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Introduction

Key Aims/Objectives

• To investigate the origins and historical development of the technical terms “artificial intelligence” and “robot.”
• To understand the important points of contact and crucial differences between the way these technologies have been presented in science fiction and how they actually exist and function in reality.
• To see how and why words matter and that the means by which we say something about technology is not neutral but often shapes what that technology is and can become.
• To provide an overview of the book, its approach to the subject matter, and its content.

Introduction

The term Artificial Intelligence (AI) identifies both a scientific field of inquiry and a technology or particular type of technological system or artifact. For most of us, however, perceptions of and expectations for AI come not from the science or the technology, but from fiction – specifically, science fiction, where one-time useful systems and devices like the HAL 9000 (2001: A Space Odyssey), Colossus (Colossus: The Forbin Project), or Ultron (Avengers: Age of Ultron) turn rogue; enslave humanity in a computer-generated dream world (e.g., the Matrix trilogy); or rise-up against their human creators and stage a revolt (e.g., Terminator, Battlestar Galactica, Bladerunner 2049, Westworld). This first chapter gets things started by sorting science fact from fiction. It looks at the origins of artificial intelligence, the hype that has surrounded the technology and its consequences as portrayed in popular culture, and the reality of machine intelligence as it exists right now in the early twenty-first century. As such, this introductory chapter is designed to demystify AI for a nonspecialist audience, account for the social/cultural/political contexts of its development, and provide readers with a clear understanding of what this book concerning AI and communication is about, what will be addressed in the chapters that follow, and why all of this matters.

1.1 Artificial Intelligence

The term “artificial intelligence” first appeared and was used in the process of organizing a research workshop convened at Dartmouth College (Hanover, NH, USA) in the summer of 1956. The initial idea for the meeting originated with John McCarthy, who was, at the time, a young assistant professor of mathematics at Dartmouth. In early 1955, McCarthy began talking with the Rockefeller Foundation (a private philanthropic organization that funds scientific research) about his plans. He eventually teamed up with three other researchers: Marvin Minsky, a cognitive scientist who, along with McCarthy, is credited as the cofounder of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); Nathaniel Rochester, a computer engineer at IBM and lead designer on the IBM 701, the first general purpose, mass-produced computer; and Claude Shannon, the Bell Labs engineer who wrote The Mathematical Theory of Communication, which has supplied the discipline of communication with its basic “sender-message-receiver” process model.

In their proposal, titled “Dartmouth Summer Research Project on Artificial Intelligence,” McCarthy et al. (1955) offered the following explanation about the basic idea and objective of the effort:

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

Although rather short, this opening paragraph contains a number of important insights and ideas that can help us get a handle on what artificial intelligence is as both a scientific subject and technological object.1

1.1.1 Intelligence

The idea begins with and proceeds from a “conjecture” or an educated guess, namely, “that every aspect of learning or any other feature of intelligence” can be simulated or modeled by a computer. This immediately raises a more fundamental question: What is intelligence? The question is clearly intelligible – we know what is being asked about – but coming up with a definitive answer turns out to be something that is difficult, if not close to impossible. Here is how AI scientist Roger Schank describes this difficulty in a short introductory essay titled “What is AI Anyway?”:

AI people are fond of talking about intelligent machines, but when it comes down to it, there is little agreement on exactly what constitutes intelligence. And, it thus follows, there is very little agreement in AI about exactly what AI is and what it should be. We all agree that we would like to endow machines with an attribute that we really can’t define. Needless to say, AI suffers from this lack of definition of its scope. (1990: 4)

So here’s the problem: how can we pursue and produce intelligence in a technological artifact, if we cannot define what intelligence is to begin with?

“One way to attack this problem,” Schank (1990: 4) continues, “is to attempt to list some features that we would expect an intelligent entity to have.” So rather than answering the question “What is intelligence?” by offering a definition, one can proceed by listing those capabilities and operations that typically characterize what is called intelligence. This is precisely what McCarthy, Minsky, Rochester, and Shannon did in the Dartmouth proposal. Instead of defining intelligence as such, they issued a short list of activities or functions that are generally considered features or recognizable characteristics of intelligence: (1) use and understand language, (2) form abstractions and concepts, (3) solve problems, and (4) self-improvement.

Schank, for his part, provides a similar list, which includes a more detailed explanation of each individual item:

Communication: An intelligent entity can be communicated with. We can’t talk to rocks or tell trees what we want, no matter how hard we try.

Internal knowledge: We expect intelligent entities to have some knowledge about themselves. They should know when they need something; they should know what they think about something; and, they should know that they know it.

World knowledge: Intelligence also involves being aware of the outside world and being able to find and utilize the information that one has about the world outside. It also implies having a memory in which past experience is encoded and which can be used as a guide for processing new experience.

Goals and plans: Goal-driven behavior means knowing when one wants something and knowing a plan to get what one wants.

Creativity: Finally, every intelligent entity is assumed to have some degree of creativity. Creativity can be defined very weakly, including, for example, the ability to find a new route to one’s food source when the old one is blocked. But, of course, creativity can also mean finding a new way to look at something that changes one’s world in some significant way. (1990: 4–5)

The one thing we should note is that “communication” is situated at the top of the list. This is not an accident or random occurrence in the ordering of the five characteristics. Many of the other capabilities depend on or need some form of communication to be evidenced and identified as such. Take internal knowledge, for example. As Schank explains:

We cannot examine the insides of an intelligent entity in such a way as to establish what it actually knows. Our only choice is to ask and observe. If we get an answer that seems satisfying then we tend to believe that the entity we are examining has some degree of intelligence. (1990: 5)

In other words, our ability to recognize whether another entity does or does not possess internal knowledge is something that depends on the ability of that entity to tell us about that knowledge in some way that we can recognize. Since we do not have direct access to the “insides of an intelligent entity,” all we can do, as Schank (1990: 5) describes it, “is ask and observe.” The same can be said for many of the other features that appear on the list; their presence or absence would require some kind of external manifestation or mode of communication in order to be detected and identified as such. Consequently, communication – and not just verbal communication through the manipulation of language but also various forms of nonverbal behaviors – is fundamental to defining and detecting intelligence. If something can explain itself to us in language that we can understand, or exhibit interactive behaviors that are intentional and significant, it is called “intelligible.” If it cannot, it is often considered to be “unintelligible.”

1.1.2 Artificial

So much for the term “intelligence,” but what about “artificial”?...