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My Book On AI (Artificial Intelligence)

For Forbes.com, I’ve done quite a lot of writing on the topic of AI (Artificial Intelligence). It’s been a lot of fun — and I’ve learned a lot

So I also checked out the books on the topic. And I found two main categories. One, there were books that were highly technical (with code and equations). Then there were books that, well, were kind of dystopian, looking at the dark side of the technology.

But I really did not find something in the middle — say a book with a focus on those who are non-technical but want a fundamental understanding of AI.

This is why I wrote Artificial Intelligence Basics: A Non-Technical Introduction. Yes, the title says it all!

Here are also the chapters:

Chapter 1—AI Foundations: This is an overview of the rich history of AI, which goes back to the 1950s. You will learn about brilliant researchers and computer scientists like Alan Turing, John McCarthy, Marvin Minsky, and Geoffrey Hinton. There will also be coverage of key concepts like the Turing Test, which gauges if a machine has achieved true AI.

Chapter 2—Data: Data is the lifeblood of AI. It’s how algorithms can find patterns and correlations to provide insights. But there are landmines with data, such as quality and bias. This chapter provides a framework to work with data in an AI project.

Chapter 3—Machine Learning: This is a subset of AI and involves traditional statistical techniques like regressions. But in this chapter, we’ll also cover the advanced algorithms, such as k-Nearest Neighbor (k-NN) and the Naive Bayes Classifier. Besides this, there will be a look at how to put together a machine learning model.

Chapter 4—Deep Learning: This is another subset of AI and is clearly the one that has seen much of the innovation during the past decade. Deep learning is about using neural networks to find patterns that mimic the brain. In the chapter, we’ll take a look at the main algorithms like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). There will also be explanations of key concepts like backpropagation.

Chapter 5—Robotic Process Automation: This uses systems to automate repetitive processes, such as inputting data in a Customer Relationship Management (CRM) system. Robotic Process Automation (RPA) has seen tremendous growth during the past few years because of the high ROI (Return on Investment). The technology has also been an introductory way for companies to implement AI.

Chapter 6—Natural Language Processing (NLP): This form of AI, which involves understanding conversations, is the most ubiquitous as seen with Siri, Cortana, and Alexa. But NLP systems, such as chatbots, have also become critical in the corporate world. This chapter will show ways to use this technology effectively and how to avoid the tricky issues.

Chapter 7—Physical Robots: AI is starting to have a major impact on this industry. With deep learning, it is getting easier for robots to understand their environments. In this chapter, we’ll take a look at both consumer and industrial robots, such as with a myriad of use cases.

Chapter 8—Implementation of AI: We’ll take a step-by-step approach to putting together an AI project, from the initial concept to the deployment. This chapter will also cover the various tools like Python, TensorFlow, and PyTorch.

Chapter 9—The Future of AI: This chapter will cover some of the biggest trends in AI like autonomous driving, weaponization of AI, technological unemployment, drug discovery, and regulation.

Sounds like something for you? If so, you can get a copy on Amazon.com.