While its role in society will only expand, a lot of unknowns remain about AI. And that’s because AI, as a topic, is almost too unwieldy, and you can approach it in a variety of ways. There is an entire philosophical avenue of thought when it comes to AI while the opposite end of the spectrum contains highly technical interpretations.
AI casts a long shadow and as such, education and understanding will be vital going into the future. Below are a few books to get started on the road to grasping AI and all it has to offer. Check out other important AI books here.
The Book of Why, by Judea Pearl and Dana Mackenzie
The idea of “correlation is not causation” has long been an established maxim of scientific research, but texts like The Book of Why are bringing different perspectives to the forefront. Well, that may be going a little too far. If anything, authors Judea Pearl and Dana Mackenzie suggest a different way to look at things, specifically by establishing a language of causality. The authors put cause and effect to the test for numerous examples and summarize that we don’t really have proof for why one thing happening may cause another thing to happen.
How AI operates—through repetition exercises that suss out predictable patterns and correlations—obscures the fact that a lot of AI systems fail to reveal why one thing caused another. The Book of Why reexamines and sheds new light on AI systems and how they could develop in the future.
Weapons of Math Destruction, by Cathy O’Neil
Weapons of Math Destruction breaks down the algorithm’s stranglehold on current society and how these AI systems determine more of our lives than we think. O’Neil, a former Wall Street quant turned Occupy Wall St. demonstrator, helpfully provides sharp examples of how algorithms touch different sectors including finance, academia, and public institutions.
The grip of the algorithm will only get stronger. Weapons of Math Destruction serves as a wakeup call into how systems data gather and how those in power use it.
Sorting Things Out: Classification And Its Consequences, by Geoffrey C. Bowker and Susan Leigh Star
A key component of AI and its mechanisms comes down to the fact that AI is a classification system. The AI system takes its data and classifies it into ways that cause an outcome. The system does this over and over until it produces a comprehensible outcome that makes sense from its data points.
What Sorting Things Out delves into is the larger meaning behind classification systems, i.e. the book posits that systems of classifications are often dictated by institutional power and cultural norms. Though it may not explicitly deal with AI for the bulk of the book, its analysis of classification systems easily translates to AI systems and furthers our understanding of them.
Machine Learning For Humans, by Vishal Maini and Samer Sabri
Though AI has been around for years and has been implemented in various areas of our lives, the truth is that the vast majority of people still do not understand its basic workings. Luckily, free texts like Machine Learning For Humans by Vishal Maini and Samer Sabri break down AI’s mysteries. With AI becoming more embedded in society, educating people on its functions will become even more crucial.
The Master Algorithm, by Pedro Domingos
If you want to go deeper into the inner workings of AI, The Master Algorithm by Pedro Domingos is a good place to begin. Domingos’ writing is all-encompassing and provides accessible insight into the world of machine learning and how the world’s biggest tech companies are helping further these complex, yet imperfect systems. Non-experts on machine learning can learn a lot here.