With the armrests you can kinda double wedge yourself on them. I got 2 decent hours in the lima airport doing this. They had the similar double seat with no armrest setup.
I got to ORD at 4am for a 7am flight and purposfully fell asleep on the ground in front of the check in desk figuring they'd wake me up. Eventually they did. The attendant woke me up and said "do you happen to be on this flight". I pointed at the first class ticket hanging out of my shirt pocket. "Good thing I checked we're closing the door now". I was like "I figured you'd maybe wake up the person in front of the desk I've been up for 38 hours." "oh you were sleeping I didn't want to bother you".
The entire flight had boarded and just walked around me. I was OUT.
Le Sigh.
And "other people in the past predicted doom about something like this and it didn't happen" is a fallacious non-argument even when the things are comparable.
[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.
no idea how much they make on it, but it's a game changer in that small area.
Isn’t that what they’ve been doing for a decade that got them to today?
I Write Sins Not Tragedies - Panic at the disco:
So you're a guest at a wedding and you're eavesdropping and passing judgement on people based on a snippet of conversation. Ruined.
Example:
Going the Distance - He's bad at racing and can't realize it. He's burning real relationships. I'd otherwise love this song.
Years ago my brother pointed out that lyrics are just a form of percussion.
I'm glad they add for you, they typically detract for me.
Not paying attention to the lyrics also les me deal with music as just grooves in a flow state as well.
Just like how sometimes when you're flying over the rockies or into canada you just don't get internets. There's still middles of no where out there. Often not very far from the freeway.