GPT (Generative pretrained Transformer) is not a simulator nor an imitator. It is a predictor . Predictors try to reverse engineer the computation that could have led to a certain output so they don't just grok what is explicitly stated in a dataset but also what is implied by its structure. As an example, In a language…

I understand what you're saying, I just think we have a fundamental disagreement over "taste is implicit in the data" that I'm not sure we can resolve here. Intuitively it makes sense to me that if you know the entire sequence of a protein's fundamental building block then you can deduce its higher-level properties, but it does not make sense that if you know a lot of high-level descriptions of an ingredient and how it tastes then you can deduce behaviors like how it will react to a certain application of heat or another ingredient.

I appreciate your links though, I think this is the first I've heard of an LLM doing something that a human can't do. I can't claim to understand a lot of the first paper and I can't access the second, but assuming your descriptions are right I agree that my rule of thumb was wrong.

Re: Food and Generative AI

#80

Post by keenmaster »

__MatrixMan__ wrote: post #55
TheHumanist wrote: post #38

Earlier quoted context omitted.

This is exactly how I feel. I want it to actually be helpful. It needs to be able to: - track what I have (if I have input that data) and how soon anything may expire - track what I made but haven't finished eating (just a few quick pictures in the fridge should update that as needed, or maybe it's time we all get that Alton Brown in-fridge camera to talk to) - can I use this in something else? How long should it sti…

As complex as it might be, the "intelligence" would probably be the easy part. Aggregating that data in a way that didn't become a burden for the user--either because its tedious, or because they don't trust you--would be quite a trick.

“Burden for the user” - why not make this a burden for the AI instead? Have the AI do some web-crawling.
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