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
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/1N5IdMTmiNbwEOD8dqammN8GAfpk41a…
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-v9cw18wTDjaCUlECKHsQnHeisLKyG…
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.
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.
We're going to code a program that takes a one word prompt and then completes the sentence that sounds like something John Green would say. We’re going to collect transcription files from Vlogbrothers episodes, do some preprocessing, then set up a recurrent neural network (RNN), train our model, and test it!
We're going to talk about Natural Language Processing, or NLP, show you some strategies computers can use to better understand language like distributional semantics, and then we'll introduce you to a type of neural network called a Recurrent Neural Network or RNN to build sentences.
We’re moving on from artificial intelligence that needs training labels, called Supervised Learning, to Unsupervised Learning which is learning by finding patterns in the world. We’ll focus on the performing unsupervised clustering, specifically K-means clustering, and show you how we can extract meaningful patterns from data even when you don't know where those patterns are.
John Green Bot wrote his first novel! We’re going to program a neural network to recognize handwritten letters to convert the first part of John Green Bot’s novel into typed text.
We’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error.
Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning.
Supervised learning is the process of learning WITH training labels, and is the most widely used kind of learning with it comes to AI - helping with stuff like tagging photos on Facebook and filtering spam from your email. We’re going to start small today and show how just a single neuron (or perceptron) is constructed, and explain the differences between precision and recall.
Artificial intelligence is everywhere and it's already making a huge impact on our lives. It's autocompleting texts on our cellphones, telling us which videos to watch on YouTube, beating us at video games, recognizing us in photos, ordering products in stores, driving cars, scheduling appointments, you get the idea.