AI Techniques

What is AI?

AI (Artificial Intelligence) is the simulation of human intelligence processes by machines, especially computer systems.

AI is the ability of a machine to act, think and behave like a human being.

AI Techniques:

AI techniques are a method that is used to enable computers to show human-like intelligence activities (Virtual perception, Decision making, Speech recognization).

Intelligence requires Knowledge.

Characteristics of Knowledge:

It is Voluminous.

Difficult to characterize accurately.

It is constantly changing.

Differs from Data by being Organized.

AI technique is a method that exploits knowledge that should be represented in such a way that,

Knowledge captures Generalization.

Understandable by People.

Easily modifiable to correct errors.

Can be used in many situations.

Knowledge can be widely used even if it is incomplete or inaccurate.

There are three important AI techniques:

1. Search

2. Use of Knowledge

3. Abstraction

Search:

Provides a way of Solving problems for which no direct approach is available.

Based on exploring and finding (Hence, takes time).

There are two most common Search Techniques:

1. Blind Search

2. Heuristic Search

Blind Search & Heuristic Search:

	Blind Search	Heuristic Search
Definition	Blind search (uninformed search) is an AI approach that finds solutions using only available actions and their outcomes, without domain-specific knowledge.	Heuristic search(informed search) is an AI technique that uses domain-specific knowledge to guide the search process toward the goal state.
Goals	Locate the desired solution or goal state within a problem space.	Find solutions more efficiently by utilizing additional information.
Methods	Includes Depth-First Search (DFS), Breadth-First Search (BFS), Uniform-Cost Search, Iterative Deepening DFS (IDDFS), and more.	Includes A* (A Star), Greedy Best-First Search, and various informed search algorithms.
Information	Operates without using heuristics or estimates of goal proximity.	Utilizes heuristics or estimates to prioritize promising paths in the search space.
Advantages	Simple implementation works on various problems.	Can find solutions faster and with less exploration, especially in complex problem spaces.
Disadvantages	Inefficient for large search spaces, lacks optimality in some cases.	Relies on accurate and relevant heuristics, and might not guarantee optimality.
Use cases	When little domain knowledge is available or heuristics aren't effective.	When domain knowledge is available and can be used to guide the search effectively.

Various kinds of Search Techniques:

Use of Knowledge:

Provides a way of Solving Complex problems by exploiting the structure of objects that are involved.

We simply use domain knowledge for Problem-Solving.

Techniques:

Abstraction:

Provides a way of separating important features and vibrations from many unimportant ones.

Examples: Chess games, Route Planning, NLP (Natural Language Processing), Image Compression.

Some techniques are listed below:

1. Generalization

2. Modeling

3. hierarchy

4. Aggregation

5. Graph Abstraction

Summary:

In my blog, I delve into key AI techniques: search algorithms, utilizing knowledge, and abstraction. These techniques are crucial in AI problem-solving. Search algorithms systematically explore problem spaces, while using knowledge to enhance decision-making. Abstraction simplifies complex concepts for easier understanding. These techniques collectively empower AI to tackle diverse challenges effectively.

Thanks for reading!!