Artificial Intelligence Strategies to Watch Out For
This article was published as a part of the Data Science Blogathon.
Artificial intelligence (AI) is the most dynamic stream in the world. Humans have always been curious about their abilities to predict, understand, act, and make decisions. Now, we can learn everything about it and create intelligent entities with this universal field of artificial intelligence.
In this article, we will glance at the four fundamental strategies that build AI.
What are the Core Artificial Intelligence Strategies?
Over the years, various researchers have defined AI in several ways with two common aspects: thought processes and behaviors.
Humanly thinking: A new endeavor to bring 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 making computers perform tasks better than people at a given time.
Acting rationally: Development of smart agents through computational intelligence.
Now, let us explore these strategies in detail:
To claim that a program behaves like a human, we must have some measure of how humans think. The following are the three aspects to do so:
Analyzing the functioning of the brain
Studying the actions of a person
After completing this mind theory, a precise computer program can be formed. This program will apply to machines and people as long as the input process and outcomes resemble human behavior. The first successful AI program, General problem solver, was developed by Allen Newell and Herbert Simon in 1957. In the study, they compared traces of the reasoning steps of the computer to those of human subjects solving similar concerns.
The field of cognitive science combines computer models from artificial intelligence with experiments from psychology to construct precise, testable theories about human behavior. The study of cognition is, however, necessarily based on actual human or animal experiments. The early days of AI were fraught with confusion: people would argue that an algorithm was an accurate model of human performance since it performed well on specific tasks. In this modern age, scientists separate these two kinds of claims, allowing cognitive science and AI to develop more rapidly.
One of the first philosophers to codify the right thinking, that is, inarguable reasoning, was Aristotle. His logic facilitated argument structures that always produced valid judgments when given appropriate assumptions. Studying these laws of thought led to the creation of the logic field, which investigates how the mind works. In the 19th century, logicians designed a specific notation for describing statements about different types of world entities. A logicist practice within artificial intelligence aims to create smart systems based on such programs. In this method, two primary blocks exist.
- First, formalizing informal knowledge in logical notation isn’t easy, especially when the knowledge isn’t 100% certain.
- Secondly, there is a vast 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 don’t have guidance on which reasoning steps to attempt first.
In 1950, Alan Turing proposed the Turing Test to define intelligence operationally. A computer passes the test if a human interrogator cannot distinguish whether the written responses are from a person or a computer after posing some written questions. For that, computers should have the following:
Automatic reasoning to utilize the accumulated data to respond to queries and to draw new findings
Machine learning to adjust to new possibilities and to notice and gather patterns.
Knowledge expression to store what it understands or listens
It was the intention of Turing’s test to avoid direct physical interaction between the interrogator and the computer because the physical simulation of people does not contribute to intelligence. Interrogators can, however, test a subject’s perceptual abilities through a video signal and pass physical objects “through the hatch” during the total Turing Test. A computer’s ability to perceive and manipulate objects will require computer vision, while its ability to move around and manipulate objects will 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 replicating an example.
Computer agents have to perform multiple tasks: function autonomously, sense their surroundings, endure over a long period, adjust to change, and seek objectives. An agent is nothing more than something that acts. It is a rational agent who serves to attain the best outcomes or, in cases of uncertainty, to acquire the best-expected results. Laws of thought saw AI as a process of making correct inferences. The ability to make accurate hypotheses is sometimes part of being a rational agent. It is because acting rationally means logically, concluding that a given action will accomplish one’s goals and then acting accordingly.
However, the correct inference does not always contain all the rationality; in some circumstances, there is no provably right way to proceed, but something still needs to be done. Furthermore, rational behavior is not always based on inference. It is usually more successful to recoil from a hot stove through a reflex action than to take a slower and deliberate action. Turing Test skills also enable agents to act rationally. Good decisions are enabled by knowledge representation and reasoning.
A complex society requires us to be able to produce comprehensible sentences in natural language. The purpose of learning is not only to improve knowledge but also to generate effective behaviors. As compared to the other approaches, rational agents have two advantages. The correct inference is only one of several 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 designs that are provably rational by defining the rationality standard mathematically and putting it into a general framework. However, human behavior is well suited to one specific environment and is defined by everything humans do. Due to complex computational demands, achieving perfect rationality is not always feasible.
By following these strategies, artificial intelligence is built, developed, and advanced to suit the requirements of the modern world. This article has combined the cultural background of AI with the practical hypothesis. Refer to the following key takeaways for a brief understanding:
Mathematicians developed a mathematical toolkit for manipulating logically certain, uncertain, probabilistic statements. Furthermore, they laid the foundation for understanding computations and algorithms.
Different people approach AI with different objectives.
Philosophers made AI possible by considering that a mind is a machine in some ways. This is because it relies on knowledge encoded in a language.
Are you aware of any other core principle behind AI? Please write to us in the comment section.
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