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Uber’s new assignment for gig workers: AI data labeling
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The gig economy giant Uber is expanding into AI training services by leveraging its existing independent contractor model to provide data labeling and testing services to AI companies.

Key development: Uber’s new “Scaled Solutions” division aims to connect businesses with independent contractors who can perform AI training tasks, marking a significant pivot in the company’s business strategy.

  • The division builds upon an existing internal team based in the US and India that handles feature testing and content conversion
  • Notable clients already include Aurora, Luma AI, and Niantic
  • Workers are being recruited from multiple countries including Canada, India, Poland, Nicaragua, and the US

Business model and compensation: Uber’s approach mirrors its ride-sharing platform by connecting AI companies with gig workers who perform various data-related tasks.

  • Workers are paid per completed task, with payments distributed monthly
  • One example from India shows workers being paid approximately $2.37 per set of AI-generated coding response evaluations
  • The company seeks workers from diverse cultural backgrounds to enhance AI adaptability across different markets

Task specifications: The work primarily involves essential but repetitive tasks necessary for training AI models.

  • Tasks include selecting optimal chatbot responses
  • Workers label obstacles in self-driving car footage on a frame-by-frame basis
  • Additional responsibilities include testing and localization services

Historical context: Uber’s venture into AI is not its first experience with advanced technology initiatives.

  • The company previously invested billions in autonomous vehicle development before shutting down the program following a fatal accident
  • In 2016, Uber acquired an AI research lab established by cognitive scientist Gary Marcus and other computer science professors

Market implications: This move positions Uber to capitalize on the growing demand for AI training services while potentially raising questions about labor practices in the AI development ecosystem.

  • The expansion leverages Uber’s established contractor management infrastructure
  • The approach could set new precedents for how AI training data is collected and processed
  • The payment model may face scrutiny given ongoing debates about gig economy compensation
Uber for AI labeling

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