Part 5/13:
Using the analogy of an onion, he described AI’s layered complexity—from basic machine learning to the sophisticated neural networks characteristic of deep learning. Each layer peels back to reveal more nuanced understanding, but also increases complexity and opacity, often termed the "black box" problem.
He explained machine learning as a process of training algorithms with data, allowing machines to recognize patterns and make autonomous decisions. He distinguished traditional machine learning from deep learning, the latter being inspired by the human brain’s architecture, capable of addressing more intricate problems without manual feature crafting.