Ask AI what goes with chicken and the answer depends on whether it learned from recipes or molecules
Quick Answer
Kaikaku.AI's 'Epicure' features three AI models that differentiate between recipe-based and chemistry-based ingredient pairings.
Quick Take
Kaikaku.AI's 'Epicure' features three AI models that differentiate between recipe-based and chemistry-based ingredient pairings. Trained on 4.14 million recipes and FlavorDB, the chemistry model excels in taste classification and nutritional values, outperforming recipe-based models despite lacking direct exposure to such data.
Key Points
- Epicure's models trained on 4.14 million recipes in seven languages.
- Chemistry-based model outperforms recipe-based models in taste classification.
- FlavorDB flavor database enhances ingredient pairing accuracy.
- AI models provide distinct recommendations based on training data type.
- Kaikaku.AI aims to innovate culinary AI applications.
Article Excerpt
From source RSS / original summaryWith "Epicure," London-based startup Kaikaku. AI presents three AI models that are the first to clearly separate whether an ingredient fits a recipe or is chemically related. Trained on 4. 14 million recipes in seven languages and the FlavorDB flavor database, each variant returns different recommendations. The purely chemistry-based model even classifies taste and nutritional values better than the recipe-based alternatives, despite never seeing that information directly.
The article Ask AI what goes with chicken and the answer depends on whether it learned from recipes or molecules appeared first on The Decoder.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.
