The Evolution of AI: From Algorithms to Deep Learning

Why are people so fired up about a computer winning yet another game? Whether it's checkers, chess, Jeopardy, or the ancient Chinese game of Go, we get excited about the potential for more when we see computers beat humans. But then nothing "big" -- in terms of generalized artificial intelligence -- seems to happen after that burst of excitement.

Now, with the excitement (and other emotions) around Google DeepMind's "AlphaGo" using machine learning and other techniques to beat one of the world's top Go players, Lee Sodol, in Korea ... it's like the dream of the 1990s (and 1980s, and 1970s, and 1960s) is alive in Seoul right now. Is this time different? How do we know?

a16z's head of research and deal team Frank Chen and board partner Steven Sinofsky -- who both suffered through the last “AI winter” -- share how everything old is new again; the triumph of data over algorithms; and the evergreen battle between purist vs. "practical" approaches. Ultimately, it's about how innovation in general plays out, at a scale both grand (cycles and gestation periods) and mundane (sometimes, the only way to make a product work is to hack together the old, the new, and everything in between).

NOTE: The Super Mario World video referenced in this podcast is at https://www.youtube.com/watch?v=qv6UVOQ0F44