Data is a valuable asset that needs to be protected. It shouldn’t be sent to large language models. So what's the alternative?

Generative AI has emerged as a promising approach to improve the performance of machine learning models. With the ability to generate new data, generative AI can create synthetic data, provide pseudo-labels for the unlabeled data, denoise the original data, and assist in active learning.

Jaxon created a custom AI model to characterize the seafloor habitats of the U.S. Caribbean and accelerated the annotation process from minutes to milliseconds per image. Jaxon incorporated domain knowledge at every step, producing a highly-accurate automation environment and saving the NOAA team months of manual effort.

Greg Harman, Jaxon's CTO, shares his thoughts on improving models with synthetic data, and striking a balance between noise and quantity.

Does there have to be a tradeoff between speed and accuracy? With rapid prototyping, you can have both. The Jaxon team discusses how.

Jaxon's CTO, Greg Harman, takes you through the loops of iterative AI development and how to take AI from toy problems to practical applications.

Do you feel like people are treating data science more like 'astrology for data'? Jaxon wants to trade the mysticism for concrete facts.

We discuss using custom ML models to analyze voice of the customer on social media, call/chat logs, reviews, surveys, and more.

Jaxon's CTO, Greg Harman, explores the meaning of truth—specifically, ground truth and its intersection with real-world data.

Active learning was an important step towards creating effective TDP and cost-sensitive ML, but is it enough by itself?