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Non-Technical Resources for Understanding Artificial Intelligence and Machine Learning

Having recently read Hannah Fry’s “Hello World: Being Human in the Age of Algorithms,” I realized, upon reflection, that there’s an excellent body of accessible publications that can help anyone—even the most technologically illiterate—build a solid conceptual understanding on the subject of artificial intelligence (AI).1

What follows is a list of some of the sources I have used to build my own basic level of knowledge on the subject. I want to be very clear that these resources are all introductory in scope. Moreover, not all deal directly with artificial intelligence, but in aggregate, these resources will get the lay person to a decent place of technological literacy along with a solid understanding of the important ethical, cultural and economic implications.

Given the significant impact of these technologies—on a broad range of activities including education, healthcare, law enforcement, transportation to name a few—having some understanding is a good start for making intelligent decisions and being well-informed in our rapidly evolving high-tech future.


Foundations

While not strictly necessary, these are helpful for learning about how computers work.

  • What is Code? by Paul Ford (bloomberg.com)
    This is a free, long-form article (that reads like a short book) that was published by Bloomberg in 2015. It’s a fantastic introduction to computer programming for the layperson.
  • Code: The Hidden Language of Computer Hardware and Software by Charles Petzold (charlespetzold.com)
    If you have the time, Petzold’s book, which predates the Ford article, is effectively a longer, more thorough version of the Ford piece.
 

AI Basics

These resources offer gentle introductions to the topic of AI.

  • The A-to-Z of AI: Making Sense of Artificial Intelligence (withgoogle.com)
    Google-sponsored website that offers bite-sized articles on a variety of important AI topics. Content is shallow, but it will provide a good overview of current issues and terminology.
  • AI for Everyone by Andrew Ng (coursera.org)
    Andrew Ng is an important figure in AI and education. His online classes are among the most popular in the space. While I don’t think he’s the best lecturer out there, his authority on the subject is bar none. This is his most accessible and basic course (and free at time of writing).
  • Machine Learning for Absolute Beginners by Oliver Theobald (amazon.com)
    Short and straight-forward book on the topic of machine learning (the current dominant branch of AI).
 

AI Context & Implications

These are general overviews of AI with strong cultural, ethical and societal discussions as well. Any of the books below could arguably serve as a decent starting point for the learner.

 

Supplemental

These resources were extremely helpful in reinforcing and improved understanding about key AI concepts (especially some of the more difficult ideas).

  • Deep Learning and Neural Networks by Grant Sanderson (3blue1brown.com)
    4-part video series that does the best job I’ve seen of helping visualize how a neural network actually works. Sanderson’s YouTube channel is brilliant if you want clear explanations on complicated mathematical topics.
  • Numsense! Data Science for the Layman by Annalyn Ng and Kenneth Soo (amazon.com)
    It isn’t about AI per se, but all of the concepts it teaches are 100% applicable to the domain. It covers a range of data topics like regression analysis, vectorization, decision trees and more.
 

Springboards into Advanced Topics

If you want to move beyond the basics, these resources will help you get there. These items start to take you out of the ‘non-technical’ purview of the preceding recommendations.

  • Learning AI if You Suck at Math by Daniel Jeffries (hackernoon.com)
    If you want to dive deeper into the subject, this 7-part series will get you much, much deeper into the weeds.
  • Neural Networks and Deep Learning by Andrew Ng (coursera.org)
    This is the introductory course to Coursera’s Deep Learning Specialization track. It is highly technical but very rewarding. If you want to dive into the deep-end of the pool, go here.

1 While I dabble in computer programming, by no means do I consider myself a computer programmer. I know just enough to cobble something together useful, like this side project, and just enough to be somewhat dangerous.



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