Artificial intelligence

Artificial intelligence

 

 

Artificial intelligence (AI) is the capacity of a computer or a computer-controlled robot to do activities that would normally be performed by intelligent individuals. The phrase is commonly used to refer to a project aimed at creating systems with human-like cognitive abilities, such as the capacity to reason, discern meaning, generalise, and learn from prior experiences. Since the invention of the digital computer in the 1940s, it has been proven that computers can be taught to perform extremely difficult jobs, such as finding proofs for mathematical theorems or mastering chess. Despite ongoing improvements in computer processing performance.

 

What does intelligence entail?

Intelligence is given to everything but the most basic human behaviour, yet even the most complex insect behaviour is seldom considered as a sign of intelligence. When a female wasp returns to her burrow with food, she places it on the threshold, checks for intruders within, and then takes her meal inside if the coast is clear. If the food is moved a few inches away from the entrance to her burrow while she is inside, the true nature of the wasp's instinctive behaviour is revealed: when she emerges, she will repeat the entire operation as many times as the food is displaced.

 

Reasoning entails making proper inferences based on the circumstances. Deductive and inductive inferences are the two types of inferences. “Fred must be in either the museum or the café,” for example, is an example of the former. “Previous accidents of this nature were caused by instrument failure; thus, this accident was caused by instrument failure,” and “Previous accidents of this nature were caused by instrument failure; therefore, this accident was caused by instrument failure.” The most important distinction between these two types of reasoning is that in deductive reasoning, the validity of the premises ensures the truth of the conclusion, whereas inductive reasoning provides credence to the conclusion without providing total assurance.

 

resolving issues

Problem solving, especially in artificial intelligence, may be defined as a methodical search through a set of options in order to arrive at a predetermined objective or solution. There are two types of problem-solving methods: special purpose and general purpose. A special-purpose technique is designed specifically for a problem and frequently takes use of extremely specific aspects of the context in which the problem exists. A general-purpose approach, on the other hand, may be used to solve a wide range of issues. Means-end analysis is a general-purpose AI approach that involves reducing the distance between the present state and the eventual objective incrementally.

 

AI methods and objectives

Connectionist vs. symbolic approaches

The symbolic (or "top-down") approach and the connectionist (or "bottom-up") approach are two different, and to some degree conflicting, approaches to AI research. The top-down method aims to duplicate intelligence by studying cognition in terms of symbol processing, regardless of the organic structure of the brain—hence the symbolic name. The bottom-up method, on the other hand, entails the creation of artificial neural networks that mimic the structure of the brain—hence the connectionist moniker.

 

Cognitive simulation, strong AI, and applied AI

AI research aims to achieve one of three goals using the approaches mentioned above: strong AI, applied AI, or cognitive simulation. The goal of strong AI is to create machines that can think. (The philosopher John Searle of the University of California at Berkeley coined the phrase "strong AI" to describe this type of study in 1980.) The ultimate goal of strong AI is to create a machine with mental abilities that are indistinguishable from those of a human. This objective sparked a lot of attention in the 1950s and 1960s, as mentioned in the section Early AI milestones.

Is it feasible to build a powerful AI system?

Applied AI and cognitive simulation, as discussed in the preceding sections of this article, appear to have a bright future. Strong AI, on the other hand—artificial intelligence that aspires to replicate human intellectual abilities—remains divisive. Its image has been tarnished by exaggerated claims of achievement in professional publications as well as the general press. Even an embodied system with the overall intellect of a cockroach is still proving elusive, much alone a system capable of competing with a human person. It is impossible to overestimate the difficulties of scaling up AI's modest successes.