This will be a living post with links to resources I think are useful (or, in some cases, that I simply want to remember to look at) for non-ML-pros who want to understand machine learning and how it will change [work/society/etc]. Full disclosure, I worked on much of the HBR content. And while I vouch for a lot of the links below, like I said, some I haven’t yet read.
Overviews of machine learning or AI
An introduction to statistical learning (textbook)
Machine learning 101.
Andrew Ng: What Artificial Intelligence Can and Can’t Do Right Now
A visual introduction to machine learning
An NBER introduction to AI.
What Everyone Needs to Know About AI (book)
Andreessen Horowitz’s primer on AI and AI playbook
This MIT Technology Review article contains a fantastic plain english description of deep learning.
This slightly violates the non-technical rule, but there are many great MOOCs, several of them on Coursera. Andrew Ng’s classic ML course; his deep learning courses; and many more.
Intro to neural networks in 20 minutes
The economics of machine learning
The single best starting point (and in book form)
Here is an NBER conference on the topic.
Big picture, trends, etc.
This CB Insights report is good on the VC-backed ML ecosystem.
Shivon Zilis’s various mappings are great comprehensive looks at the companies involved.
Benedict Evans on the next 10 years.
The New York Times on the “Great AI Awakening”
Erik Brynjolfsson and Andrew McAfee with the big picture
How to spot a machine learning opportunity even if you aren’t a data scientist
What every manager should know about machine learning
How to tell if machine learning can solve your business problem
Slightly different, but a good short tutorial on predicting who died in the Titanic
Other lists of links
from HBR ($)
The best ML resources.
On bias and social impact.
Events, newsletters, etc.
Data Elixir newsletter
Tech Review’s The Algorithm newsletter
Conference: Machine Learning and the Market for Machine Intelligence