Israeli startup raises $18.5 million to train A.I. with fake data
A key obstacle that companies interested in using artificial intelligence face is a lack of the right kind of data to train their systems.
Companies require voluminous amounts of labelled, historical samples to train A.I. systems, especially firms that work with images and videos. The demand has necessitated the existence of a whole sub-industry of firms that focus on helping other businesses annotate their data.
Some of such companies are Scale AI, which was valued at $3.5 billion in a December 2020 funding round, Hive, Sama, Labelbox, Cloudfactory, and a division of A.I. company Clarifai, among others.
But there is an alternative to employing annotators: fabricating data. This is what a fast-growing Israeli startup called DataGen helps companies do. Utilizing its own machine learning systems, Datagen creates what is referred to as “synthetic data”. These are artificially generated still and video images that DataGen’s clients can use to train their own A.I.
Cutting out the months it would typically take a data labelling company to curate an equivalent real world video or image library, DataGen is able to produce a bespoke synthetic dataset to its clients in a matter of hours, says Ofir Chakon, DataGen’s founder and chief executive officer
“Our customers have full control over all the parameters that go into the data they create,” Chakon says. “The real-world implication is that, once deployed, you can be sure it’s going to work well in different domains, with different ethnicities, in different geographic locations or any environment you can imagine.”
On Tuesday, DataGen announced a $18.5 million early stage funding round led by Israeli venture capital funds TLV Partners and Viola Ventures.
DataGen announced a $18.5 million early stage funding round led by Israeli venture capital funds TLV Partners and Viola Ventures
DataGen said the funds would help with hiring more machine learning experts and engineers, adding to the 30 employees it currently has. Chakon also added that the firm would also expand its focus from creating training sets for machine learning to data that can also be used for testing those A.I. systems once they are adequately trained.
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