This article was published as a part of the Data Science Blogthon.
(AI) is the most dynamic stream in the world. Humans have always been curious about their abilities to predict, understand, act and decide. Now, we can learn all about it and create intelligent entities with this universal field of artificial intelligence.
Source: (Image by Gerd Altmann from Pixabay)
In this article, we’ll take a look at the four fundamental strategies that make up AI.
What are the Core Artificial Intelligence Strategies?
Over the years, various researchers have defined AI in a variety of ways with two common aspects: thought processes and behavior.
Humanly Thinking: A New Attempt at Bringing Computers to Life with Active Mind Sensing.
Rational thinking: a study of the computations that enable perception, thinking, and action.
Behaving humanly: An analysis of how computers perform better than humans in a given amount of time.
Acting Rationally: The Evolution of Smart Agents through Computational Intelligence.
Now, let’s look at these strategies in detail:
AI Core Strategies H
In order to claim that a program behaves like a human, we must have some measure of how humans think. There are three aspects to doing this:
brain function analysis
study a person’s actions
A precise computer program can be created after this mind theory is worked out. This program will be applicable to machines and people as long as the input process and result are similar to human behavior. The first successful AI program named General Problem Solver was developed by Alan Newell and Herbert Simon in 1957. In the study, they compared the computer’s traces of logical steps to those of human subjects solving similar concerns.
The field of cognitive science links computer models from artificial intelligence with experiments in psychology to build accurate, testable theories about human behavior. The study of cognition, however, is not necessarily based on actual human or animal experiments. The early days of AI were filled with confusion: People would argue that an algorithm was an accurate model of human performance because it performed well on specific tasks. In this modern age, scientists separate these two types of claims, allowing cognitive science and AI to develop more quickly.
One of the first philosophers to codify right thinking was Aristotle. His logic facilitated reasoning structures that always yielded valid judgments when given appropriate inferences. Studying these laws of thought led to the field of logic, which investigates how the mind works. In the 19th century, logicians devised a specific notation to describe statements about different types of world entities. A logical exercise within artificial intelligence aims to create smart systems based on such programs. In this method, there are two primary blocks present.
First, it is not easy to formalize informal knowledge into logical notation, especially when the knowledge is not 100% certain. Second, there is a big difference between solving a problem theoretically and in practice.
Computers can exhaust their computational resources when faced with problems with a few hundred facts if they do not have guidance on which reasoning steps to try first.
to behave humanely
In 1950, Alan Turing proposed the Turing Test to operationally define intelligence. A computer passes the test if a human interrogator after asking a few written questions cannot distinguish whether the written response is from a person or a computer. For that, the computer must have the following:
Automatic reasoning for using accumulated data to answer questions and draw new conclusions
Machine learning to adjust to new possibilities and to notice and collect patterns.
knowledge expression to store what one understands or hears
Turing’s test was intended to avoid direct physical contact between the interrogator and the computer because physical emulation of people does not contribute to intelligence. However, interrogators can test a subject’s perceptual abilities via a video signal and pass physical objects “through the hatch” during the Total Turing Test. A computer’s ability to see and manipulate objects would require computer vision, while its ability to move around and manipulate objects would require robotics. AI combines these six disciplines, and Turing deserves credit for designing a test that is still relevant today. Yet AI researchers have paid little attention to passing the Turing test, believing that studying the underlying principles of intelligence is more important than copying an example.
Computer agents have to perform many tasks: act autonomously, sense their surroundings, endure long periods of time, adjust to change, and seek objectives. An agent is nothing else that acts. It is a rational agent that acts to achieve the best results or best expected results in cases of uncertainty. Laws of Thought viewed AI as a process of making correct inferences. The ability to make accurate estimates is sometimes part of being a rational agent. This is because acting rationally means reasoning logically, concluding that a given course of action will best serve one’s goals and then acting accordingly.
However, the correct estimate does not always have all rationality; In some circumstances, there doesn’t prove to be a right way to proceed, but something needs to be done nonetheless. Furthermore, rational behavior is not always based on inference. It is usually more successful to step away from a hot stove through a reflex action than to take a slow and deliberate action. Turing test skills also enable agents to act rationally. Knowledge representation and reasoning enable good decisions.
natural language response
A complex society requires us to be able to construct comprehensible sentences in natural language. The purpose of learning is not only to improve knowledge but also to generate effective behaviour. Compared to other approaches, rational agents have two advantages. Correct inference is only one of many possible mechanisms for reaching rationality, which makes it more general than the “laws of thought” approach. Second, it can be developed scientifically more readily than approaches based on human behavior and thought. It is possible to modify agent design mathematically by defining the rationality standard and putting it into a general framework that is implicitly rational. However, human behavior is adapted to a specific environment and is defined by all the things humans do. Due to the complex computational demands, it is not always possible to achieve perfect rationality.
By following these strategies, Artificial Intelligence is created, developed and upgraded to suit the needs of the modern world. This article combines the cultural background of AI with practical hypotheses. Refer to the following key points for a brief understanding:
Mathematicians developed a mathematical toolkit to logically manipulate definite, indefinite, probabilistic statements. Furthermore, they lay the foundation for understanding computations and algorithms.
Different people approach AI with different objectives.
Philosophers have made AI possible by considering that the mind is in some ways a machine. This is because it relies on knowledge encoded in its language.
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