amazon spent a lot of money on trying to do this and then found out the technology doesn’t exist and outsourced it to india
Some companies do outsource their “AI” to India, but automated checkout tech is actually good enough to be used in production now. A plain white background with separated fruits like this is exactly the environment where it works best.
automated checkout tech is actually good enough to be used in production now
not really.
amazon’s just walk out is the leader in this area, and it came out recently that the bulk of transactions, 7 in 10, are offloaded for manual review in india
amazon of course denied the claim, but so in vague corporate speak, and failed to provide figures to counter the 7-in-10. they also did confirm that they’re scaling back just walk out. i don’t think those things would be the case if this technology worked as they were hoping.
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.
[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.
The Amazon shop is a lot more complicated than a few berries on a white shelf.
That was for automated checkout. Video people counters have been around for years. I’ve worked for companies that used them to count customers by department.
this isn’t counting people. this is working out which item or items people pick up from a shelf and decide to keep, if any. that isn’t just similar to the automated checkout problem: it’s the same exact problem. if anything, this iteration of it is more challenging because a blueberry is a fair amount smaller than a tin of beans.
