Artificial intelligence refers to machines or software that model human intelligence. It also refers to the development of computer-based technology to replicate the complex processes of human cognition such as reasoning or learning, with the objective of creating machines or software capable of intelligent behavior such as incorporating experiences into new endeavors, learning from mistakes, and engaging in creative problem solving.
The study of artificial intelligence (AI) accelerated as advances in computer technology made it possible to create more and more sophisticated machines and software programs, such as those that exhibit human thinking processes. The field of AI is dominated by computer scientists, but because AI is derived from cognitive science, it has important ramifications for psychologists as well. AI supports the idea that the brain is an information processing device, much like a computer. Cognitive psychology is an underlying science of AI and creating machines that reproduce human thought allows much to be learned about the way the human brain thinks. Artificial intelligence research and cognitive psychology come together within a common conceptual framework that allows the replication of human cognitive processes through the mathematical manipulation of encoded symbols. The encoding is the means by which computer scientists attempt to represent human mental processes, and the goal is to have the most accurate representation possible.
Creating a machine using a model of human thinking, a process called modeling, highlights the complexities and subtleties of the human mind. For example, creating a machine to recognize objects in photographs would initially seem rather simple. Yet when humans look at a photograph, they do so with expectations about the limitations of photography. Our brains fill in the missing third dimension and account for other missing or inconsistent images with our sense of what the real world looks like. To program a computer to make those kinds of assumptions would be a huge task. Consider all the information a computer would need to understand that the array of images all pressed up against a flat surface actually represents the three-dimensional world. In contrast, the human mind is capable of decoding such an image almost instantaneously.
This work of simulating human thought processes has led to the development of new ideas in information processing. Examples include: the concept of fuzzy logic, in which a computer is programmed to think in broader terms than either/or and yes/no; expert systems, a group of programming rules that describe a reasoning process that allows computers to adapt and learn; data mining, detecting patterns in stimuli and drawing conclusions from them; genetic algorithm, a program that provides for random mutation that allows the machine to improve itself; and several other information processing techniques.
The National Institute of Mental Health reports that there were 9.6 million adults over age 18 with serious mental illness of any kind in 2012, representing 4.1% of all U.S. adults. More adults between ages 25 and 49 years had mental illness than other age groups, and females were diagnosed with mental illness more often than males. Among teenagers aged 13 to 18, 21.4% had a lifetime risk of being diagnosed with a severe mental disorder. The Centers for Disease Control and Prevention reports that approximately 13% of children ages 8 to 15 had a diagnosable mental disorder in 2011. In response to increasing prevalence of mental illness, databases grew correspondingly and AI information processing techniques such as knowledge discovery, data mining, and modeling were applied more and more to help diagnose mental illness. Detection and modeling of Alzheimer's disease is one area of study that may help predict which adults are predisposed for the disease or already have the disease. A model of alcoholism with 20 different attributes of mental patterns in alcoholism has been applied to evaluate related effects on the emotions and behavior of dependent children of alcoholic parents.
Expert systems are being used to better understand autism and to help diagnose this mental disorder in children. AI gaming systems use virtual techniques to impart valuable training to autistic children and even offer positive reinforcement. Therapists and teachers are able to use these computer training tools to teach autistic children life skills, social interaction abilities, and cognitive processes such as paying attention and developing vocabulary. The programs are adaptable for local languages, individual problems, rehabilitation, and overcoming social and communication barriers.
AI technology has been used in machines that track financial investments, assist doctors in diagnoses, help identify adverse interactions in patients on multiple medications, and spot credit card fraud. An Australian scientist working in Japan attempted to create a silicon brain using quantum resistors. Reported first in a 1995 article in Business Week, Hugo de Garis and a team of scientists worked to create a computing system capable of reproducing itself. As Business Week reported, the project would attempt to “not only coax silicon circuits into giving birth to innate intelligence but imbue them with the power to design themselves— to control their own destiny by spawning new generations of ever improving brains at electronic speeds.” This type of technology is called evolvable hardware.
Even with all of these technological advances, many people are skeptical that a machine will ever reproduce human cognition. Marvin Minsky, a scientist at the Massachusetts Institute of Technology, stated that the hardest thing of all in the creation of artificial intelligence is building a machine with common sense.
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