There are 2 main approaches to AI (symbolic vs connectionism), and they are very different in how they think about producing intelligence in the machine.
One approach believes in programming what we know about the world into the machine, and the other ignores any pre-existing knowledge by relying on large datasets and correlation.
There is a long-standing debate between which approach is best, despite only one of these approaches having much success.
The reason this debase still exists is because it gets to the heart of a deeper discussion on the role knowledge plays in intelligence.
In this episode I use the AI debate to uncover what I believe is a deeper problem in how people think about learning, and relate this to implications for our everyday lives.
Become a Member at nontrivialpodcast.com or patreon.com/8431143/join
Premium members get access to the full member app. This includes data visualizations of the core concepts in each episode, a Study Space for learning fundamentals, and premium articles on Techniques and Mindsets.
Members can also save personal notes, explore episode summaries and transcripts, search across episodes, track watch history and progress, and participate in the community forum. Premium membership includes ongoing support.
