How have we gotten to this point with machine learning? And where are we going?
In this episode of the a16z podcast, a16z Operating Partner Frank Chen asks these (and many other questions) to one of the OG researchers and teachers of machine learning, Professor Tom Mitchell of Carnegie Mellon University. Tom has worked in this field for decades. He’s published the research, written and edited the books, testified to Congress, taught the classes, won the awards.
First, the two stroll down memory lane, visiting the major landmarks: the symbolic approach, the “principled probabilistic methods”, today’s deep learning phase, then go on to explore the frontiers of research. Along the way, they cover:
- How planning systems from the 1970s and early 1980s were stymied by the “banana in the tailpipe” problem
- How the relatively slow neurons in our visual cortex work together to deliver very speedy and accurate recognition
- How fMRI scans of the brain reveal common neural patterns across people when they are exposed to common nouns like chair, car, knife, and so on
- How the computer science community is working with social scientists (psychologists, economists, and philosophers) on building measures for fairness and transparency for machine learning models
- How we want our self-driving cars to have reasonable answers to the Trolley Problem, but no one sitting for their DMV exam is ever asked how they would respond
- How there were inflated expectations (and great social fears) for AI in the 1980s, and how the US concerns about Japan compare to our concerns about China today
- Whether this is the best time ever for AI and ML research and what continues to fascinate and motivate Tom after decades in the field
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