Vincent Sitzmann, a postdoc at MIT, specializes in neural scene representations for computer vision. He discusses the crucial shift from 2D to 3D representations in AI, emphasizing how our understanding of vision should mirror the 3D nature of the world. Topics include the complexities of neural networks, the relationship between human perception and AI, and advancements in training techniques like self-supervised learning. Sitzmann also explores innovative applications of implicit representations and shares insights on effective research strategies for budding scientists.
