Explore emergent intelligence in AI systems through this MIT Embodied Intelligence Seminar talk. Delve into three projects showcasing surprising outcomes beyond explicit programming: unsupervised learning discovering semantics without labels, generative models producing physically plausible "videos" from static images, and self-assembling "creatures" emerging from primitive limbs without centralized control. Gain insights into the goal of maximizing AI output while minimizing input constraints, and examine topics such as representation learning, colorization, neural representations, self-supervised objectives, generative modeling, latent variable models, multi-agent interactions, and self-assembly morphologies. Discover how these concepts relate to evolution in nature and the emergence of complex systems from simple components.