Dataloop secures cash infusion to expand its data annotation tool set
Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption “bear” with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn’t seen before.
The trouble is, annotation is a manual and labor-intensive process that’s historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI — and in the data used to train that AI — an entire industry has sprung up around tools for annotation and labeling.
Dataloop, one of the many startups vying for a foothold in the nascent market, today announced that it raised $33 million in a Series B round led by Nokia Growth Partners (NGP) Capital and Alpha Wave Global. Dataloop develops software and services for automating aspects of data prep, aiming to shave time off of the AI system development process.
“I worked at Intel for over 13 years, and that’s where I met Dataloop’s second co-founder and CPO, Avi Yashar,” Dataloop CEO Eran Shlomo told TechCrunch in an email interview. “Together with Avi, I left Intel and founded Dataloop. Nir [Buschi], our CBO, joined us as third co-founder, after he held executive positions [at] technology companies and [lead] business and go-to-market at venture-backed startups.”
Dataloop initially focused on data annotation for computer vision and video analytics. But in recent years, the company has added new tools for text, audio, form and document data and allowed customers to integrate custom data applications developed in-house.
One of the more recent additions to the Dataloop platform is data management dashboards for unstructured data. (As opposed to structureddata, or data that’s arranged in a standardized format, unstructured data isn’t organized according to a common model or schema.) Each provides tools for data versioning and searching metadata, as well as a query language for querying datasets and visualizing data samples.
“All AI models are learned from humans through the data labeling process. The labeling process is essentially a knowledge encoding process in which a human teaches the machine the rules using positive and negative data examples,” Shlomo said.
