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Marketplace Tech

Teaching AI to think like a human

Marketplace Tech

American Public Media

Technology, News

4.61.2K Ratings

🗓️ 28 June 2023

⏱️ 10 minutes

🧾️ Download transcript

Summary

Behind the artificial intelligence tools that have become household names is an army of human workers teaching the bots to be smart. These aren’t the folks who testify before Congress or hype the latest updates on social media. For the most part, they’re gig workers spread across the globe who do seemingly random tasks for subcontractors of subcontractors to the big-name companies that make the news. Marketplace’s Meghan McCarty Carino spoke with features writer Josh Dzieza, who went inside the world of “data annotation” for this week’s New York magazine cover story in collaboration with The Verge. He said the people doing this work often are given little information about who or what it’s for.

Transcript

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0:00.0

Marketplace Morning reports new Skin in the Game series explores what we can learn about

0:04.6

money and careers from the $300 billion video game industry. Plus, here how an Oakland-based

0:11.0

program helps young people get the skills they need to break into this booming industry.

0:15.9

Listen to Skin in the Game and more from the Marketplace Morning report wherever you get your

0:20.7

podcasts. AI is changing how we work, starting with the people making it. From American public media,

0:29.8

this is Marketplace Tech. I'm Megan McCarty-Karino.

0:42.4

Behind the artificial intelligence tools that have become household names is an army of human

0:48.8

workers, teaching the bots to be smart. These aren't the folks testifying before

0:54.0

Congress or piping the latest updates on social media. But gig workers, for the most part,

1:00.3

spread across the globe doing seemingly random tasks for subcontractors of subcontractors of

1:07.9

the big name companies that make the news. Features writer Josh Jeza went inside the world of data

1:14.7

and notation for this week's New York magazine cover story named Collaboration with the Verge.

1:20.0

He says the people doing this work often aren't given much information about who or what it's for.

1:26.4

They know that they work on a platform and they know they're working for companies that are

1:31.9

training AI, but it's often obscured through kind of inscrutable code names like toolbox,

1:38.6

bratwurst or something like you can learn nothing about what you're working on for these names.

1:44.0

And the tasks are so small that you in a lot of cases can't figure out your what you're doing

1:49.4

or what you're changing AI to do beyond like recognize clothing or something like that.

1:55.1

And you write that this tangled supply chain is sort of deliberately hard to map. I mean,

2:02.0

you spoke with data annotators in different countries. This is kind of an ephemeral and very

2:09.9

dynamic network that is very hard to untangle. Yeah. If you think about it from the

2:16.4

the company's perspective, you know, looking at the annotation there they're requesting it reveals

...

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