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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a16z Podcast: The History and Future of Machine Learning

The a16z Podcast discusses tech and culture trends, news, and the future -- especially as ‘software eats the world’. It features industry experts, business leaders, and other interesting thinkers and voices from around the world. This podcast is produced by Andreessen Horowitz (aka “a16z”), a Silicon Valley-based venture capital firm. Multiple episodes are released every week; visit a16z.com for more details and to sign up for our newsletters and other content as well!