Science and Nature

Crash Course: Artificial Intelligence

Welcome to Crash Course Artificial Intelligence! In this series host Jabril Ashe will teach you the logic behind AI by tracing its history and examining how it’s being used today. We’ll even show you how to create some of your own AI systems with the help of co-host John Green Bot! AI is everywhere right now and has the potential to do amazing things in our lives.

The Future of Artificial Intelligence #20

10m 50s

We've spent much of this series explaining how and why we don't have the Artificial General Intelligence that we see in movies. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube & Netflix often aren't what we really want to watch next. Let's talk about what we do know, how we got here, and where we think it's headed

Episodes

  • The Future of Artificial Intelligence #20: asset-mezzanine-16x9

    The Future of Artificial Intelligence #20

    S1 E20 - 10m 50s

    We've spent much of this series explaining how and why we don't have the Artificial General Intelligence that we see in movies. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube & Netflix often aren't what we really want to watch next. Let's talk about what we do know, how we got here, and where we think it's headed

  • Algorithmic Bias and Fairness #18: asset-mezzanine-16x9

    Algorithmic Bias and Fairness #18

    S1 E18 - 10m 56s

    We're going to talk about five common types of algorithmic bias we should pay attention to: data that reflects existing biases, unbalanced classes in training data, data that doesn't capture the right value, data that is amplified by feedback loops, and malicious data.

  • Web Search #17: asset-mezzanine-16x9

    Web Search #17

    S1 E17 - 11m 8s

    Search engines can be the sort that serve up a list of results, like during a Google or Bing search, using web crawlers, an inverted index, and measuring stuff like click through and bounce back to figure out what you want to see. They can also be the kind that give you answers, like when you ask Siri or Alexa a question, relying on knowledge bases.

  • How YouTube Knows What You Should Watch #15: asset-mezzanine-16x9

    How YouTube Knows What You Should Watch #15

    S1 E15 - 10m 19s

    We’re going to talk about recommender systems which form the backbone of so much of the content we see online from video recommendations on YouTube and Netflix to ads we see on Facebook, Twitter, and everywhere else. We’ll talk about three types of systems - content-based, social, and personalized recommendations - and take a closer look at what they're good at, but also why they often fail.

  • Humans and AI Working Together #14: asset-mezzanine-16x9

    Humans and AI Working Together #14

    S1 E14 - 9m 56s

    Human-AI teams allow us to fill in each others weaknesses leveraging human creativity and insight with the ability to perform rote manual tasks and synthesize lots of information. This kind of collaboration can help us make better decisions, brainstorm new inventions, give us superhuman abilities, rescue victims of natural disasters, and of course become the ultimate chess master.

  • AI Playing Games #12: asset-mezzanine-16x9

    AI Playing Games #12

    S1 E12 - 11m 11s

    One of the best test spaces for building new AI systems are games. This is because games provide a great framework for an AI to learn an objective and slowly improve. We’re going to walk you through creating a Tic Tac Toe bot that uses the minimax algorithm to become undefeatable and we’ll talk about evolutionary neural networks like in SethBling’s MarI/O project.

  • Robotics #11: asset-mezzanine-16x9

    Robotics #11

    S1 E11 - 9m 52s

    Robots aren’t like humans who can do a lot of different things. They’re designed for very specific tasks like vacuuming our homes, assembling cars in a factory, or exploring the surface of other planets. Today, we're going to take a look at the role of AI in overcoming three key challenges in the field of robotics: localization, planning, and manipulation.

  • Symbolic AI #10: asset-mezzanine-16x9

    Symbolic AI #10

    S1 E10 - 13m 1s

    This type of AI is used broadly in video games and in expert systems like those that manage inventory at grocery stores and set rates at insurance companies. We'll show you how we represent symbols and their relations, teach you how to build a knowledge base, and then introduce some simple propositional logic that is at the heart of these AI systems.

  • Reinforcement Learning #9: asset-mezzanine-16x9

    Reinforcement Learning #9

    S1 E9 - 11m 7s

    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.

Extras + Features

  • Crash Course Artificial Intelligence: asset-mezzanine-16x9

    Crash Course Artificial Intelligence

    3m 30s

    Host, Jabril Ashe will teach you the logic behind AI by tracing its history and examining how it’s being used today. We’ll even show you how to create some of your own AI systems with the help of co-host John Green Bot! We’ll also spend several episodes on an area of AI known as machine learning which has skyrocketed in popularity in recent years.

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