Just because Amazon, king of scams, is doing an AI scam, that doesn’t mean that the underlying technology is impossible to use with minimal errors (it’s AI, it’s made of statistics, there will always be some errors).

Anyways, “just walk out” works in a different way than the fruit recognition in the OP or the checkout machines I was talking about. Image recognition of a discrete item over a white background (or a checkered background) is like, the literal ideal case for image recognition accuracy. This is as opposed to blurry store cameras looking at an entire aisle from 20 feet away and trying to guess what item the customer is taking off the shelf. It’s an entirely different problem space in every way that matters.

Anyways, even ignoring theoretical arguments, I know it’s production-ready because it’s currently beong used in production. There are dozens of stores in Calofornia right now that use checkout machines with a camera that points down towards a plain background “pad”. You place the item on the pad and it selects the most likely item in the store based on what it sees. I’ve seen a live demo of these machines where you take ~10-15 pictures of an item from different angles/rotations/positions and add it to the list of recognizable items, and the machine was able to diatinguish between that item and others accurately. This was in a very candid and scam-unlikely environment (OpenSauce) and by my evaluation this is easily consistent with other known-good image recognition applications.

The Amazon shop is a lot more complicated than a few berries on a white shelf.

[Sorry, double posted, my mobile connection is pretty bad rn]

Just because Amazon, king of scams, is doing an AI scam, that doesn’t mean that the underlying technology is impossible to use with minimal errors (it’s AI, it’s made of statistics, there will always be some errors).

Anyways, “just walk out” works in a different way than the fruit recognition in the OP or the checkout machines I was talking about. Image recognition of a discrete item over a white background (or a checkered background) is like, the literal ideal case for image recognition accuracy. This is as opposed to blurry store cameras looking at an entire aisle from 20 feet away and trying to guess what item the customer is taking off the shelf. It’s an entirely different problem space in every way that matters.

Anyways, even ignoring theoretical arguments, I know it’s production-ready because it’s currently beong used in production. There are dozens of stores in Calofornia right now that use checkout machines with a camera that points down towards a plain background “pad”. You place the item on the pad and it selects the most likely item in the store based on what it sees. I’ve seen a live demo of these machines where you take ~10-15 pictures of an item from different angles/rotations/positions and add it to the list of recognizable items, and the machine was able to diatinguish between that item and others accurately. This was in a very candid and scam-unlikely environment (OpenSauce) and by my evaluation this is easily consistent with other known-good image recognition applications.

Good point.

I thought I missed some joke in the comments.