Although it's a new complex area, there are already lots of great resources available if you'd like to learn more about AI. Here's some I found particularly interesting.
RESOURCES
If you have half an hour and want a good intro to AI from Silicon Valley Venture Capital firm Andressen Horowitz, check out: https://a16z.com/2016/06/10/ai-deep-learning-machines/
Then consider their AI Playbook which offers some useful guidance on using AI:
Good overview of DL: https://www.zdnet.com/article/what-is-deep-learning-everything-you-need-to-know/
McKinsey overview of AI:
A nice short visual intro to ML Machine Learning: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
An excellent AI overview:
Courses on AI
Machine Learning by Professor Andrew Ng on Coursera ($75 for certificate - 19 Hours)
Google Machine Learning Crash Course (Free - 15 Hours)
Bloomberg Foundations of Machine Learning Course with over 30 YouTube lectures (Free):
https://bloomberg.github.io/foml/#home
Online Services
https://cloud.google.com/products/ai/
https://azure.microsoft.com/en-us/overview/ai-platform/
https://aws.amazon.com/machine-learning/
Downloads to Try
https://www.tensorflow.org/tutorials/
Downloads for Developers Only
You need to be a developer to try this one:
Build a simple Android app for Image Recognition with Tensorflow
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#0
Other things to try
If you’re not a developer but would like to try to build your own speech recognition or computer vision projects, try these kits from Google: https://aiyprojects.withgoogle.com/
Interesting Links
A thought-provoking Speculative Design exercise from Google:
https://www.theverge.com/2018/5/17/17344250/google-x-selfish-ledger-video-data-privacy
Video of robot picker in a grocery warehouse:
https://www.bbc.com/news/av/technology-42158043/ocado-robot-picks-up-and-packs-supermarket-goods
An excellent resource on AI vs Human performance across multiple areas: https://www.eff.org/ai/metrics
The challenges of Designing for Voice Interactions:
https://design.google/library/conversation-design-speaking-same-language/
More details on data quality and bias:
https://developers.google.com/machine-learning/fairness-overview/
https://research.google.com/bigpicture/attacking-discrimination-in-ml/
https://cloud.google.com/inclusive-ml/
The excellent annual Kleiner Perkins Internet Trends Report supplied many graphs referenced in the book