ARTIFICIAL INTELLIGENCE

Artificial Intelligence: Past, Present and Future



Artificial Intelligence: Past, Present and Future

Introduction

Artificial Intelligence (AI) tries to enable computers to do the things that minds can do. These things include seeing pathways, picking things up, learning categories from experience, and using emotions to schedule one's actions—which many animals can do, too. Thus, human intelligence is not the sole focus of AI. Even terrestrial psychology is not the sole focus, because some people use AI to explore the range of all possible minds. There are four major AI methodologies: symbolic AI, connectionism, situated robotics, and evolutionary programming. AI artifacts are correspondingly varied. They include both programs (including neural networks) and robots, each of which may be either designed in detail or largely evolved. The field is closely related to artificial life (A-Life), which aims to throw light on biology much as some AI aims to throw light on psychology.

AI researchers are inspired by two different intellectual motivations, and while some people have both, most favor one over the other. On the one hand, many AI researchers seek solutions to technological problems, not caring whether these resemble human (or animal) psychology. They often make use of ideas about how people do things. Programs designed to aid/replace human experts, for example, have been hugely influenced by knowledge engineering, in which programmers try to discover what, and how, human experts are thinking when they do the tasks being modeled. But if these technological AI workers can find a nonhuman method, or even a mere trick (a kludge) to increase the power of their program, they will gladly use it.

Technological AI has been hugely successful. It has entered administrative, financial, medical, and manufacturing practice at countless different points. It is largely invisible to the ordinary person, lying behind some deceptively simple human-computer interface or being hidden away inside a car or refrigerator (Lebiere, 1998). Many procedures taken for granted within current computer science were originated within AI (pattern-recognition and image-processing, for example). On the other hand, AI researchers may have a scientific aim. They may want their programs or robots to help people understand how human (or animal) minds work. They may even ask how intelligence in general is possible, exploring the space of possible minds. The scientific approach—psychological AI—is the more relevant for philosophers. It is also central to cognitive science, and to computationalism. Considered as a whole, psychological AI has been less obviously successful than technological AI. This is partly because the tasks it tries to achieve are often more difficult. In addition, it is less clear—for philosophical as well as empirical reasons—what should be counted as success. (Rosembloom, 1987)

Discussion and Analysis

The study of artificial intelligence, referred to as AI, has accelerated in recent years as advancements in computer technology have made it possible to create more and more sophisticated machines and software programs. The field of AI is dominated by computer scientists, but it has important ramifications for psychologists as well because in creating machines that replicate human thought, much is learned ...