[davidst]

I left the following comment some months ago, duplicating it here:

[Disclaimer: Former Amazon employee and not involved with Go since 2016.]

I worked on the first iteration of Amazon Go in 2015/16 and can provide some context on the human oversight aspects.

The system incorporated human review in two primary capacities:

1. Low-confidence event resolution: A subset of customer interactions resulted in low-confidence classifications that were routed to human reviewers for verification. These events typically involved edge cases that were challenging for the automated systems to resolve definitively. The proportion of these events was expected to decrease over time as the models improved. This was my experience during my time with Go.

2. Training data generation: Human annotators played a significant role in labeling interactions for model training-- particularly when introducing new store fixtures or customer behaviors. For instance, when new equipment like coffee machines were added, the system would initially flag all related interactions for human annotation to build training datasets for those specific use cases. Of course, that results in a surge of humans needed for annotation while the data is collected.

Scaling from smaller grab-and-go formats to larger retail environments (Fresh, Whole Foods) would require expanded annotation efforts due to the increased complexity and variety of customer interactions in those settings.

This approach represents a fairly standard machine learning deployment pattern where human oversight serves both quality assurance and continuous improvement.

The news story is entertaining but it implies there was no working tech behind Amazon Go which just isn't true.

[grogenaut]

The go tech is amazing in 2 places: airport and stadium beverage tunnels. There's a premium price and high volume in those areas. The go tech has basically revolutionized the speed of getting a beer and a dog at the stadium here in Seattle. I can be back in my seat in 4 minutes including the bathroom now which for NFL means I can literally be back in a commercial break sometimes.

no idea how much they make on it, but it's a game changer in that small area.

[trollbridge]

One wonders just how much technology is needed to dispense a beer and a hot dog.

[afavour]

Couldn't you just use vending/automat machines in these scenarios? Beers in particular are... not complicated. I believe the go tech makes the existing situation better but if you were to reimagine it from ground up I can't help but imagine you could do better.

[LPisGood]

What’s still not clear to me about this story is if there was ever live human monitoring of shoppers. Did the low confidence resolution occur in real time, at some point between the customer grabbing the item and getting their bill?

[davidst]

It wasn't real-time. Recorded events were entered into a queue and latency would vary depending on the size of the queue and the number of annotators.

[BoredPositron]

I get being proud of the work done but if they scrapped the project after 10 years because of feasibility I don't think the tech rolled out at the start was "working" as intended.

[davidst]

The first iteration of the tech reached the accuracy needed to support just-walk-out for a small-format store. It did achieve that goal. I left the project before it went further.

I imagined, at the time, future goals would be to scale store size and product variety while reducing the cost of the technology, but I have no insight into how that progressed. I am sorry to learn it's been shut down.

[throwaway_15612]

Could it be improved by requiring the customers to use a "smart" shopping basket that can read RFC codes from the product packaging? In combination with vision tech it should give a relatively higher accuracy.

If so, is the reason why it is not used related to cost?

[scoot]

Obligatory /disclaimer/disclosure/. (Don't worry, most HNrs get this wrong for some reason. I will be downvoted for pointing this out, but whatever. It's a meaningful difference to those that understand.)

[Terretta]

Arguably they first disclose (employee) then disclaim (but not for a while now)...

[davidst]

I have been making this mistake for decades. I am upvoting your comment to show thanks!

[londons_explore]

As soon as you get to ~99% accuracy, you probably don't need to go further.

If the customer is accidentally billed for an orange instead of a tangerine 1% of the time, the consumer probably won't notice or care, and as long as the errors aren't biased in favour of the shop, regulators and the taxman probably won't care either.

With that in mind, I suspect Amazon Go wasn't profitable due to poor execution not an inherently bad idea.

[Slartie]

Actually, discount grocers operate on razor-thin margins of 2-4%. If your inaccuracy is geared to the benefit of your customer (because otherwise you'll be out of business due to the regulatory bodies) and thus removes just one percent of that, you suddenly lose a quarter to half of your earnings! And that goes ON TOP of the additional cost incurred with all that computer vision tech.

In addition to that, you'll have the problem of inventory differences, which is often cited as being an even bigger problem with store theft than the loss of valued product. If the inventory numbers on your books differ too much from the inventory actually on the shelves, all your replenishment processes will suffer, eventually causing out of stock situations and thus loss of revenue. You may be able to eventually counter that by estimating losses to billing inaccuracies, but that's another complexity that's not going to be free to tackle, so the 1% inaccuracy is going to cost you money on the inventory difference front, no matter what.

[davidst]

I don't have insight into what ultimately transpired at Amazon Go so take the following as speculation on my part.

It is unlikely the tech would be frozen when an acceptable accuracy threshold is reached:

1. There is a strong incentive to reduce operational costs by simplifying the hardware infrastructure and improving the underlying vision tech to maintain acceptable accuracy. You can save money if you can reduce the number and quality of cameras, eliminate additional signal assistance from other inputs (e.g., shelves with load cells), and generally simplify overall system complexity.

2. There is business pressure to add product types and fixtures which almost always result in new customer behaviors. I mentioned coffee in my prior post. Consider what it would mean to add support for open-top produce bins and the challenge of complex customer rummaging. It would take a lot of high-quality annotated data and probably some entirely new algorithms, as well.

Both of those require maintaining a well-staffed annotation team working continuously for an extended time. And those were just the first two things that come to mind. There are likely more reasons that aren't immediately apparent.