Usual business mindset on something like this is, "Sure, this is not economical for us, but it's only for six to nine months while the software guys code up the real software. In the mean time, we'll collect and maintain market share, and we'll just swap in the real software when it's ready."
I watched this sketch comedy show in Iceland as a kid, where every week they had a section called "the men behind the curtains". It was just people hidden away inside ATMs, vending machines etc. pretending it was a machine doing the work.
If it were Amazon, you know that at least 150 out of those 1000 workers had already been threatened with PIP before being put on a plan. Look up Focus and Pivot, Amazon's policy that puts around ~5-15% of corporate workers below director level a year on forced attrition.
Nah, this was definitely outsourced to a company in India; you can abuse contract/vendor employees with way less effort than it takes to abuse full-time employees.
Perhaps, although many labelling teams at Amazon for other orgs are in-house or are part-time hours. Amazon likes keeping things in-house because they don't particularly give a fuck about abusing staff.
This is also known as "stack ranking" and "rank and yank".
It's a super-gross way to run a business. I can see how you might want to "cut the fat" when starting out or growing. But keeping a policy like that for the long haul means selecting for employees that are good at that surviving. And that may not require one to even be all that productive, just good at working the system.
Its not about automation; its about making labor fungible, kinda like the garmet industry-local workers rights happen, call center instantly relocates.
This is the globalization you get while jacking off with the monkey paw.
They've surely started working on it already. Current "AI" (LLMs) aren't perfect. They require constant human adjustments.
I'm an auditor for a "machine learning" algorithm's work, and it develops new incorrect processes faster than it corrects them. This is because corrections require intervention, which involves a whole chain of humans, whereas learning new mistakes can happen seemingly spontaneously. The premise of machine learning is that it changes over time, but it has no idea which changes were good until it gets feedback.
So, to answer your question, I'm sure they're throwing a ton of money at that. But when will it be viable, if ever?
I remember trying MTurk out back in the day to try and make money on the side. It's such a mind numbing activity. Doesn't surprise me that this is still the model for smart "automated" systems like this.
I used one of these stores like 5-6 years ago, maybe more. It was basically a pop up shop so it was pretty new. It took like 15-20 minutes for me to be charged. I'm positive now that it was some poor Indian watching me because it took so long.
Yeah except with there being only 27 stores in the US that use this tech if you have 1000 people reviewing the purchases is it really a machine learning system or are you just outsourcing the process to people in another country.
It’s still machine learning, you need labelled data to teach the algorithm what is what. It’s exactly the same thing as with captcha, random people labelling pictures for self driving cars. I doubt they were watching videos in real time, they were probably going through footage afterwards and marking errors and such.
You still have to keep training the Model. These stores were in large busy markets, and having people watch and critique the AI is how they continually train the model. It took Apple over 8 years to 'announce' they're doing on device voice recognition(they probably aren't), and that was just voice recognition and LLM training vs image recognition which is hard on its own. Let alone tracking a person THROUGH a store, recognizing that someone picked something up and took it vs put it back or left it on another row.
The real reason this probably happened is because those 1000 people training the model reported metrics of failures on top of the stores showing losses due to error. The margin of error was probably greater than they wanted. Or add in the biometric data they had integrated into it adding more layers of cost and privacy protection...it probably just doesn't return the money they wanted and they'll try again in a few years probably utilizing more RFID on top of the image recognition and people tracking.