Impact AI Faster Object Search with Corey Jaskolski from Synthetaic
What if there was a way to revolutionize image-based AI, eliminating the need for extensive prework? In this episode, I sit down with Corey Jaskolski, Founder and President of Synthetaic, to talk about finding objects in images and video quickly. Synthetaic is redefining the landscape of data analysis with its groundbreaking technology that eliminates the need for time-consuming human labeling or pre-built models. It specializes in the rapid analysis of large, unlabeled video and image datasets.
In our conversation, we delve into the groundbreaking technology behind Synthetaic's flagship product and how it is revolutionizing image and video processing. Explore how it utilizes an unsupervised backend to swiftly analyze and interpret data, how it is able to work with any kind of image data, and the process behind ingesting and embedding image objects. Discover how Synthetaic navigates biased data and leverages domain expertise to ensure accurate and ethical AI solutions. Gain insights into the gaps holding AI’s application to images back, the different ways the company’s technology can be applied, the future development of Synthetaic, and more!
Key Points:
- Corey’s background in AI and ML and what led to the creation of Synthetaic.
- Why Synthetaic focuses on processing images and videos quickly.
- How the company leverages ML in its approach.
- Details about image ingestion and embedding processes.
- How the definition of potential objects varies depending on the type of imagery used.
- Explore the role of domain expertise in addressing challenges.
- Hear examples of the technology’s diverse range of applications.
- Recommendations to leaders of AI-powered startups.
- His hope for the future trajectory of Synthetaic.
Quotes:
“We think about the machine learning problems a little bit differently, because we're not labeling data to go ahead and build a bespoke frozen traditional AI model.” — Corey Jaskolski
“We take this very broad view of objects where anything that could be discrete from anything else in the imagery gets called an object, at the risk of basically finding, if you will, too many objects.” — Corey Jaskolski
“We think of RAIC as something that solves the cold start problem really well.” — Corey Jaskolski
“By and large, we're training image and video-based AIs the same way. We need a paradigm shift that really allows AI to be the force multiplier that it can be.” — Corey Jaskolski
Links:
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