When people talk about artificial intelligence, they usually donβt mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.
Reinforcement learning has recently become popular for doing all of that and more.
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasnβt been until recently that weβve been able to observe first hand the amazing results that are possible.
In 2016 we saw AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Self-driving cars have started driving on real roads with other drivers and even carrying passengers, all without human assistance.
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
Yet learning about supervised and unsupervised machine learning is no small feat. To date I have over 16 courses just on those topics alone.
And still reinforcement learning opens up a whole new world. As youβll learn in this book, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
Itβs led to new and amazing insights both in behavioral psychology and neuroscience. As youβll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. Itβs the closest thing we have so far to a true general artificial intelligence.