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>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

>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.
  - thuuuomas 3y ago
    > It doesn't matter what the cook thinks. This is the fantasy of GenAI boosters stated concisely - that these statistical methods present a new objectivity that avoids the shortcomings of human subjectivity. Of course, these web-scraped models are distilled subjectivity in aggregate, subjectivity at scale. ChatGPT is made of people!
burkaman 3y ago
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 and as much research material as they need". If the task here is "develop a novel recipe without trying it", the answer is no. You can't predict the interactions of every possible combination of ingredients and processes no matter how much time and experience you have.
- petra 3y ago
  >> You can't predict the interactions of every possible combination of ingredients and processes no matter how much time and experience you have. That's just the thing machines do better than humans. Not sure about LLM's though.
- famouswaffles 3y ago
  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 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 In fact, with a capable enough architecture and sufficient data (quantity, qualit, variance), there is nothing a predictor couldn't divine with enough training. You assert >Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. In the pursuit of recipe prediction, a predictor will learn to internally model taste because taste is implicit in the data. You don't need the hypothetical perfect predictor to demonstrate your assertion as false because a capable predictor already does. >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 and as much research material as they need". You are wrong. Train a language model on descriptions of the functions of proteins and an equivalent protein sequence and you get a language model that can predict novel functioning sequences from function descriptions alone. https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Not only is this not something a human expert can achieve in a couple days, It's not something a human expert can achieve at all. It is a Super-Human ability.
smith7018 3y ago
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.
- famouswaffles 3y ago
  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.
- ska 3y ago
  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.
deleted 3y ago
[deleted]
ForestCritter 3y ago
There are alot of bad recipes floating around out there. Recipe books are often geared to teaching you how to cook not just what to cook. There's techniques to cooking that affect how recipes come out. A good recipe book will teach you how to treat ingredients, how they interact with each other and then give you ideas on how to put them together.

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.
- thuuuomas 3y ago
  > It doesn't matter what the cook thinks. This is the fantasy of GenAI boosters stated concisely - that these statistical methods present a new objectivity that avoids the shortcomings of human subjectivity. Of course, these web-scraped models are distilled subjectivity in aggregate, subjectivity at scale. ChatGPT is made of people!

burkaman 3y ago

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 and as much research material as they need". If the task here is "develop a novel recipe without trying it", the answer is no. You can't predict the interactions of every possible combination of ingredients and processes no matter how much time and experience you have.

petra 3y ago
>> You can't predict the interactions of every possible combination of ingredients and processes no matter how much time and experience you have. That's just the thing machines do better than humans. Not sure about LLM's though.
famouswaffles 3y ago
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 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 In fact, with a capable enough architecture and sufficient data (quantity, qualit, variance), there is nothing a predictor couldn't divine with enough training. You assert >Actually generating a recipe is obviously much harder, and should be effectively impossible unless you include a humanoid robot that can cook and taste. In the pursuit of recipe prediction, a predictor will learn to internally model taste because taste is implicit in the data. You don't need the hypothetical perfect predictor to demonstrate your assertion as false because a capable predictor already does. >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 and as much research material as they need". You are wrong. Train a language model on descriptions of the functions of proteins and an equivalent protein sequence and you get a language model that can predict novel functioning sequences from function descriptions alone. https://www.nature.com/articles/s41587-022-01618-2 https://www.nature.com/articles/s41587-022-01618-2 Not only is this not something a human expert can achieve in a couple days, It's not something a human expert can achieve at all. It is a Super-Human ability.

smith7018 3y ago

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.

famouswaffles 3y ago
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.
ska 3y ago
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.

deleted 3y ago

[deleted]

ForestCritter 3y ago

There are alot of bad recipes floating around out there. Recipe books are often geared to teaching you how to cook not just what to cook. There's techniques to cooking that affect how recipes come out. A good recipe book will teach you how to treat ingredients, how they interact with each other and then give you ideas on how to put them together.