Artificial Intelligence For Dummies
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You can hardly avoid hearing about artificial intelligence (AI) today. You see AI in the movies, in the news, in books, and online. It's been in the news a lot lately, with all of the frenzy surrounding ChatGPT (see more about that below).

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AI is part of robots, self-driving (SD) cars, drones, medical systems, online shopping sites, and all sorts of other technologies that affect your daily life in so many ways. Some people have come to trust AIs so much, that they fall asleep while their SD cars take them to their destination — illegally, of course (see “Tesla driver found asleep at wheel of self-driving car doing 150km/h” at The

Many pundits are burying you in information (and disinformation) about AI, too. Some see AI as cute and fuzzy; others see it as a potential mass murderer of the human race. The problem with being so loaded down with information in so many ways is that you struggle to separate what’s real from what is simply the product of an overactive imagination.

Just how far can you trust your AI, anyway? Much of the hype about AI originates from the excessive and unrealistic expectations of scientists, entrepreneurs, and businesspersons.

AI in our everyday lives

Using various media as a starting point, you might notice that most of the useful technologies are almost boring. Certainly, no one gushes over them. AI is like that: so ubiquitous as to be humdrum.

You’re using AI in some way today; in fact, you probably rely on AI in many different ways — you just don’t notice it because it’s so mundane. A smart thermostat for your home may not sound very exciting, but it’s an incredibly practical use for a technology that has some people running for the hills in terror.

There are also really cool uses for AI. For example, you may not know there is a medical monitoring device that can actually predict when you might have a heart problem, but such a device exists.

AI powers drones, drives cars, and makes all sorts of robots possible. You see AI used today in all sorts of space applications, and AI figures prominently in all the space adventures humans will have tomorrow.

The ChatGPT controversy

The latest media storm around AI came in early January 2023, when OpenAI launched a free preview of its ChatGPT chatbot. A chatbot is a computer program designed to simulate human conversation. ChatGPT (GPT stands for generative pretrained transformer) is a particularly powerful chatbot able to produce natural, human-like writing through its use of 570GB of data from the Internet.

ChatGPT can answer questions and write articles, poems, emails, and research papers; it can also write programming code, translate languages, and perform other tasks related to language.

ChatGPT's possible real-world uses include:

  • Customer service
  • Ecommerce
  • Research
  • Education and training
  • Computer code writing and debugging
  • Scheduling and booking
  • Entertainment
  • Health care information and assistance
However, while many people are excited about the possibilities for ChatGPT and other similar technologies being developed, there are plenty of concerns about how it can be used in bad ways, too — for example, to cheat in school by having it write essays and research papers.

It’s difficult to discern whether a piece of writing has been generated by ChatGPT or a human. In addition, the technology is far from perfect; the text it produces is often inaccurate and biased, and therefore, can spread false and even harmful information.

AI can, and is, serving us well in many ways, but it’s important to understand its limitations. AI will never be able to engage in certain essential activities and tasks, and won’t be able to do other ones until far into the future. For example, while it can produce a piece of music with the data you’ve entered and in the style of a particular musician, say Beethoven, it cannot actually create anything. AI doesn’t have an imagination or original ideas.

The history of AI, starting with Dartmouth

The earliest computers were just that: computing devices. They mimicked the human ability to manipulate symbols in order to perform basic math tasks, such as addition. Logical reasoning later added the capability to perform mathematical reasoning through comparisons (such as determining whether one value is greater than another value).

However, humans still needed to define the algorithm used to perform the computation, provide the required data in the right format, and then interpret the result.

During the summer of 1956, various scientists attended a workshop held on the Dartmouth College campus in Hanover, New Hampshire, to do something more. They predicted that machines that could reason as effectively as humans would require, at most, a generation to come about. They were wrong. Only now have we realized machines that can perform mathematical and logical reasoning as effectively as a human (which means that computers must master at least six more intelligences before reaching anything even close to human intelligence).

The stated problem with the Dartmouth College and other endeavors of the time relates to hardware — the processing capability to perform calculations quickly enough to create a simulation.

However, that’s not really the whole problem. Yes, hardware does figure in to the picture, but you can’t simulate processes that you don’t understand. Even so, the reason that AI is somewhat effective today is that the hardware has finally become powerful enough to support the required number of calculations.

The biggest problem with these early attempts (and still a considerable problem today) is that we don’t understand how humans reason well enough to create a simulation of any sort — assuming that a direction simulation is even possible.

Consider the issues surrounding the accomplishment of manned flight by the Wright brothers. They succeeded not by simulating birds, but rather by understanding the processes that birds use, thereby creating the field of aerodynamics.

Consequently, when someone says that the next big AI innovation is right around the corner and yet no concrete dissertation exists of the processes involved, the innovation is anything but right around the corner.

Continuing with expert systems

Expert systems first appeared in the 1970s and again in the 1980s as an attempt to reduce the computational requirements posed by AI using the knowledge of experts. A number of expert system representations appeared, including:
  • Rule based: These use "if … then" statements to base decisions on rules of thumb.
  • Frame based: These use databases organized into related hierarchies of generic information called frames.
  • Logic based: These rely on set theory to establish relationships).
The advent of expert systems is important because they present the first truly useful and successful implementations of AI.

You still see expert systems in use today, although they aren’t called that any longer. For example, the spelling and grammar checkers in your application are kinds of expert systems. The grammar checker, especially, is strongly rule based. It pays to look around to see other places where expert systems may still see practical use in everyday applications.

A problem with expert systems is that they can be hard to create and maintain. Early users had to learn specialized programming languages, such as List Processing (LisP) or Prolog.

Some vendors saw an opportunity to put expert systems in the hands of less experienced or novice programmers. However, the products they used generally provided extremely limited functionality in using small knowledge bases.

In the 1990s, the phrase expert system began to disappear. The idea that expert systems were a failure did appear, but the reality is that expert systems were simply so successful that they became ingrained in the applications that they were designed to support.

Using the example of a word processor, at one time you needed to buy a separate grammar checking application, such as RightWriter. However, word processors now have grammar checkers built in because they proved so useful (if not always accurate).

Overcoming the AI winters

The term AI winter refers to a period of reduced funding in the development of AI. In general, AI has followed a path on which proponents overstate what is possible, inducing people with no technology knowledge at all, but lots of money, to make investments.

A period of criticism then follows when AI fails to meet expectations, and finally, the reduction in funding occurs. A number of these cycles have occurred over the years — all of them devastating to true progress.

AI is currently in a new hype phase because of machine learning, a technology that helps computers learn from data. Having a computer learn from data means not depending on a human programmer to set operations (tasks), but rather deriving them directly from examples that show how the computer should behave. '

Machine learning is like educating a baby by showing it how to behave through example. This technology has pitfalls because the computer can learn how to do things incorrectly through careless teaching.

Five tribes of scientists are working on machine learning algorithms, each one from a different point of view. These five tribes of machine learning and their origins are:

  • Symbolists: Logic and philosophy
  • Connectionists: Neuroscience
  • Evolutionaries: Biology
  • Bayesians: Statistics
  • Analogizers: Psychology
At this time, the most successful solution is deep learning, which is a technology that strives to imitate the human brain. Deep learning is possible because of the availability of powerful computers, smarter algorithms, large datasets produced by the digitalization of our society, and huge investments from businesses such as Google, Facebook, Amazon, and others that take advantage of this AI renaissance for their own businesses.

People are saying that the AI winter is over because of deep learning, and that’s true for now. However, when you look around at the ways in which people are viewing AI, you can easily figure out that another criticism phase will eventually occur unless proponents tone the rhetoric down.

About This Article

This article is from the book:

About the book authors:

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.

Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.

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