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When Jordan Fisher was looking for a way to reinvent an aspect of the physical world he felt was long overdue for change, his gaze quickly turned to the checkout lane.

Fisher, a machine learning expert who is the founder and CEO of autonomous checkout firm Standard AI, believes traditional computer technology is nearing the end of its run as an agent of disruption, in large part because artificial intelligence has the power to bring about massive changes beyond the digital realm.

That realization led him to conclude that computers with the ability to interact with the physical world had the promise to finally revolutionize vast swaths of life that had long been stuck in the past.

“Traditional software’s only really good with digital information, and the vast majority of world and commerce happens in physical reality where traditional software just couldn’t make an impact,” Fisher said in an interview. But with artificial intelligence in hand, “it only takes a couple of minutes before you start stumbling upon something like autonomous checkout, and you’re like, ‘OK, this is huge. Ninety percent retail is physical retail, there’s billions and billions of hours that people just spent waiting in line every year. Can we have an impact that was previously not possible?’”

Standard AI CEO Jordan Fisher

Courtesy of Standard AI

 

Fisher is leading a company that is looking to use its expertise in machine learning and computer vision to eventually eliminate the need for humans to oversee the checkout process in retail locations. For now, Standard AI is focused on smaller retailers like convenience stores, but Fisher believes that the autonomous checkout tech his company and competitors like Amazon, Grabango, Zippin and Trigo have developed will become a common sight across the retail landscape, including in full-size grocery stores.

Fisher noted that he was also drawn to the car industry, which he believes is another example of where machine learning is playing a key role in pushing society ahead, but decided that experimenting with moving vehicles was just too risky.

“You can think of autonomous checkout as an inverted self-driving car, so the cameras are pointing outward instead of inward, and instead of the car moving, the people move,” Fisher said. “But we get this luxury of being able to make a mistake without killing anybody, which is really important.”

A mathematician who worked as a machine learning specialist for the U.S. Securities and Exchange Commission before founding Standard, Fisher recently spoke with Grocery Dive about the future of autonomous checkout.

This interview has been edited for length and clarity.

GROCERY DIVE: Autonomous checkout technology has been around for several years, but has been somewhat slow in showing up stores. What do you think is keeping the technology from faster adoption by retailers?

We picked this application space because we thought it’d be a little bit easier than self-driving cars. But it’s still been a really hard market to start growing. It’s a physical product that requires physical operations to install and to maintain. It’s not like software where you flip a switch and you can reach 100 million people the next day.

To what extent do you think cost is an obstacle?

We’ve been working aggressively to get the price down, and that’s a core requirement. A lot of retailers have a lot of cash flow, but they don’t really have a lot of that to spare for capex on hardware. So getting this to work was step one, which we’ve done, showing that we can start scaling it and make this repeatable is step two, and we’ve done that as well. And now step three is we have to make this a really economical product for everyone involved.

When do you expect to bring your technology to retailers larger than convenience stores?

It’s hard to predict, more because the c-store market for us is huge, and we’ve got our hands full. I think you’re going to start seeing other innovations in grocery even before you get to full autonomous, like identifying produce at self-checkout stations, so we can make a better checkout experience. And I would expect to see that pretty quickly over the next couple of years.

Other than checkout, what other uses for computer vision do you see in the retail arena?

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