AI = Actual Indians, apparently.
Phrased differently, 30% of all transactions were still entered by a human overseas watching cameras at the time they decided to pull the plug, years after the initial launch.
There was a 2 year period in which I bought lunch at an Amazon Go daily. I was naive to the magic so I thought it was the greatest innovation ever.
https://tech.walmart.com/content/walmart-global-tech/en_us/b...
Shame to see another project fall to the strategy of AI = "actually indians". I wonder how many other companies have engaged in this stuff.
If you want to compete with the likes of GPT-4, Claude, or Gemini today, you're looking at billions, just for training, not counting infra, data pipelines, evals, red teaming, and everything else that comes with it.
Builder.ai wasn't able to use GenAI to actually build software. And when the money ran out and no model was ever announced, investors lost trust and clients lost patience.
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.