Welcome back to another episode of AI Innovators with Rob May, the podcast that deep dives into how AI is reshaping industries and the groundbreaking minds behind it.

In today's episode, we're joined by Pawel Zimoch, co-founder of Featrix AI, a company known for pioneering vectorization solutions for the enterprise.

As we explore the world of vectors and embeddings, Pawel unpacks the significance of data predictability and why some data sets work better with AI than others.

We'll also delve into the evolution of Featrix, from its founding story to its innovative strides in custom embeddings and multi-modal data analysis.

Whether you're an entrepreneur, a data scientist, or simply curious about the future of AI, this episode is packed with insights you won't want to miss. Tune in for a fascinating conversation on the complexities, challenges, and exciting advancements in the realm of AI.

Pawel Zimoch's Links

( Featrix.ai )

(Linkedin)

( Featrix)

Rob's Links

( halfcourt.vc )

( Rob AI Newsletter )

00:00 - Embeddings use cosine similarity for comparative analysis.

04:57 - Predictive data value varies; feature engineering necessary.

06:28 - Efficient AI system predicts project viability quickly.

12:04 - Human judgment and problem-solving skills prevail.

13:59 - Learning computer science is essential for understanding AI.

16:26 - Managing models requires significant context from data scientists.

22:28 - Connect with others for broader learning perspectives.

23:28 - Success in entrepreneurship relies on understanding people.