{"id":122301,"date":"2020-12-17T16:12:00","date_gmt":"2020-12-17T16:12:00","guid":{"rendered":"https:\/\/news.microsoft.com\/?p=440402"},"modified":"2020-12-17T16:12:00","modified_gmt":"2020-12-17T16:12:00","slug":"the-perfect-cheeto-how-pepsico-is-using-microsofts-project-bonsai-to-raise-the-snack-bar","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2020\/12\/17\/the-perfect-cheeto-how-pepsico-is-using-microsofts-project-bonsai-to-raise-the-snack-bar\/","title":{"rendered":"The perfect Cheeto: How PepsiCo is using Microsoft\u2019s Project Bonsai to raise the (snack) bar"},"content":{"rendered":"

Once the developers had created that simulation framework, the AI algorithm learns through trial and error as well as feedback from operators \u2013 a process called reinforcement learning<\/a>. In the simulation, the AI solution can simulate a day\u2019s run in a mere 30 seconds.<\/p>\n

That means the AI solution has easily gone through more simulated runs than an operator could see in many lifetimes. And its computing power means it can come up with the right option far faster. Plus, it learned from the company\u2019s most skilled operators and Cheetos experts, so it\u2019s monitoring the fluctuations in quality and productivity from the highest level of experience.<\/p>\n

The AI solution \u201ccould encapsulate the knowledge and skill of the best operators, then apply that through other facilities,\u201d says Jayson Stemmler, a technical project manager at Neal Analytics who worked on the PepsiCo pilot project. \u201cThis solution reveals interactions and relationships that might not be intuitive to operators but that exist in the data. Without the manual measurement process, PepsiCo\u2019s engineers are able to be more efficient with their time and focus on breakthrough innovation.\u201d<\/p>\n

\"A<\/p>\n

A few bad Cheetos?<\/strong><\/h2>\n

After the solution spent some time in its simulation proving ground, it was time to take it to a test plant in PepsiCo\u2019s Plano facility to see how it did with the real thing, which means testing it with some imperfect Cheetos.<\/p>\n

\u201cTo develop this technology, we need to be able to make product that\u2019s not good, so the AI can learn to take the product back into spec,\u201d says Sean Eichenlaub, a senior principal engineer at PepsiCo.<\/p>\n

Personally, I don\u2019t see how any Cheetos could be \u201cnot good,\u201d but I understand PepsiCo is going for perfect.<\/p>\n

With the computer vision system continually monitoring and sending data to the Project Bonsai solution, any variance from that ideal can be fixed ASAP.<\/p>\n

\u201cWith faster corrections, we can avoid the potential issues of going out of spec, such as having to discard product, or problems with packaging and waste,\u201d Eichenlaub says.<\/p>\n

I, for one, am all for a bag full of perfect Cheetos. And while the company prepares to use this Project Bonsai solution at a production plant, it\u2019s also looking into using it with other Frito-Lay products, including the even-more-complex tortilla chip.<\/p>\n

Leah Culler edits Microsoft\u2019s AI Blog for Business & Technology.<\/em><\/p>\n

Related:<\/strong><\/h3>\n","protected":false},"excerpt":{"rendered":"

Once the developers had created that simulation framework, the AI algorithm learns through trial and error as well as feedback from operators \u2013 a process called reinforcement learning. In the simulation, the AI solution can simulate a day\u2019s run in a mere 30 seconds. That means the AI solution has easily gone through more simulated […]<\/p>\n","protected":false},"author":2,"featured_media":122302,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[135,50],"class_list":["post-122301","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-news","tag-artificial-intelligence","tag-recent-news"],"_links":{"self":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/122301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/comments?post=122301"}],"version-history":[{"count":0,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/122301\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media\/122302"}],"wp:attachment":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media?parent=122301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/categories?post=122301"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/tags?post=122301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}