Scale AI: Why Data Will Power the AI Revolution
- Scale AI emerged from a fairly small-stakes problem
- The 26-year-old founder and CEO wanted to know when to restock his fridge
- He looked around for a side project and decided to build a camera inside his fridge that would tell him if he was running low on milk
- After a few weeks, he realized there was no way he’d get enough data to train his system to properly quantify his fridge’s contents
- That’s when he came up with Scale, what he calls “data infrastructure to power the AI revolution.”
First they built machines that could do arithmetic, but the idea that you could have them do these more nuanced tasks that required what we view as humanlike understanding was this very exciting technological concept
Rapid Iteration
- Self-serve, on-demand labeling to train AI with high-quality ground-truth data and enables machine-learning engineers and researchers to receive labels and instruction feedback in a matter of hours and scale to production volumes in days
- Integrates with Scale’s ecosystem of products to add data into existing ML data pipelines
- Customers benefit from the company’s ML expertise and infrastructure
- Rapid’s users include Block and At-Bay
Growing with the Company
- Since its early days, Scale has massively scaled, and its founder has grown along with it
- Wang now oversees a successful business employing hundreds
- His ability to do enterprise sales was unparalleled
- Cognitive rigor has also helped him as a manager
- Communication is one element of being authentic
- Surround yourself with people who are smart, ambitious, and hardworking
- People who help reinforce your best qualities and call you out on your worst
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