Artificial intelligence might one day be used to power genuinely humanlike cyborgs or other figments of humanity’s fertile imagination. For now, Ingo Stork is using the technology to help restaurant chains waste less food and do more with fewer workers.
Dr. Stork is co-founder of PreciTaste, a startup that uses AI-based sensors and algorithms to accomplish one fairly specific task: predict how much food people will order at any given moment, and make sure that it’s being prepared in a timely fashion.
The idea—to reduce waste—came in part from a visit Dr. Stork made to a quick-service kitchen one afternoon a few years ago, where he watched a cook fire up 30 burger patties, and then throw them all away when no one showed up to eat them. Why, he wondered, should this cook have to follow that day’s schedule, written in anticipation of a normal day at the restaurant, instead of the slow one it turned out to be?
“Each of those burgers is a 50-mile car ride in terms of CO2 emissions,” he says, referring to the energy required to raise the cows and eventually transform them into burgers. “Think of all the logistics just to get them there, all just to go to waste and be discarded.”
Using AI tools to reduce waste and increase productivity in fast-food joints is hardly the stuff of science fiction. It isn’t as flashy as some of the artificial intelligences that have been getting wider attention lately, such as DALL-E, which can create clever images based on text suggestions, or GPT-3, text-generation software good enough to write scientific papers about itself. And it’s not as likely to make headlines as Google’s LaMDA chatbot, which can produce such humanlike conversation that one of the company’s engineers declared it to be sentient—a notion the company flatly rejected.
But, with a few exceptions, these headline-grabbing systems aren’t having a material impact on anyone’s bottom line yet.
The AI systems that currently matter the most to companies tend to be far more humble. Were they human, they would probably be wearing hard hats and making cameos on the reality show “Dirty Jobs”…
Specialization trumps flexibility
Every fast-food restaurant chain that Dr. Stork’s New York City-based company, PreciTaste, works with presents a new set of challenges for his engineers and the AI-powered restaurant-management systems they build.
“Each food chain has its own menu, operations, equipment and way of handling things,” he says. The array of wall-mounted cameras equipped with machine vision that can track an order from the moment its raw ingredients leave a refrigerator until it’s ready to be handed to a customer may have to be laid out differently, for example. And the number of preparation steps can vary greatly by restaurant.
PreciTaste says it can’t disclose which chains are considering its technology. But it’s working with the commercial-kitchen fabrication giant Franke to pilot its tech in a handful of national fast-food and fast-casual restaurants, says Greg Richards, vice president of business development at the company. (Franke has since the 1970s been a supplier to McDonald’s.)
To make its system work, depth-sensing cameras must be trained to recognize how much of an ingredient—say, rice—remains in a prep tray. Knowing when to replenish it depends on what will happen to demand, which in turn depends on factors including weather and local holidays that might determine whether people will go out to eat and what they’ll order. All of this and more is fed into the same kind of prediction algorithms that help retailers like Amazon manage their logistics networks.
Today’s AI systems lack common sense, can ehave erratically when faced with unexpected events, and have minimal ability to transfer knowledge “learned” from one task to analogous situations. In this way, it could be said that today’s artificial intelligence possesses no intelligence at all—it is, as one AI pioneer put it, just “complex information processing.”
The result is that engineers and data scientists have to do a lot of hand-holding for their fragile AIs, including planning, hardware engineering, and writing software. All that to build a scaffolding within which an AI can be trained to accomplish a set of tasks that have been defined as narrowly as possible.
See full article here.
