I’m excited to announce the launch of my latest Massively Open Online Course (or MOOC) called Google AI for Anyone, created with the lovely folks from HarvardX and EdX.
The goal of this course, as its name suggests, is to be a broad introduction of what AI is (and perhaps more importantly, what it isn’t) designed for everybody to enjoy!
No engineering, no programming, no math required.
The course will begin with an introduction to what AI is all about and understand where it stands in the hype cycle. I’ll demonstrate examples where common conceptions about AI are wrong and how we can easily fall into fear about the possibilities instead of being encouraged by the opportunities.
Through understanding the hype lifecycle and getting to the point of understanding the realities of AI, we’ll also look at examples where people followed the curve and became productive. I’ll share a fun anecdote of how the actor David Hewlett of Stargate fame broke through the hype and came up with some great innovative ideas for using AI in his industry!
Using the examples of games, medicine, self-driving cars, and others, we’ll dig into how people build AI systems using data. If you’re interested in building AI models, I have other courses and books to help!
If you want, once you’ve gained a high-level understanding of how AI and Machine Learning work, we’ll also go a little lower and explore the anatomy of an artificial neuron and how it’s just a simple mathematical function. When these functions work together similarly to biological neurons, exciting things can happen!
The course will wrap up with a discussion about the ethics of AI systems, particularly when used to make important decisions about people’s lives, and a roundup of some of the tools that will help you avoid mistakes!
You can access the course on edX today!
Here’s the table of contents:
Chapter 1: Introduction to AI
Chapter 1 Introduction
1.1. What is AI?
1.2. Understanding Data
1.3. Play with Teachable Machine
1.4. Applications of AI - Computer Vision
1.5. Play an Online Driving Game
1.6. From Data to Training
1.7. Play Tic Tac Toe
1.8. Other AI Applications
1.9. Chapter 1 Summary
Chapter 2: How Machines Learn
2.1. Recap of Chapter 1
2.2. How Neurons Work
2.3. What is Machine Learning?
2.4. About Neurons
2.5. What is Deep Learning
2.6. Deep Learning for Computer Vision
2.7. Reevaluating the Teachable Machine
2.8. Recap of Chapter 2
Chapter 3: Ethics and Bias in AI
3.1. Chapter 1 and 2 Recap
3.2. Understanding Bad Data
3.3. Teachable Machine with Bad Data
3.4. Explore Bias Busting Tools
3.5. AI Ethics and Bias: More than Data
3.6. Responsible AI Guidelines
3.7. Watch the TF Community Day Video
3.8. Principles and Tools for Responsible AI
3.9. Recap and What’s Next