Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Show Notes(01:59) Hyun shared his upbringing and experience living in Korea, Singapore, and the US.(04:18) Hyun described his undergraduate experience at Duke University.(08:21) Hyun shared how he got a real taste of the game-changing potential of deep learning from the experience of bringing ML to diagnose Parkinson’s disease with brain MRI scans.(10:54) Hyun talked about his journey of leveling up coding and ML knowledge.(12:13) Hyun reflected on his motivation to pursue a Ph.D. program in computer science at Duke.(15:22) Hyun talked about his participation in the 2016 Amazon Robotics Challenge as the “Team Duke” leader and its Motion Planning function.(17:25) Hyun reflected on his decision to take a leave of absence from his Ph.D. program and return to Korea to work as an ML Research Engineer at the AI Research Lab of SK Telecom, a major Korean conglomerate.(19:46) Hyun discussed his research on game AI and synthetic image generation during his time with SK Telecom.(22:57) Hyun shared the founding story of Superb AI.(27:11) Hyun described going through the Y Combinator Winter 2019 batch.(32:25) Hyun unpacked the evolution of Superb AI’s Labeling platform since its inception.(34:47) Hyun walked through the process of prioritizing the product roadmap.(36:54) Hyun zoomed in on Superb AI’s automated labeling feature, Custom Auto-Label, which automatically detects and labels common or niche objects in images and videos.(40:21) Hyun touched on challenges with manually reviewing and auditing labels.(42:25) Hyun dissected the data-centric problems in computer vision that the newly released Superb DataOps platform is built to solve.(46:46) Hyun hinted at Superb AI’s product roadmap, judging from current industry-wide pain points.(48:53) Hyun highlighted a customer use case of Superb AI product offerings.(51:42) Hyun shared his vision of where Superb AI fits into the quickly evolving AI Infrastructure ecosystem.(54:15) Hyun shared valuable hiring lessons to attract people who are excited about Superb AI’s mission.(58:01) Hyun expanded his perspectives on defining and scaling a global company culture.(01:00:06) Hyun reflected on the challenges of running a remote-first company.(01:01:54) Hyun shared fundraising advice for founders seeking the right investors for their startups.(01:03:35) Hyun highlighted the difference between being a researcher and a founder.(01:05:08) Closing segment.Hyun’s Contact InfoLinkedInTwitterSuperb AI ResourcesWebsite | LinkedIn | Twitter | YouTube | GitHub | DocsSuperb AI Suite Labeling PlatformSuperb AI DataOps PlatformThe Ground Truth NewsletterSuperb AI AcademyMentioned ContentPeopleAndrew NgAndrej KarpathyIan GoodfellowBookZero To One (by Peter Thiel)About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to or browse the full guest list.

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.