I was lucky enough to start my job as a product manager just as a machine learning project was underway! I was able to jump on board and get involved.
I’m glad I have built up a basic knowledge of machine learning over the past few years, as it was very helpful in being able to ask the right questions and have quality conversations with the lead developer.
I want to share some machine learning (ML) and artificial intelligence (AI) resources for non-developers interested in learning more.
- People & AI Guidelines, from Google
- I highly recommend checking this out if you’re involved with data or design for AI. From the beginning to the end of the AI process, this contains practical tips, examples, and insightful research findings
- You can find more resources from the same team in their AI library
- AI in Industry Podcast
- This podcast focuses on real-world uses of AI, featuring interviews with people who have implemented AI in various industries
- It discusses not just the implementation of AI, but everything that goes around it, including data gathering/cleaning and necessary culture changes
- Creative Next PodCast
- “Creative Next future-proofs designers, engineers, writers, marketers, and entrepreneurs to prepare for collaboration with smart machines“
- This is sometimes less practical and more exploratory than the one above, but interesting and insightful
- A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control (book)
- As the description says, this book is “an entertaining and provocative look at one of the most important developments of our time and is a practical user’s guide to this first wave of practical artificial intelligence”
- This book does a great job of telling interesting stories about real-world implementations of AI and the (often unintended) side effects of those use cases. It will increase your knowledge of how AI can be used, and help increase your awareness of related ethical considerations
- Also, consider checking out IBM’s AI essentials course
- Learn about designing AI in a team, with a framework and tools to help you out with responsible AI and working with data
- Note: You need to have taken their design thinking course first
- Added 8.26.20, special thanks to Matt Holfelner for the suggestion!
- Human-Centered Machine Learning
- An excellent article about thinking about machine learning from a human perspective, beginning with whether it’s even an appropriate solution to your problem
If you’re not a developer but interested in learning more about how AI works, check out these resources:
- Elements of AI , a free online course
- Created by the University of Helsinki and AI startup Reaktor, this free course teaches the basics of AI to people from a wide variety of backgrounds
- LinkedIn Learning Artificial Intelligence Foundations: Machine Learning
- This course explains the three core types of machine learning (supervised, unsupervised, reinforcement learning)
- It discusses various algorithms to consider for each
- It also talks about some of the challenges you will run into when starting out with ML
- The teacher does a great job of breaking things down and using analogies to help you understand the concepts
(added 8.26.20) If you’re looking to learn more about responsible AI, check out these resources:
- The Algorithmic Justice League
- Focusing on ethical and accountable AI, this site – and their newsletter – focuses on the social implications and potential harm that AI can do
- Google’s Responsible AI principles
- Learn about how Google is trying to ensure their AI efforts will benefit everyone
- Microsoft’s AI for Good
- Read about real-world efforts to employ AI for good
What other AI resources have you enjoyed?