Artificial intelligence(AI) is the study of how to make computers do things that, at the moment, people do better. Moreover, Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with making machines capable of performing tasks that typically require human intelligence. Throughout this article, we would try to define AI techniques.
Are there any techniques that are appropriate for the solution of a variety of Artificial intelligence problems? The answer to this question is yes, Artificial Intelligence Techniques are the solution to a variety of problems. The study by the researcher has found that intelligence requires knowledge. Knowledge is the information about a domain. Solving many problems requires much knowledge.
From our above discussion, we can conclude that AI techniques are a method that exploits knowledge, that should be represented in such a way that:
- Knowledge captures generalizations.
- It is not necessary to represent separately each situation. Knowledge not having such important properties are called “data” rather than knowledge.
- Understood by people who must provide it.
- Modified to correct errors and reflect changes in the world.
- Useable in a great many situations.
Important Classes of Artificial Intelligence Techniques
There are two types of important classes of Artificial Intelligence Techniques methods for representing and using knowledge and methods for conducting a heuristic search.
These two aspects interact heavily with each other. The choice of a knowledge representation framework determines the kind of problem-solving methods that can be applied.
Knowledge serves two important functions in AI programs. Firstly, it defines what can be done to solve a problem and specify what it means to have solved the problem. Secondly, it provides advice on how best to go about solving a problem efficiently.
Mostly Used Artificial Intelligence Techniques
We will get an overview of the most used Artificial Intelligence Techniques. It is probably not possible to give a precise definition of Artificial Intelligence Techniques. But we will discuss some examples of what one is.
The heuristics technique is one of the most basic techniques used for AI and is based on the principle of trial and error. It learns from its mistakes. Heuristics is a problem-solving method that uses shortcuts to produce good-enough solutions given a limited time frame or deadline.
Heuristics are a flexible technique for quick decisions, particularly when working with complex data. Decisions made using a heuristic approach may not necessarily be optimal. Derived from the Greek word meaning “to discover”.
2. Natural Language Processing
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. It is a field of artificial intelligence in which computers analyze, understand, and derive meaning from human language in a smart and useful way.
By using NLP, one can organize and structure knowledge to perform tasks such as automatic summarization, translation, sentiment analysis, and speech recognition.
Also Read: What are the Differences Between RPA and AI?
3. Artificial Neural Network
An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. Moreover, it is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards.
ANNs have self-learning capabilities that enable them to produce better results as more data becomes available. In conclusion, the idea of ANNs is based on the belief that the working of the human brain.
4. Genetic Algorithms
Genetic algorithms provide computers with a method of problem-solving that is based upon the implementations of evolutionary processes. In other words, this allows you to explore a space of parameters to find solutions that score well according to a “fitness function”.
Therefore, they are a way to implement function optimization: given a function g(x) (where x is typically a vector of parameter values), find the value of x that maximizes (or minimizes) g(x).
5. Support Vector Machines
In these types of problems, the objective is to determine whether a given data point belongs to a certain class or not. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future.
The main idea behind SVM is that you try to find the boundary line that separates the two classes, but in such a way that the boundary line creates a maximum separation between the classes.
Artificial Intelligence and technology are one side of life that always interest and surprise us with new ideas, topics, innovations, products …etc. Till now AI is not implemented as the Marvel films represent it ( Iron Man).
However, there are many important tries to reach the level and to compete in the market, like sometimes the robots that they show on TV. Nevertheless, the hidden projects and the development of industrial companies.