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Today we have the pleasure of being joined by Tom Dietterich, Distinguished Professor Emeritus at Oregon State University.
If you're apart of #MachineLearning Twitter, then you've absolutely seen the debate "what is machine understanding?" pop up recently. Many have weighed in, including recent podcast guest Gary Marcus. Tom, a foundational researcher in what know now as machine learning, recently threw his hat into the ring with his blog post "What does it mean for a machine to "understand", and in our conversation, goes into great detail examining these opinions. We cover a lot of ground, including Tom's position in the debate, examples of what he believes understanding is and how even the most narrow examples of it fall under this umbrella, and his thoughts on the role of deep learning in potentially getting us to AGI. We also discuss the "hype engine" around even the most nominal advancements. Finally, check out the "From the interview" portion of the show notes page, as Tom dropped a ton of interesting nuggets and shouted out quite of few great researchers.
Blog Post: "What does it mean for a machine to "understand""
Episode #298 - Gary Marcus - Rebooting AI: What's Missing, What's Next
The Shallowness of Google Translate - Douglas Hofstadter
Searle's "Chinese Room" Argument
Towards Artificial General Intelligence with Greg Brockman - TWIML Talk #74
Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru
"Fairwashing" and the Folly of ML Solutionism with Zachary Lipton - Talk #285