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Crash Course: Artificial Intelligence
Reinforcement Learning #9
Season 1
Episode 9
Reinforcement learning is useful in situations where we want to train AIs to have certain skills we don’t fully understand. We’re going to explore these ideas, introduce a ton of new terms like value, policy, agent, environment, actions, and states and we’ll show you how we can use strategies like exploration and exploitation to train John Green Bot to find things more efficiently next time.
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10:50
In our final episode of Crash Course AI, we're going to look towards the future.
13:01
Jabril tries to make an AI to settle the question once and for all.
10:56
We're going to talk about 5 common types of algorithmic bias we should pay attention to.
11:08
Search engines are just AI systems that try to help us find what we’re looking for.
14:33
We need to save Jabril and John Green Bot’s movie nights.
10:19
We’re going to talk about recommender systems.
9:56
We’re going to focus on the benefits of humans and AI working together.
13:06
We create a game and then build an AI to destroy it.
11:11
One of the best test spaces for building new AI systems are games.
9:52
Robots are built to perform specific tasks.
13:01
Symbolic AI represents problems using symbols and then uses logic to search for solutions.
15:17
Let’s try to help John Green Bot sound a bit more like the real John Green using NLP.