6 Books on AI To Read in 2020
- 08 Oct 2020
A good book is hard to come by these days and with Youtube videos, AI-related contents are easily digested at the expense of your time only. Educational blogs are also a good alternative for those who prefer reading in detail. Yet, nothing can top the immersive experience of a good book, so here is our list of 6 books on AI that you should get your hands on in 2020.
Hello World: Being Human in the Age of Algorithms by Hannah Fry
Hello World: Being Human in the Age of Algorithms
Fry wrote this book explaining what Artificial Intelligence, machine learning and complex algorithms mean and providing explanations behind many modern phenomena like driverless cars. She would even address tough questions like should a driverless car put a priority on protecting the driver or the person he is about to run over? Would you hand over your medical records to a company run by Artificial Intelligence knowing you might be able to help improve treatments?
Despite being a passionate advocate for maths and technology, Fry suggests we do not put too much faith in them. Humans are somehow intimidated by algorithms, at the same time in awe of their capabilities and when we overtrust anything we do not understand. She believes that by working together, humans and AI can be the perfect team. Despite being not too technical, it does, however, provokes the readers to think. Read more about the book here.
Machine Learning Yearning by Andrew Ng
Machine Learning Yearning
This is a free e-book from Andrew Ng, the founder of deeplearning.ai, that teaches you how to plan and structure your AI projects, specifically Machine Learning. The e-book provides guidelines to machine learning practitioners to help them make important decisions related to design and data collection, to name a few. The author himself stated that you will possess a deep understanding of how to set the technical direction for your machine learning project.
It covers broad topics such as conducting error analysis and building test sets. This book is not for you if you are interested in machine learning research as you would not find any complex mathematical equations. However, it is suitable for anyone who has a basic understanding of machine learning. Get your latest draft of Machine Learning Yearning here.
irl Decoded: A Scientist’s Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology by Rana el Kaliouby
Girl Decoded: A Scientist’s Quest to Reclaim Our Humanity by Bringing Emotional Intelligence to Technology
Rana el Kaliouby
This book tells a tale of how an Egyptian-American visionary and scientist goes on a mission to fulfil her calling — humanizing our technology and how we connect. Rana el Kaliouby, a Muslim woman within a field that is still predominantly run by males, tells us about her journey of personal transformation coming from a family that valued tradition above anything else. Her mother was also one of the first female computer programmers in the Middle East.
Upon receiving her PhD at Cambridge, she moved to America to begin her mission to humanize technology — to combat our fundamental loss of emotional intelligence online. That’s when she co-founded Affectiva, making them the pioneers in the new field of Emotion AI. Her memoir shares the many intersecting worlds she navigates through as a student, scientist, wife, mother, Egyptian-American, entrepreneur, and CEO and is sure to encourage and empower a generation of future scientists. You can read more about the book here.
Deep Learning: A Practitioner’s Approach by Josh Patterson & Adam Gibson
Deep Learning: A Practitioner’s Approach
Josh Patterson, Adam Gibson
Yet another book on the list that focuses on the technical aspects of Artificial Intelligence, Deep Learning was authored by Josh Patterson and Adam Gibson, focusing on specifically DL4J. The codes in the book are in Java yet it has a strong focus on the application of deep learning models, presenting them clearly and concisely — helping readers understand better.
So is this book for you? The authors felt that too many books on AI left out core topics that enterprise practitioners need and decided to begin with the materials that practitioners would need to brush up to better support their deep learning projects. Therefore, if you’re a Java engineer or a practising data scientist, this is the book for you. Check this book out and learn more about it here.
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place by Janelle Shane
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place
If you’re looking for a book to convince you that we’re not heading towards a ‘robopocalypse’ with AI, then this is it. Scientist Janelle Shane, the creator of the popular blog AI Weirdness, creates silly AIs — they learn how to name paint colours, create recipes and even flirt — to understand them better. Shane took the time to not only ensure that humour is well incorporated into her writing but also made sure the technical details are present with on-point examples — like how biological evolution and AI systems discover the method of locomotion on their own. She also uses the results from her experiments to demonstrate what could go right and what could go wrong.
In short, this book is a quick read, explaining how the algorithms used today are not as what they are portrayed in the movies. Perfect for anyone wondering about what the robots around us are thinking. Read more on her book here and you could check out her blog too.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
The Master Algorithm aims at giving readers a peek into the learning machines that drive Google, Amazon and even your mobile devices. Pedro Domingos puts together a plan for the future universal learner and discusses what it would mean for the society.
The book starts by discussing abstractly the master algorithm and then moves on to some of the philosophical issues associated with using such algorithms. It also places many techniques in historical perspectives, such as the rise, fall and the rise again of deep neural networks with support vector machines taking a lead as the popular technique today. Read more about the Master Algorithm right here.