Cognitive Niche in AI
Artificial Intelligence (AI) has come of age. Two thousand and twenty marked the sixty-fourth anniversary for the term Artificial Intelligence to be accepted as the official label for a new discipline that seemed to hold great promise in the pursuit of understanding the human mind. AI, as a path-breaking concept that came to be known in public and academic discourse, has accomplished a lot during this period, breaking new ground and providing deep insights into our minds, our technologies, and the relationship between them.
The history of AI can, furthermore, be reviewed from different perspectives — humanistic, cognitive, sociological, and philosophical, among others. This essay will try to examine AI from two key perspectives — scientific and engineering. The former represents AI claims about the human mind and the nature of intelligence; the latter embodies the wide array of computer systems that are built by AI practitioners or by others who have or claim to have, taken inspiration from ideas in AI in order to solve a practical problem in an area of application. Currently, we are witnessing a resurgence of interest in, and application of, AI in areas such as education, video gaming, financial forecasting, medical diagnosis, health and elderly care, data mining, self-aware computing, and the Semantic Web. The list is illustrative, but it clearly indicates the broad range of application domains that draw on AI techniques for ideas and solutions.
In a general sense, whenever one encounters something qualified as “smart” or “intelligent” — as in “smartphones,” “smart homes,” “intelligent tutoring systems,” or “intelligent decision support” — an association with AI is implied. With the advent of computers in the 1950s, chess turned into a test for assessing the capabilities of AI machines. After a few decades when software techniques had been thoroughly explored and exploited, the obvious next step was to look at the hardware. Two main possibilities arise in this realm: The development of special-purpose hardware and the use of many processors at the same time. Both of these strategies were motivated by the idea that speed is a crucial component of Artificial intelligence. The idea of multiple processors, however, has proved to work effectively only in special domains such as chess.
The next approach in AI is motivated by the idea that a vast amount of knowledge is the key to intelligence. What we need in order to achieve human-level intelligence, the advocates of this approach argue, is to provide a machine with enough common-sense knowledge for it to be able to continue the process of knowledge acquisition on its own. Creativity, in art and music but also in daily activities, is considered a mark of intelligence. Studying creativity might indeed provide an acid test for AI and cognitive science: “Cognitive science cannot succeed if it cannot model creativity, and it is here that it is most likely to fail”