By Oren Etzioni and the AI2 Team
At AI2, we have liberal doses of AI and machine learning with our coffee. So, we are often asked for resources on AI by both technical and non-technical friends eager to learn more about this hot topic. It’s not that our friends can’t do a Google search — there are too many resources out there, and it’s hard to tell which are good and which are confusing; what serves as a gentle introduction, and what’s for more advanced interest.
In response, we’ve put together a brief list of high-quality resources. We’ve erred on the side of brevity instead of completeness. However, we are maintaining a “living document” of these resources at AI2, so please make recommendations in the comments section, and we will update and refine the list over time.
Engineers can scroll down to find technical resources in the next section.
If you’d like a very brief no-nonsense introduction to AI, see MIT Technology Review’s summary: What is AI?
That summary is coupled with a crisp overview of Machine Learning terminology: What is Machine Learning?
Both are authored by Karen Hao and feature elegant flow charts to guide your understanding.
For a comprehensive dive into AI and its applications, we recommend Andrews Ng’s AI for Everyone Coursera series.
To cut through some of the hype surrounding AI
We recommend the following brief, popular articles by Oren Etzioni:
- Deep Learning Isn’t Magic (Wired, 2016)
- No, the Experts Don’t Think Superintelligent AI is a Threat to Humanity (MIT Technology Review, 2016)
- AI Won’t Exterminate Us — it Will Empower Us (Backchannel, 2014)
If you want more depth, this Harvard Business Review article by MIT Professors Erik Brynjolfsson and Andrew McAfee is an insightful and elegantly written overview: The Business of Artificial Intelligence
On regulatory and ethical issues, we recommend the following:
- Should AI Technology Be Regulated?: Yes, and Here’s How (Oren Etzioni, CACM, 2018)
- Are We Having An Ethical Crisis in Computing? (Moshe Vardi, CACM, 2019)
- Ethically Aligned Design, First Edition (IEEE, 2016)
- AI Now Report 2018
Finally, we recommend two outstanding books that provide an overview of the field and its implications for the future:
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos, Professor, Paul G. Allen School of Computer Science & Engineering, University of Washington
- Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee (Principal Research Scientist at MIT) and Erik Brynjolfsson (Director of the MIT Center for Digital Business)
Technical Resources for Engineers
For a gentle introduction, an engineer might start with these AI overview presentations prepared by AI2 team members:
For more depth, we recommend this insightful review article by UW Prof. Pedro Domingos: A Few Useful Things to Know about Machine Learning
For developing your own machine learning skills
Many people recommend online courses including:
- Andrew Ng’s Machine Learning Coursera class
- Coursera & UW’s Machine Learning Specialization classes created by Profs. Carlos Guestrin and Emily Fox
To delve specifically into Deep Learning, we recommend:
- A truly introductory tutorial of RL & Deep: Faizan Shaikh’s blog post, Simple Beginner’s Guide to Reinforcement Learning & its Implementation
- Andrew Trask’s book, Grokking Deep Learning, which teaches you to build deep learning neural networks from scratch
- A code first introduction from Fast.ai: Deep Learning from the Foundations shows how to build a state of the art deep learning model from scratch
- Andrew Ng’s follow up Deep Learning classes explain CNNs and RNNs and how to apply them
- The 3Blue1Brown YouTube series on neural networks: Probably the best visual presentation of deep learning (especially Chapters 3,4 about Backpropagation). Good for moderately technical people with some calculus background, but new to deep learning.
Stanford and CMU make their course materials available online here:
Additional technical resources
If this brief list isn’t sufficient for you, please see the additional resources provided by Aditya Gupta: Best Resources to learn AI, Machine Learning & Data Science.
We also recommend the hands-on book Data Science from Scratch: First Principles with Python by AI2’s Joel Grus. Be sure to get the second edition (and note the many positive reviews of the first one!).
Finally, here’s a practical note from a tweet by Joel: