Better Beer Through GPUs: How GPUs and Deep Learning Help Brewers Improve Their Suds
J Thoendell stashed this in Food
Four years ago, Cohen was grappling with a problem that will be familiar to any data scientist. To get meaningful insights for the institute he needed more data. And to get it, he had to beg the college students around him to slurp tea and record their impressions. Not easy.
A Business Built on Free Beer
Thatʼs when it hit Cohen: forget tea. Heʼd build his data set by offering free beer. Volunteers packed into his tastings, scribbling down their impressions of whatever suds Cohen served them. Bitter India pale ales. Crisp pilsners. Malty, chocolatey doppelbocks. They inhaled the two- to three-ounce portions.
Within weeks, Cohen had a trove of data that started yielding insights. He could use the data to identify flaws in beers. Beer that tastes like fresh-cut grass, for example, reveals too much of a compound called cis-3-hexenol. Thatʼs caused when hops used in a beer are stale. Itʼs something any brewer will want to know right away.
With every chug, Jason Cohen’s data set grows larger.
Better still, Cohen could tease out insights that might escape the taster. A novice drinker, for example, may not know the difference between a good beer and one that has been “skunked” — giving the beer a manure-like flavor — because of exposure to too much light. But, by analyzing a drinkerʼs impressions of a beer, Cohen can. Better yet, he could predict what demographic groups would like a beer.
Thatʼs when Cohen realized he didnʼt have a research project. He had a business. Turns out 11 percent of all U.S. beer sales by volume last year came from small brewers. Better still, these fast-growing brewers are guzzling more than their share of sales, grabbing 19 percent of the beer industryʼs $101.5 billion in retail sales.