Ride-hailing giant Uber is diversifying its business portfolio by venturing into the AI labeling space, reports Bloomberg. The company is leveraging its gig economy model to connect businesses with independent contractors for data annotation, testing, and localization tasks. This move marks a significant expansion of Uber's business model, which has traditionally focused on ride-hailing and food delivery services.

The new "Scaled Solutions" division at Uber aims to provide businesses with access to a pool of "nuanced analysts, testers, and independent data operators" who can perform tasks such as data labeling, testing, and localization. This division is an extension of an internal team that has been working on tasks like new feature testing and converting restaurant menus to Uber Eats selections. The team has members based in the US and India.

Uber's foray into AI labeling is a strategic move, given the growing demand for machine learning and large-language models. The company has already been using artificial intelligence and machine learning for its own business and is now making these capabilities available to other companies for a fee. Some of the clients that have already partnered with Uber's Scaled Solutions division include Aurora, Luma AI, and Niantic.

The AI model training process relies heavily on human workers to perform tedious tasks such as selecting the most human-sounding chatbot results or labeling obstacles like pedestrians in self-driving car footage frame-by-frame. This process requires a large workforce, and companies often hire workers in developing countries to perform these tasks at relatively low wages. For instance, an engineer in India told Bloomberg that they were paid 200 rupees (approximately $2.37) per set for comparing and rating the correctness of AI-generated responses to complex coding problems.

Uber is currently hiring gig workers from Canada, India, Poland, Nicaragua, and the US to perform data labeling, testing, and localization tasks. The company is paying different amounts per completed task, with earnings issued to workers on a monthly basis. Uber is also looking to hire people with diverse cultural backgrounds to help make AI more adaptable in various markets.

This is not Uber's first foray into the AI space. The company has invested billions of dollars in developing its own self-driving cars, although it shut down the operation after one of its vehicles was involved in a fatal accident. In 2016, Uber also acquired an AI research lab founded by cognitive scientists Gary Marcus and several other computer science professors.

Uber's expansion into AI labeling has significant implications for the gig economy and the future of work. As the demand for machine learning and large-language models continues to grow, it is likely that more companies will follow Uber's lead and hire gig workers for data annotation and testing tasks. This trend could lead to new opportunities for workers in developing countries, but it also raises concerns about fair compensation and working conditions for gig workers.

In conclusion, Uber's venture into AI labeling marks a significant shift in the company's business strategy and has far-reaching implications for the gig economy and the future of work. As the company continues to expand its Scaled Solutions division, it will be interesting to see how this move affects the broader tech industry and the workers who power it.