What Books Are We Reading On AI and Machine Learning in 2020?

Whether you are a seasoned professional in this industry or just starting to dip your toes in, there is always more to learn about AI and machine learning. 

IDC reports that many businesses across the globe are increasing their AI spending in 2020 to improve customer experience, employee productivity, and open new business opportunities.

Whether you’re directly involved with building machine learning models, testing algorithms, and labeling training data or not, these books will help you understand the possibilities of AI and its impact on businesses.

Here’s a list of some of the books that we’ve been reading over the last few months. We hope you enjoy reading them while quarantined at home or spending time on professional growth.  

1. Active Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning) - Burr Settles

Although this isn’t a new ebook, it’s definitely worth reading. It provides a basic introduction to the concept of active learning. The key idea is that if you allow a machine-learning algorithm to choose the data from which it learns, it will work faster and more efficiently with much less training. 

You function as an “oracle,” to help determine what data will improve the efficiency of the model. In an active learning model, you only need to label those specific datasets. The AI will pose “queries” to you, and you get to determine which datasets are worth working through. 

This helps AI learn more quickly and efficiently by labeling queries it poses to you which will be beneficial. Considering that at least 80% of AI project time is spent organizing and labeling data, this can save hours of work over the life of a project.

Settles’ lecture gives you a basic introduction to this idea, discusses the theoretical foundations of active learning, and attempts to summarize the current work in this area. 

If you’re interested in this concept of active learning but don’t feel confident in your knowledge, this ebook is a great place to start. Hear why Infinia ML data scientist, Ben Schneller is excited about active learning in our recent webinar, Data Prep: What Data Scientists Wish You Knew.  

2. AI Superpowers: China, Silicon Valley, and the New World Order China, Silicon Valley, and the New World Order - Kai-Fu Lee

Dr. Kai-Fu Lee is one of the world’s leading experts on AI and machine learning. In his book, he argues that although the US has been at the forefront of AI technology for a long time, China is catching up more quickly than most realize. 

While some consider his views to be debatable, particularly his take on China’s copying U.S. technology and his predictions about AI’s potential effects on the jobs, he offers insights on the potential effects of this U.S.-China AI race on the world.

He talks at length in the book about who will be affected by these changes and what you need to do now to prepare for them. This is a great AI book if you are curious about what the far-reaching economic and societal impacts of an AI-driven world could be. You can also hear from Dr. Lee in this PBS documentary, In the Age of AI.

3. Human + Machine: Reimagining Work in the Age of AI - Paul Daughtery & H. James Wilson

Paul Daughtery and Jim Wilson’s book is written specifically for business leaders. They want you to be able to take advantage of the benefits of using AI in your business and help you create new roles for your employees in the coming AI-enriched workforce. 

The authors argue that businesses who are able to quickly adapt to and harness the power of AI will grow leaps and bounds ahead of those who don’t. AI, they say, is not just an addition to existing business processes, but instead will mark a total paradigm shift in the way people do business. 

In fact, they find this so important that they are donating all royalties from their book sales to help fund education and training programs to help the coming workforce prepare for the coming age of artificial intelligence.

The book pulls on the authors’ knowledge and experience as well as research from more than 1,500 organizations across the world. If you want to prepare your business for changes AI technology will bring, this is a must-read.

Learn more about the role humans play in the AI tech stack in our white paper. 

4. Machine, Platform, Crowd: Harnessing Our Digital Future - Andrew McAfee and Erik Brynjolfsson

Written by two MIT alumni, this machine learning book isn’t focused on whether changes are coming, but instead on what we can do to prepare for the digital age. 

Concepts that were science-fiction have become reality. McAfee and Brynjolfsson expertly articulate the steps you should take to arm yourself and your business for “digital disruption.”

As jobs change and the workforce continues to adapt, this book works to help you prepare yourself to work alongside AI technology instead of fighting against it. This, they argue, is what will result in success for you and your business. Adapting has become especially important during the pandemic. 

Our guide will help ensure you choose an outsourcing vendor that can adapt to changing conditions.

5. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass - Mary Gray and Siddharth Suri

Although this book is a little adjacent to AI, it’s well worth the read. Authors Mary L. Gray and Siddharth Suri take a deep-dive into the invisible human workforce that powers many automated systems that we use every day. 

Huge companies like Google, Apple, and Microsoft can only function thanks to an unseen, and often underpaid workforce. These “ghost workers” complete tasks like proofreading, flagging inappropriate content, and other projects that make the internet appear smarter.

This book provides valuable insight about the necessity of humans in the loop for all data work and how data workers can and should be treated equitably by their employers. Keep an eye out for CloudFactory mentions and learn how CloudFactory invests in its workforce in Kenya and Nepal.

We’ve published several free resources about outsourcing, data processing for machine learning, quality data labeling, and scaling data teams.

What are your favorite AI and machine learning books? Let us know in the comments below