How have we gotten to where were are with machine learning? Where are we going?
a16z Operating Partner Frank Chen and Carnegie Mellon professor Tom Mitchell first stroll down memory lane, visiting the major landmarks: the symbolic approach of the 1970s, the "principled probabalistic methods" of the 1980s, and today's deep learning phase. Then they 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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