It is a branch of computer science that seeks to design intelligent machines, that is, equipped with computer systems capable of simulating human abilities in logical thinking and learning in order to perform tasks that usually require human intelligence to accomplish...
Artificial intelligence (AI) is a branch of computer science. The simplest definition of it is that it is the science that makes machines think like humans, i.e. a computer with a...
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The capabilities of artificial intelligence in facial recognition have developed very quickly in a short period of time, and these systems work by comparing facial features...
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One example of using artificial intelligence to improve smartphone photos is the Super Res Zoom feature that allows users to digitally zoom in while taking a photo using an...
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The term “drone” combines “drone” and part of the word “robot.” Drones have now become one of the advanced tools in modern warfare.
The world is entering an era in which the so-called “strong” artificial intelligence is dominating, trying to compete with the cognitive capabilities of humans in terms of creating opportunities and creating risks in terms of political, economic, financial and cyber security. The world's major powers have started a race against time in order to achieve leadership and control the capabilities resulting from the uses of artificial intelligence, which is an indicator of the present and future of international leadership. The volume of international investment in artificial intelligence has been increasing since 2010 by about 60% annually, which makes it possible to increase the AI quotient of companies.
Here we can ask: Where are we now? and what?. The manifestations of the age of artificial intelligence say that we live in an era of agricultural economy and medieval feudalism. It may not be correct to say this description, when we understand that it may injure someone, but the essence of the age in which we live is the one who reveals it.
The era of agricultural economy imposed the existence of only two classes in society, and no third, namely the landlord class, who were famous for their feudalism, bourgeoisie and blue-blooded people in the descriptions of history and historians, who dealt with the issue of the injustice of those classes in those eras. The second is the toiling class and the workers, who, according to the laws of the landlord class, are not entitled to own or be freed from work in the landlords' fields and lands.
It is artificial intelligence that specializes in one field, for example, there are artificial intelligence systems that can beat the world champion in the game of chess, which is the only thing they do.
This type refers to computers with the level of human intelligence in all fields , that is, it can perform any intellectual task that a person can perform. Creating this type of intelligence is much more difficult than the previous type and we have not reached this level yet.
Oxford philosopher Nick Bostrom defines superintelligence as “thought far smarter than the best human minds in nearly every field, including scientific creativity, general wisdom, and social skills,” and because of this type the field of artificial intelligence is an interesting area to delve into.
In the 1940s and 1950s, a number of researchers explored the relationship between neuroscience, information theory, and cybernetics. Some of them built machines that use electronic networks to display primitive intelligence such as turtles and. W. Gray Walter and the Johns Hopkins Monster. Many of these scholars gathered for meetings of the Teleological Society at Princeton University and the Relativity Club in England. By 1960, the curriculum was largely obsolete, although some elements of it came to life again in the 1980s.
When digital computers became possible in the mid-1950s, artificial intelligence research began to explore the possibility that human intelligence could be reduced to symbol control. The research center of the three institutions: CMU, Stanford and the Massachusetts Institute of Technology, each developed its own method of research. John Haugeland called these approaches to AI "good old fashioned AI" or "GOFAI".
During the 1960s, symbolic approaches had great success in simulating higher-level thinking in small representative programs. Curricula based on cybernetics or neural network have been abandoned or pushed into the background. In the 1980s, though, advances in symbolic artificial intelligence stalled, and many believed that symbolic systems would not be able to simulate all processes of human cognition, particularly perception, robotics, learning and pattern recognition. A number of researchers have begun looking at "semi-symbolic" approaches to specific problems in artificial intelligence.
In the 1990s, AI researchers developed complex mathematical tools to solve specific sub-problems. These tools are truly scientific, in the sense that their results are both quantifiable and verifiable, and have been responsible for many of the recent successes of AI research. This common mathematical language also allows for a high level of collaboration with more fields (such as mathematics, economics, or operations research). Russell Stewart and Peter Norvig described this movement as nothing less than a "revolution" and a "victory for the regulars".
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