Scale AI and its 22-year-old CEO lock down $100 million to label Silicon Valley’s data
Big artificial intelligence companies are promising an automated future but many of their products rely on the labeled training data coming from Scale AI, a startup that highlights machine learning’s intimate bond between human contractors and algorithms.
The three-year-old startup announced Monday that it had closed a $100 million Series C round of financing led by Founders Fund with participation from Accel, Coatue Management, Index Ventures, Spark Capital, Thrive Capital, Instagram founders Kevin Systrom and Mike Krieger and Quora CEO Adam d’Angelo. A report in Bloomberg details that this funding will bring Scale’s valuation past $1 billion.
“In general, AI and machine learning is just growing so quickly as a field, that it’s appropriate to raise this amount that will allow us to capitalize on our ambitions,” the company’s 22-year-old executive Alexandr Wang told TechCrunch in an interview. “We don’t want to be in the business of constantly needing to raise capital, so ideally this is the last fundraise for us.”
Scale has around 100 employees, according to Wang, but its limited full-time staff is a small fraction of the human-power behind the services Scale offers. The startup has nearly 30,000 contractors aiding in the labeling process. “The humans are pretty critical to what we’re doing because they’re there to make sure that all the data we provide is really high quality,” Wang says.
Companies provide Scale with data via their API and the startup puts its resources to work labeling the text, audio, pictures and video so that its customers’ machine learning models can be trained.
The startup’s customers include Waymo, OpenAI, Airbnb and Lyft.
For a customer working with autonomous driving data, Scale’s services may mean taking collected video frames and manually segmenting out individual cars, humans or other obstacles. For another customer, it can mean making common sense language connections to ensure natural language processing models can understand language in context.
The “human insight” can help minimize labeling bias and give customers data that is more precise and more accurate though, as with just about all AI startups, the hope is that these insights will gradually usher in a future where reliance on these humans-in-the-loop will be lessened. In the meantime, Scale sits atop an army of contractors that might hold the key to bulking up Silicon Valley’s machine learning intelligence.
“AI companies will come and go as they compete to find the most effective applications of machine learning. Scale AI will last over time because it provides core infrastructure to the most important players in the space,” Founders Fund partner and former Trump advisor Peter Thiel said in a statement.
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