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Artificial Intelligence Basics: A Non-Technical Introduction
Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success.
Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life.
Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had—and will continue to have—an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking.
What You Will Learn
Study the core principles for AI approaches such as machine learning, deep learning, and NLP (Natural Language Processing) Discover the best practices to successfully implement AI by examining case studies including Uber, Facebook, Waymo, UiPath, and Stitch Fix Understand how AI capabilities for robots can improve business Deploy chatbots and Robotic Processing Automation (RPA) to save costs and improve customer service Avoid costly gotchas Recognize ethical concerns and other risk factors of using artificial intelligence Examine the secular trends and how they may impact your business
Who This Book Is For
Readers without a technical background, such as managers, looking to understand AI to evaluate solutions.
200 pages, Kindle Edition
Published August 1, 2019
112 people are currently reading
283 people want to read
About the author
Tom Taulli
50 books17 followersI'm the author of a variety of books on technology and finance, with the latest being How to Create the Next Facebook and High-Profit IPO Strategies. I also write for Forbes.com and the IPOPlaybook.com.
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Displaying 1 - 18 of 18 reviews
July 13, 2021
Taulli wrote a non-technical introduction to the basis of artificial Intelligence. He shows where the field started, what it looks like currently and where it has the most potential for growth in the coming years.
Why I started this book: Navy Professional Reading title and newly arrived.
Why I finished it: Solid background to the field of artificial intelligence. Great book to get everyone on the same page and using the same vocabulary.
Why I started this book: Navy Professional Reading title and newly arrived.
Why I finished it: Solid background to the field of artificial intelligence. Great book to get everyone on the same page and using the same vocabulary.
February 21, 2025
A solid introduction to AI. While the book was published in 2019, as I write this in 2025, it already feels somewhat outdated. AI has advanced so much since then, and many recent developments aren’t covered. That said, it’s still a good starting point for anyone new to the topic.
June 15, 2021
Wreck a nice beach or recognize speech
Pretty good nontechnical overview book that’s pretty up to date. Some of the details I could nitpick, such as perpetuating the myth deep learning requires massive data* when really it could be as small as 10 datapoints to still benefit.
Was nice to see get some history of the past 10 years in an easy to read with examples format.
Also was nice they drew distinctions between machine learning, deep learning, automation, and ai.
* hardly anyone does deep learning from raw out of the box randomly weighted network. Nearly everyone takes something that is close and does transfer learning. Any image problem will start with an image model eg resnet, any audio problem an audio model (or take fft and again use an image model), any NLP problem an NLP model eg BERT (actually transfer learning came to NLP pretty late, thanks Jeremy Howard!), and so on for every application.
Pretty good nontechnical overview book that’s pretty up to date. Some of the details I could nitpick, such as perpetuating the myth deep learning requires massive data* when really it could be as small as 10 datapoints to still benefit.
Was nice to see get some history of the past 10 years in an easy to read with examples format.
Also was nice they drew distinctions between machine learning, deep learning, automation, and ai.
* hardly anyone does deep learning from raw out of the box randomly weighted network. Nearly everyone takes something that is close and does transfer learning. Any image problem will start with an image model eg resnet, any audio problem an audio model (or take fft and again use an image model), any NLP problem an NLP model eg BERT (actually transfer learning came to NLP pretty late, thanks Jeremy Howard!), and so on for every application.
November 23, 2021
While in some areas this text is a little more than basic in my opinion it is a good primer to understand some of the key ins and outs of artificial intelligence (AI). Depending on your level of knowledge of AI it might be beneficial to read the list of acronyms and glossary before reading the entire text. Worth the time investment to read.
January 15, 2020
Fulfills its claim of 'basics: a non-technical introduction' quite well.
As Deane Barker writes, this book "[helps] prove the point that AI is really category, not a single technology." Being familiar with one of the theory or its application should make this 'basics' book an easier read. Neither seems to be prerequisite because the author jumps between the two -- as Barker's review describes -- in a way I believe would help the reader fill out their understanding.
As Deane Barker writes, this book "[helps] prove the point that AI is really category, not a single technology." Being familiar with one of the theory or its application should make this 'basics' book an easier read. Neither seems to be prerequisite because the author jumps between the two -- as Barker's review describes -- in a way I believe would help the reader fill out their understanding.
February 23, 2021
Frankly, too basic unless the reader has no previous understanding.
December 30, 2022
Takeaways from this book as an AI beginner
Machine learning is a subset of AI and deep learning is a subset of machine learning.
The data used to train the models can be structured label formatted or unstructured like image, video, audio.
Machine learning: Reinforcment learning in ML is a way of rewarding accurat learning and punishing those that are not.
K-Nearest neighbor is a supervised learning(values that are close to each other act similarly)
K-mean is unsupervised learning ( put similar unlabeled data into different groups)
Deep learning: detect patterns that humans are unable to detect.
An activation function like the sigmoid compress the data between 0 and 1.
Backpropegation a technique to adjust the weight of a NN
Recurent NN is a function that process input but also prior inputs.
ConvulutedNN can be used in image recognition
Natural language processing is the use of AI to allow computers to understand ppl
Machine learning is a subset of AI and deep learning is a subset of machine learning.
The data used to train the models can be structured label formatted or unstructured like image, video, audio.
Machine learning: Reinforcment learning in ML is a way of rewarding accurat learning and punishing those that are not.
K-Nearest neighbor is a supervised learning(values that are close to each other act similarly)
K-mean is unsupervised learning ( put similar unlabeled data into different groups)
Deep learning: detect patterns that humans are unable to detect.
An activation function like the sigmoid compress the data between 0 and 1.
Backpropegation a technique to adjust the weight of a NN
Recurent NN is a function that process input but also prior inputs.
ConvulutedNN can be used in image recognition
Natural language processing is the use of AI to allow computers to understand ppl
July 23, 2025
إذا كنت تبحث عن مقدمة سلسة ومبسّطة لفهم الذكاء الاصطناعي دون الحاجة إلى خلفية تقنية، فهذا الكتاب هو الخيار المناسب لك. بأسلوب واضح ومباشر، يأخذك المؤلف في رحلة تمهيدية عبر مفاهيم الذكاء الاصطناعي، بدءًا من تعريفه وأنواعه، مرورًا بتاريخ تطوره، وصولًا إلى تطبيقاته العملية في مختلف المجالات. سيعجبك تركيزه على الجوانب الأخلاقية والاجتماعية، والتي غالبًا ما تُهمل في الكتب ذات الطابع التقني. كما أن الفصل الأخير الذي يعرض خطوات بناء مشروع ذكاء اصطناعي، كان مفيدًا وعمليًا لمن يرغب في تطبيق المعرفة على أرض الواقع. شعرتُ أن هذا الكتاب يخاطبك كقارئ عادي، لكنه لا يستخف بعقلك، ويمنحك الأدوات لفهم ما يدور حولك في هذا العصر التقني المتسارع. أنصحك به إن كنت تريد أن تبدأ من نقطة الصفر، وتكوّن رؤية شاملة دون التورط في التفاصيل المعقدة
تابعني على شبكات التواصل الاجتماعي
سليمان العوشن
http://about.me/aloshan
تابعني على شبكات التواصل الاجتماعي
سليمان العوشن
http://about.me/aloshan
July 21, 2025
This book was poorly edited. There were numerous spelling, grammar and syntax errors. It became very distracting. Another problem I had with the book was the structure. While reading, it was apparent many of the sections of the book were written independently then organized into chapters based on subject; almost as if the author took old blogs and made them into a book. This was jarring because the author would mention an article or subject previously discussed, but it was presented as if it were the first time.
December 30, 2023
What about Al and K12 classroom?
The book "Artificial Intelligence Basics A Non-Technocal Introduction" offer readers an overview of artificial intelligence from past (Henry Ford) to present (Apple, Google, McDonald's, Microsoft). What one can appreciate is the breadth of information provided. However, as a k12 classroom teacher, I wanted to know more about artificial intelligence impact on children and learning.
The book "Artificial Intelligence Basics A Non-Technocal Introduction" offer readers an overview of artificial intelligence from past (Henry Ford) to present (Apple, Google, McDonald's, Microsoft). What one can appreciate is the breadth of information provided. However, as a k12 classroom teacher, I wanted to know more about artificial intelligence impact on children and learning.
December 27, 2021
Great intro to AI
May 18, 2022
Good introduction and overview of AI, with summary, pros and cons for each chapter.
February 1, 2024
Very basic, not too technical vs not too layman.
February 2, 2024
An approachable primer written so carelessly, and apparently without any kind of editing, that it became a distraction. Errors on *every* page!
June 9, 2024
Becoming a bit dated. 4 stars otherwise.
August 16, 2024
Good for the basic knowledge of AI, and things are explained in a simple and normal terms
Want to read
March 19, 2025Buchempfehlung von Dr. Thomas Stoffmehl, Vorstandssprecher von Vorwerk
September 27, 2022
Yes, it has "Basics" in the title, but I was expecting something more from this textbook.
The author tries to put too many concepts together, providing only general examples, experts opinions or real applications.
Unless, you've never heard of Machine Learning, Deep Learning or AI this book is not for you.
Notes:
Turing Test: the evaluator, a human, ask open ended questions of the other two, one human and one computer, with the goal of determining which one is the human. It indicates that the machine can process large amounts of information, interpret speech, and communicate with humans.
Stronger AI, when a machine truly understands what is happening versus weak AI, a machine is pattern matching and usually focus on narrow tasks
Big Data: volume, the scale of the data; variety, they diversity of the data; velocity, the speed at which data is being created; veracity, value, variability, visualisation.
Data process
business understanding,
data understanding (in-house or open source data),
data preparation (duplication, outliers, consistency, validation rules, beaning, one hot encoding, conversion tables),
modelling, evaluation, deployment
Normal distribution: it's represents this sum of probabilities for a variable vehicle is common in the natural world (the histogram is a bell curve). They estimate that 68% of the data items will fall within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations.
Correlation: correlation or dependence is any statistical relationship where the cause or not between two random variables or bivariate data.
Deep learning: an artificial neural networks in which multiple layers of processing are used to extract progressively high-level features from data
Robotic process automation (RPA): is a software technology that makes it easy to build robots that emulate humans actions interacting with digital systems and software
Natural language processing (NLP): cleaning and pre processing the text (tokenisation text must be parsed and segmented into various parts), language understanding and generation.
The author tries to put too many concepts together, providing only general examples, experts opinions or real applications.
Unless, you've never heard of Machine Learning, Deep Learning or AI this book is not for you.
Notes:
Turing Test: the evaluator, a human, ask open ended questions of the other two, one human and one computer, with the goal of determining which one is the human. It indicates that the machine can process large amounts of information, interpret speech, and communicate with humans.
Stronger AI, when a machine truly understands what is happening versus weak AI, a machine is pattern matching and usually focus on narrow tasks
Big Data: volume, the scale of the data; variety, they diversity of the data; velocity, the speed at which data is being created; veracity, value, variability, visualisation.
Data process
business understanding,
data understanding (in-house or open source data),
data preparation (duplication, outliers, consistency, validation rules, beaning, one hot encoding, conversion tables),
modelling, evaluation, deployment
Normal distribution: it's represents this sum of probabilities for a variable vehicle is common in the natural world (the histogram is a bell curve). They estimate that 68% of the data items will fall within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations.
Correlation: correlation or dependence is any statistical relationship where the cause or not between two random variables or bivariate data.
Deep learning: an artificial neural networks in which multiple layers of processing are used to extract progressively high-level features from data
Robotic process automation (RPA): is a software technology that makes it easy to build robots that emulate humans actions interacting with digital systems and software
Natural language processing (NLP): cleaning and pre processing the text (tokenisation text must be parsed and segmented into various parts), language understanding and generation.
Displaying 1 - 18 of 18 reviews