One stout to rule them all: Reconciling artificial intelligence, data science and malted alcoholic beverages

Abstract

Beer is a phenomenal beverage. It has previously shaped the history of many peoples, states and cultures. The beauty of beer is its versatility. Starting from the original implementations that were murky or diluted, over time researchers found novel approaches to gradually develop beverages that are diverse, intense and are pleasant for the end user. Recently, the industry came up with the so-called \textit{craft beers}, that often differ from the commercial beers in production volume (due to lower capacities of the craft beer producers) and tasting profile (often having more intense unusual flavours). However, while it is often relatively easy to judge if a particular commercial beer is likely to be enjoyable, the same cannot be said about craft beers, as there are far too many styles, implementations and ingredients involved in their production. This creates a gap between the beverage producers and the consumers due to the inability of the former to judge the preferences and the consumption trends of the latter. As a response to this challenge we present a novel collaborative beverage-related data collection and analysis framework - the Distributed Beverage Analysis (DBA). The idea behind this study is to identify the common trends and support them by empirical evidence to better understand the needs of the consumers. We empirically verify DBA at the biannual \textit{Kraft Bier Fest} conducted by Vienna Kraft brewery in (you guessed it) Vienna. To showcase a need in such kind of analysis, we evaluate various large language models (LLMs) against our collaborative framework and confirm that many AI models cannot be reliably used to reason over the trends and patterns in the evolving world of craft beer.