Join Amir Bar, a PhD candidate at Tel Aviv University and UC Berkeley, as he unpacks his groundbreaking research on visual-based learning and self-supervised object detection. He introduces ‘EgoPet,’ a unique dataset that captures animal behavior from their perspective, aiming to bridge the gap between AI and nature. The discussion dives into challenges of current classification methods, the significance of ego-centric data in robotic training, and the potential to enhance robotic navigation by mimicking animal locomotion. Exploration of these topics reveals fascinating insights into future AI advancements.