Explore deep learning limitations and emerging frontiers in this lecture from MIT's Introduction to Deep Learning course. Delve into the rise of deep learning, the power of neural networks, and historical perspectives on AI hype. Examine neural networks as function approximators and their capacity for generalization. Investigate adversarial attacks on neural networks and the synthesis of robust adversarial examples. Understand the importance of uncertainty in deep learning and explore Bayesian approaches for quantifying model uncertainty. Learn about multi-task learning using uncertainty and the concept of learning to learn through AutoML. Discover how AutoML works on the cloud and spawns powerful ideas for the future of deep learning.