This is a YouTube video titled "How Can AI Understand Who You Are?" containing a monologue or presentation discussing the challenges and approaches to AI personalization and interpretability, focusing on understanding user perception and beliefs to improve AI alignment and user engagement. The content is from July 2025 and does not indicate multiple speakers or a conversation involving Robert Ta.
Personalization relates to interpretability. Let me tell you what I mean by that. Interpretability is really the system's understanding of the user. And if we separate the user's perception of themselves and the reality of who they are, those are really different lenses on things. That's really complicated. Everybody could kind of relate to not having quite the same lens on reality that everybody else has. Right. We call that communication. Communication is hard. So understanding what somebody believes, who they really are, how they perceive the world, all of those things make up the interpretability and the personalization that a software system can provide to the user. So then how do you model that? And that's exactly what we do with Epistemic Me, our open source SDK for AI alignment. So it really just starts with can the software system understand the user in the best way? And then ultimately, if like a coach, the AI or the agent meets the user where they're at and and recommends the next best thing or asks the next best question to then recommend the next best thing. I think that type of interaction, we want to make that feel like the user is seen and heard. And those are very subjective things. But from an objective perspective, if you're personalizing experiences in an AI way to a user, the user probably wants to come back more. They probably engage more with it. They go up the value ladder of your AI. So high level, it's such an easy statement to say every AI application should be personalized. But when you get to the lower level details of system interpretability of the representation of the person and their beliefs and their perception, that's where it gets really, really complicated. And that's what we do really well.
