Scale AI and its 22-year-old CEO lock down $100 million to label Silicon Valley's data – TechCrunch

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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."


Data Annotation: The Billion Dollar Business Behind AI Breakthroughs

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When Lei Wang became a data annotator two years ago her job was fairly simple: Identifying people's gender in images. But since then Wang has noticed increasing complexity in the tasks she is assigned: from labeling gender to labeling age, from framing 2D objects to 3D bounding boxes, from daylight images to late night and foggy scenes, and the list goes on. Wang is 25 years old. She used to be a receptionist, but when her company shut down in 2017 an algorithm engineer friend suggested she explore a new career path in data annotation -- the essential process of labeling data to make it usable for artificial intelligence systems, particularly those using supervised machine learning. Being out of a job, she decided to give it a try.


Data Annotation: The Billion Dollar Business Behind AI Breakthroughs

#artificialintelligence

When Lei Wang became a data annotator two years ago her job was fairly simple: Identifying people's gender in images. But since then Wang has noticed increasing complexity in the tasks she is assigned: from labeling gender to labeling age, from framing 2D objects to 3D bounding boxes, from daylight images to late night and foggy scenes, and the list goes on. Wang is 25 years old. She used to be a receptionist, but when her company shut down in 2017 an algorithm engineer friend suggested she explore a new career path in data annotation -- the essential process of labeling data to make it usable for artificial intelligence systems, particularly those using supervised machine learning. Being out of a job, she decided to give it a try.


'At Google, we list AI projects we don't do'

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Jia Li is very passionate about artificial intelligence (AI) and how it can improve healthcare. When one of her close family members suffered from a skin condition, she worked on developing an image recognition technology to help classify such diseases and diagnose them better. Now, as the head of R&D for Cloud AI, Google Cloud and an adjunct professor at Stanford University's School of Medicine, Dr. Li and her team at Google focus on research and innovation to solve real-world problems. This includes developing AI products on Google Cloud to power solutions for diverse industries. Edited excerpts: Dr. Li who before joining Google led research and innovation efforts at Snapchat's parent company Snap and Yahoo!


What Tech's Unicorn Cult Can Learn from the Art World

The New Yorker

In the thirty-one months since the investor Aileen Lee popularized the term "unicorn" as shorthand for a startup technology company worth a billion dollars or more, the concept has gone from novelty to gestalt to frenzy to trouble to embarrassment. Some unicorns have been sold for a fraction of their once-billion-dollar values. Others have had investors mark down the value of their stakes. A few, like the blood-testing company Theranos, have been accused of fraud, hinting at a rot beneath other startups. But one transformation wrought by the unicorn phenomenon endures: where company valuations used to be concerns that were tertiary to their identity, how much a company is worth now defines it.