Comment by davidst

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

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.

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.

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.

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

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

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.

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.