Chapter 1

Delving into What AI Means

IN THIS CHAPTER

Defining AI and its history

Using AI for practical tasks

Seeing through AI hype

Connecting AI with computer technology

Common apps, such as Google Assistant Alexa, and Siri, have all of us who are online every day, using artificial intelligence (AI) without even thinking about it. Productivity and creative apps such as ChatGPT, Synesthesia, and Gemini help us focus on the content rather than on how to get there. The media floods our entire social environment with so much information and disinformation that many people see AI as a kind of magic (which it most certainly isn’t). So the best way to start this book is to define what AI is, what it isn’t, and how it connects to computers today.

Of course, the basis for what you expect from AI is a combination of how you define AI, the technology you have for implementing AI, and the goals you have for AI. Consequently, everyone sees AI differently. This book takes a middle-of-the-road approach by viewing AI from as many different perspectives as possible. We don’t buy into the hype offered by proponents, nor do we indulge in the negativity espoused by detractors. Instead, we strive to give you the best possible view of AI as a technology. As a result, you may find that you have expectations somewhat different from those you encounter in this book, which is fine, but it’s essential to consider what the technology can actually do for you — rather than expect something it can’t.

Defining the Term AI

Before you can use a term in any meaningful and useful way, you must have a definition for it. After all, if nobody agrees on a meaning, the term has none; it’s just a collection of characters. Defining the idiom (a term whose meaning isn’t clear from the meanings of its constituent elements) is especially important with technical terms that have received more than a little press coverage at various times and in various ways.

Saying that AI is an artificial intelligence doesn’t tell you anything meaningful, which is why people have so many discussions and disagreements over this term. Yes, you can argue that what occurs is artificial, not having come from a natural source. However, the intelligence part is, at best, ambiguous. Even if you don’t necessarily agree with the definition of AI as it appears in the sections that follow, this book uses AI according to that definition, and knowing it will help you follow the text more easily.

Discerning intelligence

People define intelligence in many different ways. However, you can say that intelligence involves certain mental activities composed of the following activities:

• Learning: Having the ability to obtain and process new information
• Reasoning: Being able to manipulate information in various ways
• Understanding: Considering the result of information manipulation
• Grasping truths: Determining the validity of the manipulated information
• Seeing relationships: Divining how validated data interacts with other data
• Considering meanings: Applying truths to particular situations in a manner consistent with their relationship
• Separating fact from belief: Determining whether the data is adequately supported by provable sources that can be demonstrated to be consistently valid

The list could easily grow quite long, but even this list is relatively prone to interpretation by anyone who accepts it as viable. As you can see from the list, however, intelligence often follows a process that a computer system can mimic as part of a simulation:

1. Set a goal (the information to process and the desired output) based on needs or wants.
2. Assess the value of any known information in support of the goal.
3. Gather additional information that could support the goal. The emphasis here is on information that could support the goal rather than on information you know will support the goal.
4. Manipulate the data such that it achieves a form consistent with existing information.
5. Define the relationships and truth values between existing and new information.
6. Determine whether the goal is achieved.
7. Modify the goal in light of the new data and its effect on the probability of success.
8. Repeat Steps 2 through 7 as needed until the goal is achieved (found true) or the possibilities for achieving it are exhausted (found false).

Even though you can create algorithms and provide access to data in support of this process within a computer, a computer’s capability to achieve intelligence is severely limited. For example, a computer is incapable of understanding anything because it relies on machine processes to manipulate data using pure math in a strictly mechanical fashion. Likewise, computers can’t easily separate truth from mistruth (as described in Chapter 2). In fact, no computer can fully implement any of the mental activities described in the earlier list that describes intelligence.

As part of deciding what intelligence actually involves, categorizing intelligence is also helpful. Humans don’t use just one type of intelligence; rather, they rely on multiple intelligences to perform tasks. Howard Gardner a Harvard psychologist has defined a number of these types of intelligence (for details, see the article “Multiple Intelligences” from Project Zero at Harvard University https://pz.harvard.edu/resources/the-theory-of-multiple-intelligences) and knowing them helps you relate them to the kinds of tasks a computer can simulate as intelligence. (See Table 1-1 for a modified version of these intelligences with additional description.)

TABLE 1-1 The Kinds of Human Intelligence and How AIs Simulate Them