This is a bad submission because Boing Boing heavily editorializes, and is at least 2 levels removed from the actual reporting at this point - the editorialized Boing Boing excerpts, the editorialized Gizmodo article, The Information article, whatever the sources leaked to TI as a friendly outlet, and then the truth. And there are people on Twitter disputing the insinuations that the human workers were watching you as you shop, which would bespeak a true level of failure, or just reviewing stuff afterwards, which would surprise no one.
Also reminds me of the revelation that Expensify was mostly using manual transcription vs. "AI" to categorize and interpret receipts/invoices: Expensify sent images with personal data to Mechanical Turkers https://news.ycombinator.com/item?id=15796189
All these AI companies have a bunch of people who just do labeling to help train the model. Like saying google has millions of people powering waymo when its just recaptcha having users help with labelling image captures like select all the images with street signs or bicycles .
Securities Fraud?
(With apologies to Matt Levine)
These types of systems take a week to train the obvious clean use case, and a decade to be able to perform with people intentionally or unintentionally messing with the system
Just don’t act surprised when we “find out” the dash carts are also powered by humans
The title here suggests they're watching you shop and entering items into a list as you go (which would be ridiculous). It seems to actually just be low-paid workers spot checking to ensure the "algorithm" got it right?
I can't believe they're throwing in the towel right now. Sure, whatever pipeline they were using before was maybe a dead end but with technology like GPT/Gemini vision they couldn't make it work? Did they even try?
I remember seeing a couple of blogs that said this last year.
[dupe]
More discussion: https://news.ycombinator.com/item?id=39908579
In a tech-crazed era where the boundaries of reality blur with the audacity of innovation, there unfolds a tale so bewildering, it vaults past the edges of conventional disruption into the realm of the utterly surreal. This isn't just another yarn spun from the looms of Silicon Valley's grand dream factories. No, this saga pirouettes on the razor's edge of imagination and insanity, where the most cutting-edge "AI" in retail isn't AI at all—it's the keen eyes and quick wits of Pakistanis, stationed a world away, orchestrating the illusion of machine precision in real-time.
Let's zoom in on Lahore, pulsating with life and a fervor for innovation that makes Silicon Valley look like a retirement home for tired tech. Here, in a place that hums with the energy of endless possibility (and perhaps too much caffeine), sits the command center of the world's most "advanced" computer vision system, SeeAll. Only, SeeAll's vision is purely human, powered by a legion of sharp-eyed annotators who scrutinize live feeds from grocery store checkouts, identifying every item with the accuracy and flair only a human can muster.
The masterminds behind this grand ruse? VisioTech, a company shrouded in the mystique of technological advancement, promising a checkout experience free from human error, barcodes, and the tedium of waiting. Their secret sauce wasn't algorithmic; it was organic, brainpower fueled by chai and an undying spirit of camaraderie.
This narrative, though dripping with the trappings of high-tech, is really an ode to the human element in the digital age. Customers, awash in the glow of seamless transactions, never questioned the "how" of their flawless shopping experience, unaware that the real magic lay in the hands of the PaaS team. This crew, capable of identifying the most obscure products with a glance, operated in a symphony of clicks and keystrokes, a ballet of productivity that no machine could replicate.
The facade crumbled when a technical hiccup at an Ohio grocery store exposed the gears behind the magic. What followed was a tumultuous unraveling that laid bare VisioTech's elaborate scheme. Yet, the fallout was anything but predictable. The world, instead of recoiling in outrage, leaned in, captivated by the audacity and sheer inventiveness of it all.
Honestly I think it's cool that Amszaon took that approach. Do things that don't scale to better understand the value/market fit. Then you can work on the hard problem of building the complicated tech to enable it at scale. So often companies jump straight to the hard tech issues without even trying to learn from the real world.
Previously discussed: https://news.ycombinator.com/item?id=39908579