Jabril tries to make an AI to settle the question once and for all, “Will a cat or a dog make us happier?” But in building this AI, Jabril will accidentally incorporate the very bias he was trying to avoid. So today we’ll talk about how bias creeps into our algorithms and what we can do to try to account for these problems. https://colab.research.google.com/drive/1N5IdMTmiNbwEOD8dqammN8GAfpk41arw

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.

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.

Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to build a movie recommender system to find that perfect movie. Follow Along: https://colab.research.google.com/drive/1-v9cw18wTDjaCUlECKHsQnHeisLKyG8U

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.

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.

Our game is called TrashBlaster, and it’s like Asteroids but with trash in the ocean, and instead of a spaceship John Green Bot is wielding a laser. We’ll use machine learning techniques such as an evolutionary neural network alongside a carefully crafted fitness function to create an unstoppable AI.

Follow along: https://colab.research.google.com/drive/1uYXTDeBbPeuJfM1teufZ9nUaiRIN9nHW

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.

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.

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 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.