5 Books to for Non-Technical Users to Better Understand AI | by Ivo Bernardo | Sep, 2022

A selection of resources that explain complex concepts regardless of readers’ technical background

[Disclaimer: This post contains affiliate links to Book Depository]

Artificial intelligence and data science are getting more relevant for society as time goes by. Long gone are the days where algorithms were applied for marketing or sales purposes alone; today, artificial intelligence is transforming the way we interact, commute, or even drive.

For non-technical people, it may be a bit overwhelming to understand most AI techniques. Normally, when someone speaks of AI, they tend to speak with a lot of technical terms or confusing jargon that makes it hard to understand the impact of algorithms in today’s life. Luckily, with the proliferation of books on AI, there are some resources out there that target non technical users and that aim to explain these concepts simply with a low technical layer.

In this post, we’ll see some books that are relevant for non-technical users to understand the impact of AI in today’s world and where they can learn where the field is heading to.

Let’s start!

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking — Foster Provost & Tom Fawcett

A well-written introduction into artificial intelligence and data science techniques, Provost & Fawcett’s book is a great way to get introduced into the industry. The book provides some answers to common questions that non-technical users may have when getting in contact with data science & AI:

• What does training a “model” mean?
• How does AI relate to data engineering and visualization?
• What are the most common use cases we can apply predictive modelling to?
• What is text mining?

This book is a very good introduction that contains just the right level of technical detail. If you are looking for a lightweight approach to data science, this one will be a great choice!

Find it on Book Depository.

Artificial Intelligence Basics: A Non-Technical Introduction — Tom Taulli

A good introductory book that details the different subbranches of artificial intelligence and data science, such as natural language processing, computer vision, or robot process automation.

Through clever diagrams and easy to understand language, this book is a great quick introduction to the field, helping to understand the current state-of-art of artificial intelligence trends.

If you already have some contact and knowledge about AI and general data science, this book may be a bit basic for you. Nevertheless, although short, it contains interesting ideas, particularly if you are just starting to learn terms like big data, neural networks, or deep learning.

You can find it on Book Depository, here.

Weapons Of Math Destruction: How Big Data Increases Inequality and Threatens Democracy — Cathy O’Neill

The proliferation of the deployment of algorithms in several areas of society brought big concerns when it comes to ethics and morality. AI models can be extremely harmful to a fair and just society as they may reinforce some bias that is already present in the data fed to the models.

Cathy O’Neill’s book is a very concise read on the dangers of blindly applying AI models to every aspect of our society. If you are just starting to understand the impact of AI in our daily lives, this book will give you a thoughtful perspective of how machine learning models should be deployed and why you need to be careful with certain AI “magical” solutions.

As with most technological breakthroughs, artificial intelligence has the power to be applied to a majority of use cases — some of them will probably make society extremely productive, while others may have extremely harmful caveats. This book is probably one of the best resources that tackles that issue with detail and accuracy.

You can find it on Book Depository.

Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies — Steve Finlay

Another non-technical guide for Artificial Intelligence and Machine Learning, Steve Finlay’s book is a good introduction with little technical jargon and approaches artificial intelligence in a non-technical way.

Although not as deep and less business-oriented when compared with Provost & Fawcett’s book, this one will complement it well with a few thoughtful chapters that are oriented towards important questions that you may not see in other resources, such as:

• What do the scores generated by a predictive model represent?
• What are decision trees?
• When can I buy a self-driving car?

This book will help give you a good non-technical overview on the last decade of machine learning, using a thoughtful and semi-chronological approach.

You can find it on Book Depository.

How to Lie with Statistics — Darrell Huff

This is the oldest book on this list and was edited way before mankind started to conceptualize the field of modern “artificial intelligence”! Most AI algorithms rely on statistics —at least, the main objective of data scientists is to model real life phenomena in a way that can generalize to the other scenarios, and statistics are our current best guess at doing so.

An outside-the-box recommendation, Darrell Huff’s book is a summary on the most common techniques that practitioners can use when lying with statistics. While not exactly related to artificial intelligence, it is a great resource to help you understand why people can fool you with algorithms, summary statistics or plots.

This is a great read to understand how even the greatest AI models can be extremely biased and tweaked to make it seem that they are highly accurate. Although mostly focused on plots and summary statistics, the principles on the book are adaptable to data science or machine learning projects as the principle of fooling others using false stats is recurrent on some projects.

Find it on Book Depository.

Thank you for taking the time to read this post!

If you are a non-technical reader (such as a product or project manager, a marketeer, or just a curious person!), I hope these resources help you understand AI a bit better. Particularly when it comes to non-ethical deployment of machine learning models, it’s extremely relevant that non-technical users are able to understand how AI models work and how they build relationships between input data and prediction.

When society is at a stage where non-technical people are able to understand the implications behind training models on inaccurate or biased data, we will truly be able to benefit on more accurate, fair, and reliant AI systems — I really believe that these resources do a great job at contributing towards that stage.

Have other recommendations to add? Write them in the comments!

A selection of resources that explain complex concepts regardless of readers’ technical background

[Disclaimer: This post contains affiliate links to Book Depository]

Artificial intelligence and data science are getting more relevant for society as time goes by. Long gone are the days where algorithms were applied for marketing or sales purposes alone; today, artificial intelligence is transforming the way we interact, commute, or even drive.

For non-technical people, it may be a bit overwhelming to understand most AI techniques. Normally, when someone speaks of AI, they tend to speak with a lot of technical terms or confusing jargon that makes it hard to understand the impact of algorithms in today’s life. Luckily, with the proliferation of books on AI, there are some resources out there that target non technical users and that aim to explain these concepts simply with a low technical layer.

In this post, we’ll see some books that are relevant for non-technical users to understand the impact of AI in today’s world and where they can learn where the field is heading to.

Let’s start!

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking — Foster Provost & Tom Fawcett

A well-written introduction into artificial intelligence and data science techniques, Provost & Fawcett’s book is a great way to get introduced into the industry. The book provides some answers to common questions that non-technical users may have when getting in contact with data science & AI:

• What does training a “model” mean?
• How does AI relate to data engineering and visualization?
• What are the most common use cases we can apply predictive modelling to?
• What is text mining?

This book is a very good introduction that contains just the right level of technical detail. If you are looking for a lightweight approach to data science, this one will be a great choice!

Find it on Book Depository.

Artificial Intelligence Basics: A Non-Technical Introduction — Tom Taulli

A good introductory book that details the different subbranches of artificial intelligence and data science, such as natural language processing, computer vision, or robot process automation.

Through clever diagrams and easy to understand language, this book is a great quick introduction to the field, helping to understand the current state-of-art of artificial intelligence trends.

If you already have some contact and knowledge about AI and general data science, this book may be a bit basic for you. Nevertheless, although short, it contains interesting ideas, particularly if you are just starting to learn terms like big data, neural networks, or deep learning.

You can find it on Book Depository, here.

Weapons Of Math Destruction: How Big Data Increases Inequality and Threatens Democracy — Cathy O’Neill

The proliferation of the deployment of algorithms in several areas of society brought big concerns when it comes to ethics and morality. AI models can be extremely harmful to a fair and just society as they may reinforce some bias that is already present in the data fed to the models.

Cathy O’Neill’s book is a very concise read on the dangers of blindly applying AI models to every aspect of our society. If you are just starting to understand the impact of AI in our daily lives, this book will give you a thoughtful perspective of how machine learning models should be deployed and why you need to be careful with certain AI “magical” solutions.

As with most technological breakthroughs, artificial intelligence has the power to be applied to a majority of use cases — some of them will probably make society extremely productive, while others may have extremely harmful caveats. This book is probably one of the best resources that tackles that issue with detail and accuracy.

You can find it on Book Depository.

Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies — Steve Finlay

Another non-technical guide for Artificial Intelligence and Machine Learning, Steve Finlay’s book is a good introduction with little technical jargon and approaches artificial intelligence in a non-technical way.

Although not as deep and less business-oriented when compared with Provost & Fawcett’s book, this one will complement it well with a few thoughtful chapters that are oriented towards important questions that you may not see in other resources, such as:

• What do the scores generated by a predictive model represent?
• What are decision trees?
• When can I buy a self-driving car?

This book will help give you a good non-technical overview on the last decade of machine learning, using a thoughtful and semi-chronological approach.

You can find it on Book Depository.

How to Lie with Statistics — Darrell Huff

This is the oldest book on this list and was edited way before mankind started to conceptualize the field of modern “artificial intelligence”! Most AI algorithms rely on statistics —at least, the main objective of data scientists is to model real life phenomena in a way that can generalize to the other scenarios, and statistics are our current best guess at doing so.

An outside-the-box recommendation, Darrell Huff’s book is a summary on the most common techniques that practitioners can use when lying with statistics. While not exactly related to artificial intelligence, it is a great resource to help you understand why people can fool you with algorithms, summary statistics or plots.

This is a great read to understand how even the greatest AI models can be extremely biased and tweaked to make it seem that they are highly accurate. Although mostly focused on plots and summary statistics, the principles on the book are adaptable to data science or machine learning projects as the principle of fooling others using false stats is recurrent on some projects.

Find it on Book Depository.

Thank you for taking the time to read this post!

If you are a non-technical reader (such as a product or project manager, a marketeer, or just a curious person!), I hope these resources help you understand AI a bit better. Particularly when it comes to non-ethical deployment of machine learning models, it’s extremely relevant that non-technical users are able to understand how AI models work and how they build relationships between input data and prediction.

When society is at a stage where non-technical people are able to understand the implications behind training models on inaccurate or biased data, we will truly be able to benefit on more accurate, fair, and reliant AI systems — I really believe that these resources do a great job at contributing towards that stage.

Have other recommendations to add? Write them in the comments!