Importance of Real Data for Training AI Models

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Exploring the significance of labeled data in training AI models as they function as function approximators, adapting and predicting outcomes based on the data fed into them. Examples like training self-driving cars and chatbots showcase the critical role of labeled data in enabling AI systems to learn and generalize functional mappings.

Machine learning models learn patterns and relationships from data to make predictions or decisions. The quality of the data influences how well these models can represent and generalize from the data.

Nihit Desai is the Co-founder and CTO at Refuel.ai. The company is using LLMs for tasks such as data labeling, cleaning, and enrichment. He joins the show to talk about the platform, and how to manage data in the current AI era.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer .