Superb AI generates customized training data for machine learning projects

One of the big challenges of developing a machine learning project can be simply getting enough relevant data to train the algorithms. That’s where Superb AI, a member of the Y Combinator Winter 2019 class, can help. The startup helps companies create customized data sets to meet the requirements of any project, using AI to speed up the tagging process.

Hyun Kim, who is CEO and co-founder at the startup, says one of the big stumbling blocks for companies trying to incorporate AI and machine learning into their applications is coming up with a set of suitable data to train the models. “Superb AI uses AI to make customized AI training data for large tech companies. Clients work with us to develop machine learning-based features in their products multiple times faster than they could themselves,” Kim told TechCrunch.

Kim and his co-founders (CTO Jung Kwon Lee, machine learning engineers Jonghyuk Lee and Moonsu Cha and Hyundong Lee, head of APAC sales and operations, who is based in Seoul, South Korea) all were working in the field when they identified the data problem and decided to launch a company to solve it.

Traditionally, companies working on a machine learning project will hire human workers to tag data, but this has been expensive and error prone, assuming you even had the data to work with. Kim and his co-founders, who worked on AI projects and studied the subject in college, came up with the idea of putting AI to work on the tagging part of the problem.

“Instead of relying on slow and error-prone manual labor, Superb AI uses proprietary deep learning AI that assists humans to achieve up to 10x faster labeling of images and videos,” Kim explained. The company will also help find data sources for companies that don’t have any data to begin with.

Kim says that they don’t take humans out of the process completely, but they do enhance tagging accuracy by combining human workers with artificial intelligence underpinnings. He says that this involves a couple of steps. First, it splits training data into as many components as possible in order to automate each piece one at a time. If the data is too complex, and the AI tools can’t automate the tagging, they use a second approach called “human in the loop.” As humans label data, the AI can learn over time and eventually take over more and more of the process.

The co-founders decided to apply to Y Combinator to gain a foothold in Silicon Valley, where they could expand their market beyond their native South Korea. “It’s definitely been a game changer. The amount of knowledge and experience we gained from the YC partners and fellow entrepreneurs is really unbelievable. And also the vast YC network helped us find our early customers in the Valley,” Kim said.

The company, which launched last October, is up to 13 employees, including the co-founders. It has raised $300,000 in seed investment and has already generated the same amount in revenue from the product, according to Kim.