4 ms·

I feel like generating recipe ideas/titles is the easy part, and advanced AI techniques are not necessary. Even the article notes that there is not really any d

I feel like generating recipe ideas/titles is the easy part, and advanced AI techniques are not necessary. Even the article notes that there is not really any difference between 1 round of training and 40 rounds of training with their model. Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. I think the best you could do is generate a possible recipe that highlights ingredients and techniques you'll need to experiment with. Also some of their final ideas don't really make sense: - "Carb smart mexican beef and capsicum stuffed peppers with fries , avo and sour cream". Carb smart but it comes with fries? And "capsicum stuffed peppers" is a strange way to phrase it. - "One - pan beef meatloaf italiano with green peas and spinach". You can cook this in one pan if you really want to but it will not be good. - " little ears " pasta serves as balsamic tomatoes. Strange phrasing. A do think a different model could solve these problems, but coming up with generic recipe ideas is not really a problem anyone has.

guhidalg 3y ago
One problem I have is recipe clustering by ingredients. I want to minimize the different ingredients I need to buy so I can get some economies of scale while shopping. I don't know of any service that does this, I have been thinking about building it for my website but I haven't figure out what is a good UI for it.
- ForestCritter 3y ago
  better homes and garden cookbook with the red and white checkered cover. The basics are all there and the ingredient lookup is in the index. Say I lookup potato, all the potato recipes are in the index. It also offers substitutions. I have an older version and a newer version.
kastnerkyle 3y ago
There was a really nice post on doing this kind of thing with CRF back in 2015 [0]. Open source data, and code on github. Also a nice tutorial on structured prediction using CRF type models. Would be interesting if you could prompt, LoRA distill, or use modern LLM tricks against a well-labeled and curated set, similar to how other tagging problems are handled with modern pretrained models. [0] https://open.nytimes.com/our-tagged-ingredients-data-is-now-on-github-f96e42abaa1c https://open.nytimes.com/our-tagged-ingredients-data-is-now-...
tornato7 3y ago
I get all of my recipes from GPT-4 these days. Granted, most of them are just regurgitating a common recipe, but I find that it's able to tweak recipes appropriately if I say "I'm out of X, can I use Y or Z instead?" - I've never actually had a bad recipe come of it.
- scudsworth 3y ago
  post a picture of your dinner
  - gffrd 3y ago
    "steak frites lasagna with cream base and avocado mash tartare"
- deleted 3y ago
  [deleted]
- deleted 3y ago
  [deleted]
- giraffe_lady 3y ago
  I mean if you can cook already you can do whatever you want. I could make a decent meal from a recipe in a language I don't speak just off a handful of cognates. Doesn't mean I followed the recipe in a meaningful sense. When I've looked at gpt recipes they've had some pretty deranged proportions and you'd be in trouble following them precisely. If you just need a list of ingredients and a loose technique though eg "tofu & cashew stir fry with soy sauce and garlic" then ya sure I mean absolutely.
- swatcoder 3y ago
  If you think about how it works, it's going to have a strong representation of oft-published recipes and oft-published substitutions and will be able to mash those together into plausible original recipes that are adjacent to them. Making a satisfying meal for a few people with comparatively mainstream tastes should be pretty reliable. But the more esoteric you go, aiming for something really novel or something unusually historic/traditional/regional, or the more technical and delicate, the more it'll turn to hallucinating naive text continuations that will lead you astray. An experienced cook will know to spot the weird stuff and revise on the fly; an inexperienced one is going to end up with some... odd dishes. It's the same as many of us see when used for code assistance. It's great at inventing "original" boilerplate which inspiration has been exhaustively covered in source material, but gets real wacky and unreliable the more you move away from that.
famouswaffles 3y ago
>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.
- swatcoder 3y ago
  I can't imagine any cook or chef who believes that all the necessary information for all good recipes has been captured in text, let alone in the text that's been digitized and made available for training. That's not to say that LLM's can't mash together reasonable new recipes for certain audiences, especially for variations on popular modern standards, but the idea that their working space of recipes is exhaustive across all cuisine and tastes is absurd.
  - famouswaffles 3y ago
    It doesn't matter what the cook thinks. Predictors don't just grok what is explicitly stated in a dataset but also what is implied by its structure. In a language model trained on protein sequences alone and nothing else, you will find biological structure and function emerge in the inner layers. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 Language Models are predictors. If the text you give them is the shadow in Plato's cave, they don't try to draw shadows, they try to build walls. They are trying to reverse engineer the computation that must have led to that output. They will not stop at "surface level similarity" or "plausible" by choice. They will train until they have completely succeeded or until the architecture or data fail them. With a capable enough architecture and sufficient data, there is no recipe a predictor couldn't divine with enough training. For the perfect predictor of recipes, Is the architecture capable enough ? Is the data sufficient (both variance and quantity) ? I'm not sure but this is not a question a cook can answer. The OP claimed >Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. This is just false. And you don't need the hypothetical perfect predictor (just a capable one) to see it.
nick_rocks 3y ago
seems like there's a high potential for the AI to hallucinate and get confused with ingredient measurements... last I checked, LLMs struggle with numbers.

guhidalg 3y ago

One problem I have is recipe clustering by ingredients. I want to minimize the different ingredients I need to buy so I can get some economies of scale while shopping. I don't know of any service that does this, I have been thinking about building it for my website but I haven't figure out what is a good UI for it.

ForestCritter 3y ago
better homes and garden cookbook with the red and white checkered cover. The basics are all there and the ingredient lookup is in the index. Say I lookup potato, all the potato recipes are in the index. It also offers substitutions. I have an older version and a newer version.

kastnerkyle 3y ago

There was a really nice post on doing this kind of thing with CRF back in 2015 [0]. Open source data, and code on github. Also a nice tutorial on structured prediction using CRF type models. Would be interesting if you could prompt, LoRA distill, or use modern LLM tricks against a well-labeled and curated set, similar to how other tagging problems are handled with modern pretrained models. [0] https://open.nytimes.com/our-tagged-ingredients-data-is-now-on-github-f96e42abaa1c https://open.nytimes.com/our-tagged-ingredients-data-is-now-...

tornato7 3y ago

I get all of my recipes from GPT-4 these days. Granted, most of them are just regurgitating a common recipe, but I find that it's able to tweak recipes appropriately if I say "I'm out of X, can I use Y or Z instead?" - I've never actually had a bad recipe come of it.

scudsworth 3y ago
post a picture of your dinner
- gffrd 3y ago
  "steak frites lasagna with cream base and avocado mash tartare"
deleted 3y ago
[deleted]
deleted 3y ago
[deleted]
giraffe_lady 3y ago
I mean if you can cook already you can do whatever you want. I could make a decent meal from a recipe in a language I don't speak just off a handful of cognates. Doesn't mean I followed the recipe in a meaningful sense. When I've looked at gpt recipes they've had some pretty deranged proportions and you'd be in trouble following them precisely. If you just need a list of ingredients and a loose technique though eg "tofu & cashew stir fry with soy sauce and garlic" then ya sure I mean absolutely.
swatcoder 3y ago
If you think about how it works, it's going to have a strong representation of oft-published recipes and oft-published substitutions and will be able to mash those together into plausible original recipes that are adjacent to them. Making a satisfying meal for a few people with comparatively mainstream tastes should be pretty reliable. But the more esoteric you go, aiming for something really novel or something unusually historic/traditional/regional, or the more technical and delicate, the more it'll turn to hallucinating naive text continuations that will lead you astray. An experienced cook will know to spot the weird stuff and revise on the fly; an inexperienced one is going to end up with some... odd dishes. It's the same as many of us see when used for code assistance. It's great at inventing "original" boilerplate which inspiration has been exhaustively covered in source material, but gets real wacky and unreliable the more you move away from that.

famouswaffles 3y ago

>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.

swatcoder 3y ago
I can't imagine any cook or chef who believes that all the necessary information for all good recipes has been captured in text, let alone in the text that's been digitized and made available for training. That's not to say that LLM's can't mash together reasonable new recipes for certain audiences, especially for variations on popular modern standards, but the idea that their working space of recipes is exhaustive across all cuisine and tastes is absurd.
- famouswaffles 3y ago
  It doesn't matter what the cook thinks. Predictors don't just grok what is explicitly stated in a dataset but also what is implied by its structure. In a language model trained on protein sequences alone and nothing else, you will find biological structure and function emerge in the inner layers. https://www.pnas.org/doi/full/10.1073/pnas.2016239118 https://www.pnas.org/doi/full/10.1073/pnas.2016239118 Language Models are predictors. If the text you give them is the shadow in Plato's cave, they don't try to draw shadows, they try to build walls. They are trying to reverse engineer the computation that must have led to that output. They will not stop at "surface level similarity" or "plausible" by choice. They will train until they have completely succeeded or until the architecture or data fail them. With a capable enough architecture and sufficient data, there is no recipe a predictor couldn't divine with enough training. For the perfect predictor of recipes, Is the architecture capable enough ? Is the data sufficient (both variance and quantity) ? I'm not sure but this is not a question a cook can answer. The OP claimed >Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. This is just false. And you don't need the hypothetical perfect predictor (just a capable one) to see it.

nick_rocks 3y ago

seems like there's a high potential for the AI to hallucinate and get confused with ingredient measurements... last I checked, LLMs struggle with numbers.