Artificial intelligence (AI):  is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring common sense and everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

‘Strong’ AI aims is to produce a machine whose overall intellectual ability is indistinguishable from that of a human being. To date, progress has been meagre. Some critics doubt whether research will produce even a system with the overall intellectual ability of an ant in the foreseeable future. Indeed, some researchers working in AI’s other two branches view strong AI as not worth pursuing.

‘Applied’ AI, also known as advanced information processing, aims to produce commercially viable “smart” systems—for example, “expert” medical diagnosis systems and stock-trading systems. Applied AI has enjoyed considerable success.

In cognitive simulation, computers are used to test theories about how the human mind works—for example, theories about how people recognize faces or recall memories. Cognitive simulation is already a powerful tool in both neuroscience and cognitive psychology.


Artificial life, also called A-life or alife:  is a computer simulation of life, often used to study essential properties of living systems (such as evolution and adaptive behaviour). Artificial life became a recognized discipline in the 1980s, in part through the impetus of American computer scientist Christopher Langton, who named the field. He characterized artificial life as “locating life-as-we-know-it within the larger picture of life-as-it-could-be,” a concept that brought together people interested in computer models of adaptive and self-organizing systems, not just in biology but also in economics, social science, and physical chemistry.

Life on Earth is incredibly complex. Millions of species, constructed from a vast array of different chemicals, interact in innumerable ways. It is difficult to extract any general principles of biological design from among life’s complex details. A-life seeks to illuminate this problem by simulating lifelike processes within computers. By creating highly simplified artificial “aliens” or ‘automatons’ and comparing their development and behaviour to real biology, it is often possible to discover something of life’s essential character.

Early efforts at artificial life centred on creating lifelike automatons, devices that appear to operate on their own after being set in motion. Modern A-life researchers inquire into the essential nature of life: What is it? What is necessary for it to exist and propagate? How can complex living organisms arise from the interaction of genes and environment? What are the mechanisms by which organisms respond intelligently and adapt to changes in their environment, both during their lifetimes and through the generations? The subject most frequently tackled today is evolution: What are the principles by which life develops itself into ever-increasing complexity, variety, and competence? This is of interest not only to biologists but also to engineers, who wish to emulate evolution’s remarkable ability to create complex yet robust structures that require no ongoing human intervention.

Another common A-life research interest is collective behaviour. Many communal “animals,” including ants and even the individual cells that make up an organism, appear to behave in highly intelligent ways. A-life researchers, in conjunction with biologists, have been able to show that such behaviour can and does arise “from the bottom up” by combining remarkably straightforward rules. An ant nest emerges from simple processes without requiring an overall blueprint and without any individual ant needing to understand what part it plays in the whole enterprise.

The process of abstracting biology into the more general topic of complex adaptive systems meshes with a number of other developments in science and technology, such as complexityand chaos theory, as well as the networking theories inspired by the Internet. Collectively, these developing fields may form part of a general paradigm shift in both scientific and popular thought away from the linear, comparatively predictable world of, say, planetary orbits, and the top-down hierarchies of traditional forms of organization (businesses, governments, or artifacts) toward a more bottom-up, self-organizing, and emergent way of looking at the world.

A-life also raises ontological questions. One of its fundamental tenets is that life is a process, a spatiotemporal pattern, not the substrate on which that process takes place. The human body, for instance, maintains its appearance and properties even though the material of which it is made is constantly being replaced. This “process view” explains how life can be emulated in a computer, since the same processes can occur in other virtual substrates made from abstract symbols and rules for their interaction. So far, most experiments have involved simplified imitations of life, and many researchers have been content to look no further. But among the philosophical issues raised by A-life research is the question of whether such processes, when they occur in the silicon memory of a computer instead of the carbon chemistry of an animal, might actually be alive.




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