Data Science Investment Counter —Funding raised by UK data science companies in 2018.
£ 5.640 Million

Labelbox raises $10m in Series A funding to improve industrial AI

Labelbox, a collaborative training data platform for machine learning applications, raised $10m in Series A funding, the company revealed on Tuesday.

The round was led by Gradient Ventures, Google’s AI-focused venture fund, together with previous investors Kleiner Perkins, First Round Capital, and angel investor Sumon Sadhu.

Labelbox also announced that Anna Patterson, founder and Managing Partner at Gradient Ventures, VP of Engineering at Google, and Square board member, will join their board.

The company said they would use the funds to keep on expanding their team and double its headcount in 2019 by hiring new talents in engineering, sales, marketing, and customer success roles.

“Labelbox substantially reduces model development times and empowers data science teams to build great machine learning applications”, said Manu Sharma, founder and CEO of Labelbox. “With the new funding, Labelbox will continue to double down on bringing data labelling infrastructure to the machine learning teams with powerful automation, collaboration, and enterprise-grade features.

“We’re excited to work with the team at Gradient Ventures and appreciate their support as we scale our business to meet customer demand,” he added, “We’re also proud to have incredible investors who have believed in us since the beginning, such as Bucky, Ilya, and Bill from Kleiner Perkins and First Round Capital.”

The platform, which acts as a central hub for humans to interface with artificial intelligence (AI), is currently used by customers such as FLIR Systems, Lytx, Airbus, Genius Sports, KeepTruckin and thousands of users worldwide.

“Labelbox is well-positioned to fuel the industrialisation of machine learning across many sectors, such as manufacturing, transportation, and healthcare. In doing so, they will unlock the potential of AI for companies across the globe,” Anna Patterson said.

AI systems are increasingly deployed in different industries, and their computation power is ever-increasing. Because of this, one of the primary necessities in operating AI is to create interfaces through which they can learn from humans.

“The confluence of accessible GPU compute and deep learning technology has paved the way for companies to build production AI systems,” said Peter Welinder, advisor to Labelbox and Research Scientist at Open AI, “the hard part now is teaching machines to think. Labelbox is the key technology to do just that, enabling AI teams to develop new breakthrough products.”

Image via Pixabay.


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