| Title | This AI Compressed 'All Human Cooking' Into 2 Megabytes | |
| Date | Sunday May 31, @05:18PM | |
| Author | hubie | |
| Topic | ||
| from the foodie-is-as-foodie-does dept. | ||
A London startup trained an AI on 4.1 million recipes across seven languages
- KAIKAKU.AI published Epicure, a family of three ingredient AI models trained on 4.14 million multilingual recipes.
- The model doesn't store recipes—it stores what was learned from them, letting users navigate cooking knowledge mathematically.
- Three variants—Cooc, Chem, and Core—sit at different points on a recipe-context vs. flavor-chemistry spectrum, each answering a slightly different culinary question from the same 2MB file.
Josef Chen says he compressed all of human cooking into two megabytes. That's a bold claim. It also checks out.
Chen, co-founder and CEO of London food AI startup KAIKAKU.AI, published a paper on arXiv this week, alongside researcher Jakub Radzikowski, presenting Epicure—three AI models trained on 4.14 million recipes pulled from 11 datasets across seven languages. The result: a map of 1,790 ingredients, each described by 300 numbers, ...
[...]
Think of it as a map. Every ingredient gets a precise location based on how it behaves across millions of real dishes worldwide. The math is blunt: 1,790 ingredients × 300 numbers per ingredient × 4 bytes each ≈ 2.05 megabytes. Those numbers encode which ingredients appear together, which share flavor compounds, and which belong to the same culinary tradition. Once the model learns all that from the recipes, the recipes can go. The knowledge lives in the coordinates.
This is essentially the same trick word2vec pulled on language back in 2013, when Google researchers showed that you could do arithmetic with meaning. Epicure does that for food. Take beef, point it toward America and you'll get bread, lettuce, maybe beer. Point it toward South East Asia and the model stops thinking about burgers and grills and starts thinking about soy sauce, ginger, and sesame oil.
[...]
Epicure comes in three versions, and picking the right one depends on what you're actually asking. Cooc learns from recipe co-occurrence—what shows up together in real dishes. Chem learns from flavor chemistry—which ingredients share aroma compounds from the FlavorDB chemical database. Core is a mix between the previous two.
[...]
Why this isn't ChatGPT for food
Epicure has no general knowledge, no language generation, and no ability to hallucinate an ingredient it's never seen. It knows 1,790 ingredients. That's the whole world, as far as this model is concerned. What it gives up in breadth it gains in reliability—unlike recipe chatbots that will confidently suggest poison as a cooking ingredient if you push them the wrong way.
[...]
Practical uses aren't hard to picture. A chef asks what the East Asian equivalent of a Mediterranean ingredient looks like. A food product developer asks what minimally processed swap lands in the same flavor zone as an additive. A recipe app needs a coherent substitution when an ingredient is missing from the pantry.
The Epicure paper is a research release. The trained models are live on Hugging Face and an interactive ingredient map is publicly accessible at epicure.kaikaku.ai. They even released an MCP for your agents. Full training code is not released at this time.
I would clarify it to "All Modern Human Cooking", as the ingredients don't include woolly mammoth nor dodo. But it does have bison.
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printed from SoylentNews, This AI Compressed 'All Human Cooking' Into 2 Megabytes on 2026-06-12 14:06:47