As part of some research into artificial intelligence (AI) and machine learning (ML) over the past few months, I’ve come across a lot of reading material.
Here’s some that stood out to me and that I can recommend looking into. Please note that this is very much on the non-technical end of the spectrum: Primers, as well as pieces focusing on societal impact, ethics, and other so-called “soft” aspects, i.e. societal, political, humanitarian, business-related ones. These are the types of impact I’m most interested in and that are most relevant to my work.
The list isn’t comprehensive by any means—if you know of something that should be included, please let me know!—but there’s a lot of insight here.
Resources, reading lists, content collections:
- Intelligence and Autonomy Initiative by Data & Society‘s initiative develops policy research connecting the dots between robots, algorithms and automation.
- O’Reilly’s AI section is an ace collection on everything from hands-on to quite meta.
- Interactive Machine Learning, a reading list by Greg Borenstein
- Machine Learning for Designers by Patrick Hebron
- Weapons of Math Destruction by Kathy O’Neil (Thanks for the pointer, Chris!)
- The Dark Secret at the Heart of AI in Technology Review
- Artificial Intelligence Owes You an Explanation by John Frank Weaver
- We Need to Tell Better Stories About Our AI Future by Sara M. Watson
- Experience Design in the Machine Learning Era by Fabien Girardin
- The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy
- Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems by Greg Borenstein
- Humanitarian efforts could be aided by AI, a great example of how to put AI to good use.
- How to Learn Anything in the Age of AI by Greg Amrofell
- Prioritizing Human Well-being in the Age of Artificial Intelligence (PDF) by the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems
- Ethics for the AI Age by Cennydd Bowles
For completeness’ sake (and as a blatant plug) I include three recent blog posts of my own:
- AI: Process v Output. Machine learning and artificial intelligence (AI) are beginning to govern ever-greater parts of our lives. If we want to trust their analyses and recommendations, it’s crucial that we understand how they reach their conclusions, how they work, which biases are at play. Alas, that’s pretty tricky. This article explores why.
- First steps with AI & image recognition (using TensorFlow), in which I get hands-on to learn some very basic machine learning techniques.
- Some thoughts on Google I/O and AI futures, my initial thoughts on Google I/O’s AI-related announcements.