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.
The Amazon shop is a lot more complicated than a few berries on a white shelf.
not in the ways that matter, and small, organic items like individual berries are far harder to account for than standardized product packaging
That’s not necessarily true-- in fact, two similarly packaged items that are otherwise different might actually be harder to tell apart when packaged.
Could be or could be the berries are put in the same arrangement each day and it’s just tracking which black blob disappears.
[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.
it’s AI, it’s made of statistics, there will always be some errors
7 in 10 required manual review
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.
which is why that wasn’t the setup of just walk out
every location was quite literally purpose built with the express goal of making the just walk out technology as accurate as it possibly could be
You place the item on the pad and it selects the most likely item in the store based on what it sees
this is a completely different problem
nobody’s placing the berry or berries they decide to eat or not eat in a separate area before placing them in their mouth
this is a completely different problem
Yes, that’s what I’ve been trying to explain. And no, JWO was not built to be accurate, it was built to be convenient. That’s a very different incentive that will lead to skipping alternatives that are less convenient but more accurate-- like the checkout kiosks I’ve been talking about. I’m not defending JWO and it’s obviously both a harder problem and one that’s not managed well, focusing on optics over accuracy.
nobody’s placing the berry or berries they decide to eat or not eat in a separate area before placing them in their mouth
That’s not necessary, they’re already placed in a nearly ideal environment by the person setting up the berry bowl. Notice how the “bowl” is a white square with each fruit placed in a way where they’re separated by the whitespace. You wouldn’t even need to train a model on the whole bowl, you could just do an image region detection --> object recognition pipeline. The hardest part about the berry bowl would by far be determining the person taking the fruit! (In fact, I wouldn’t be surprised if that was manually reviewed, with that few instances to look at.)
