ANGELA D’ORAZIO – MASTER BLENDER, MACKMYRA:
“We always strive to challenge the traditions in the very traditional whisky trade and that’s something we really do now when we develop a whisky with the help of AI. We see AI as a part of our digital development, it is really exciting to let AI be a complement to the craft of producing a high-quality whisky. For me as a Master Blender it is a great achievement to be able to say that I’m now also a mentor for the first ever created AI whisky in the world” says Angela D’Orazio, Mackmyra.
MICROSOFT SAYS:
“This AI-generation can have an impact in different industries globally. I envision AI systems generating recipes for sweets, perfumes, beverages, and maybe even sneaker designs. Many of these have already been attempted, but large-scale adoption is still lagging behind. We are showing the way forward, and these new AI solutions can be used to generate products that retain the spirit, look and feel of the brands behind them, while at the same time being new and unique.”
Utilizing AI is not only faster than a person carrying out the process manually, but thanks to the algorithm’s ability to sift through and calculate a vast amount of data, new and innovative combinations that would otherwise never have been considered can be found. However, the AI solution is not designed to replace a Master Blender. The idea is for the whisky to be generated by AI and still be curated by a person.
Jarno Kartela – Machine Learning Partner, Fourkind:
“Algorithms don’t have senses so we need another take on how to understand something so complex as whisky. Although lacking human expertise, we can teach machines to understand what elements previous recipes and products are made of and how they are perceived and ranked by customers and experts. With this as a raw data asset, we can leverage a combination of explorative algorithms to generate endless new recipes and products and then use a set of discriminative algorithms to understand which of them might be great, repeating until better recipes are not found. This requires a lot from the computation side, as we need millions of iterations while keeping track of what worked and what did not before reaching a solid guess of a good new whisky.”
