Eye on AI - April 2nd, 2021
Welcome to Aigora's "Eye on AI" series, where we round up exciting news at the intersection of consumer science and artificial intelligence!
This week, we’ll be looking at AI in food production and delivery, and will be focusing on two stories in particular that show how food retailers and producers alike are continuing to utilize AI to make food production and distribution smarter, cleaner, and more autonomous.
Chipotle’s Investment in Self-Driving Vehicle Foreshadows Ambitions
We begin with The Spoon article “Chipotle Invests in Self-Driving Delivery Vehicle Company Nuro,” which, as the title suggests, details Chipotle’s investment in the autonomous vehicle company Nero as part of its Series C round of funding.
“Nuro’s low-speed vehicles [can] travel faster and can go farther than a sidewalk robot like Starship or Kiwibot,” writes The Spoon’s Chris Albrecht. “Plus, Nuro’s technology has gotten approvals from both the federal government and the State of California. Nuro’s self-driving pods have actually been operating without human intervention for some time now.”
The move comes on the heels of a slew of digital enhancements that allowed Chipotle’s digital growth to increase 174% in 2020. That’s quite impressive considering we were in a pandemic year, less so when you consider that many fast-food chains exceeded pre-pandemic sales predictions by the end of 2020. But not all fast food companies saw their sales increase.
One reason –– perhaps the main reason –– why some fast food companies saw pandemic success while others floundered is the utilization of AI-enabled contactless capabilities. In almost every case, the chains that got food safely, efficiently, and with less human contact were all big winners, Chipotle among them.
“Chipotle has been playing the digital long game, adding features like advanced ‘Chipotlanes’ for drive-thru customers,” continues Albrecht. “... Chipotles also got into the ghost kitchen game last November as dining rooms remained closed thanks to COVID… Tying these threads together, it’s not hard to see how digital ordering, high-tech drive-thrus, ghost kitchens and self-driving vehicles could all work together. An autonomous Chipotle pod pulls into a Chipotlane, a human (or conveyor belt!) puts the order in the pod, which then drives off to make the delivery. Repeat all day long.”
It doesn’t take a machine-learning algorithm to predict what will likely happen next: Fast food chains will continue to expand AI capabilities, inching toward full autonomy; mom and pop restaurants (and those reluctant to implement AI) will suffer, and slowly but surely the world will shift away from dine-in to remote contactless consumption.
For more on this, see our post from March 5th on McDonald’s new AI drive-thru, or this post from last year on how fast food was beginning to use the pandemic to push into full autonomy.
AI Is Reshaping Food Production
Of course, food sales can’t increase if food production doesn’t match the demand. According to the EurekaAlert! article “How artificial intelligence is helping make food production smarter”, food production workers are also beginning to utilize AI to increase output in big ways.
Food production is the monitoring and management of raw materials, supply chains, market prices and more, and addresses the delicate relation between supply and demand. Effective food production requires the dissection of immense amounts of data. By utilizing AI to help understand that data, companies can help ensure the right food is in supply at the right time.
“Food production generates vast amounts of data, much of which has so far gone unused,” says Wolfgang Maaß, Professor of Business Informatics at Saarland University and the German Research Center for Artificial Intelligence. “That data represents unemployed capital... Once these data sets have been linked, analysed and evaluated, they can provide concrete recommendations for action whenever key production decisions need to be made.”
One technology that is helping producers understand their data is 'Evarest', a research project led by Maaß. ‘Evarest’ uses machine learning to link their production data with general data such as weather forecasts, price indices, and plant or equipment data from other companies. By feeding their data into Evarest, producers can understand data trends, giving them the ability to predict things like harvest production to understand supply and demand relationships. This gives them the ability to make informed decisions about production capacity and increase revenue streams.
“The data platform that the research team has developed enables food producers to retrieve information that is fact-based and sector-specific,” writes the Saarland University editorial team. “The synopsis and the recommendations generated by the system provide users with an array of parameters that can be adjusted to optimize their production processes.”
'Evarest' research project received €2.3 million in funding through the 'Smart Data Economy' technology programme. It’s not the only AI company delving into food production, and with good reason –– food production is big business. Expect producers looking for an edge to utilize ‘Evarest’ or similar technology.
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