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How does AI data labeling work, and why do we need human experts to teach AI?

What does it take to create a well-operating AI model? Data is the key, but there is something more that lies behind high-quality data. According to Manu Sharma, human experts are the answer. While we can create purely synthetic data in some cases, teaching a model entirely new concepts requires humans. What’s more, ensuring that AI is aligned with human intent is crucial, and data labeling is at the heart of that alignment. 

In this episode of the AI at Scale podcast, Gosia Gorska welcomes Manu Sharma, the Founder and CEO of Labelbox, the leading data factory for building next-generation AI. Manu starts with explaining what data labeling is. He highlights the critical role of data labeling in AI development, the evolution of data annotation techniques, and the future of AI technologies. He shares his extensive experience in aerospace engineering, AI research, and entrepreneurship, highlighting how his company supports businesses in aligning and training their AI models through high-quality data labeling. 

Producing high-quality data at scale involves multiple steps and meticulous processes. To address this challenge, Labelbox manages one of the largest networks of AI tutors globally – alignerr.com, which includes university professors, domain experts, language experts from various regions, and more. They label data through part-time jobs and help producing high-quality data at scale. 

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