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Comment by hyperrail | Hacker News Reader

My favorite example of this sort of thing, mentioned in the Guardian story, is the kerfuffle late last year when Expensify's receipt-reading SmartScan feature was found out to be partly backed by human receipt readers: https://qz.com/1141695/startup-expensifys-smart-scanning-tec...

(I don't file expenses often, but personally when I do so through Expensify, I find SmartScan so slow that I now assume all my "SmartScanned" receipts are going to a human.)

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Quote from the article

>How to start an AI startup

>1. Hire a bunch of minimum wage humans to pretend to be AI pretending to be human

>2. Wait for AI to be invented

Translation: Lean Startup Concierge/Wizard of Oz MVP. For anyone wanting to learn or refresh about that, here's a descent article I just Googled up (I reviewed a few and picked this one): https://grasshopperherder.com/concierge-vs-wizard-of-oz-test...

How do they handle bogus results from MTurk? I know people that tried to use it and had to stop because 95% of the "workers" just put meaningless data in hoping you don't notice and they get paid.

It depends on the task, but if you can get them to do multiple tasks in one, you can ask them to do a task you already know the answer to. If they get the known answer wrong, instant reject.

Couldn't you just solve that by sending the job to multiple workers and comparing the results?

That's an obvious solution, but you might start approaching a point where it's actually cheaper to hire a dedicated person for it.

That doesn't solve the problem of a person being wrong.

Think about it.

I consulted with a great company in this field and they also use a mix of OCR and humans. Difference is they have trained in-house staff, rather than farming it out to just anyone for the cheapest they can get.

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