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No Priors: Artificial Intelligence | Technology | Startups
The Data Foundry for AI with Alexandr Wang from Scale
No Priors: Artificial Intelligence | Technology | Startups

The Data Foundry for AI with Alexandr Wang from Scale

Conviction 39m 25 months ago
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com. Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners. Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.

Show Notes

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Alexandr Wang was 19 when he realized that gathering data will be crucial as AI becomes more prevalent, so he dropped out of MIT and started Scale AI. This week on No Priors, Alexandr joins Sarah and Elad to discuss how Scale is providing infrastructure and building a robust data foundry that is crucial to the future of AI. While the company started working with autonomous vehicles, they’ve expanded by partnering with research labs and even the U.S. government.  
In this episode, they get into the importance of data quality in building trust in AI systems and a possible future where we can build better self-improvement loops, AI in the enterprise, and where human and AI intelligence will work together to produce better outcomes. 
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alexandr_wang
() Introduction
() Data infrastructure for autonomous vehicles
() Data abundance and organization
()  Data quality and collection
() The role of human expertise
() Building trust in AI systems
() Evaluating AI models
() AI and government contracts
() Multi-modality and scaling challenges

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