Artificial Intelligence: Foundations, Goals, Techniques, and Applications

Introduction

What is Artificial Intelligence?

Artificial Intelligence (AI) is defined by John McCarthy, one of its pioneers, as “the science and engineering of making intelligent machines, especially intelligent computer programs.” AI enables computers, robots, and software to think intelligently, emulating human cognition in tasks requiring thought, learning, and decision-making.

AI develops by examining human cognitive processes—how we learn, decide, and solve problems—and then creating software and systems that replicate these abilities.

Philosophy of AI

The origin of AI stems from a fundamental question: “Can a machine think and behave like a human?” This curiosity led to the pursuit of creating machines capable of human-like intelligence, allowing computers to simulate and, potentially, surpass human cognitive functions.

Goals of AI

1. Create Expert Systems – Systems that demonstrate intelligent behavior, learn, and provide explanations and advice to users.
2. Implement Human Intelligence in Machines – Develop machines that understand, learn, and behave like humans.

Key Contributors to AI

AI integrates various fields, including Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. Its core objectives are to develop reasoning, learning, and problem-solving abilities within machines.

Programming With and Without AI

AI programming allows for dynamic problem-solving and adaptability, unlike traditional programming, which only addresses specific, predefined issues.

Programming Without AI	Programming With AI
Solves specific problems only.	Solves a range of general problems.
Structure changes with modification.	Easy updates without structure impact.
Modifications are complex and slow.	Fast and simple modifications.

What is an AI Technique?

AI techniques organize and process vast, unstructured, and evolving information. Effective AI techniques enable:

• Understandability by contributors.
• Easy error correction.
• Applicability in incomplete or imprecise contexts. These techniques also boost execution speed, especially in complex scenarios.

Applications of AI

AI applications are diverse, influencing multiple domains:

• Gaming – AI enhances strategic games like chess by considering numerous potential moves based on heuristics.
• Natural Language Processing – AI enables human-computer interaction through natural language.
• Expert Systems – Provides reasoning and advice through integrated machines and software.
• Vision Systems – AI interprets visual data, aiding in tasks like mapping or medical diagnostics.
• Speech Recognition – AI can process and understand spoken language across different accents and dialects.
• Handwriting Recognition – Recognizes and converts handwritten text to editable digital formats.
• Intelligent Robots – Equipped with sensors, these robots perform complex tasks, adapt to environments, and learn from their experiences.

History of AI

Below are some key milestones in AI history during the 20th century:

Year	Milestone/Innovation
1923	The term "robot" appears in Karel Čapek's play "Rossum's Universal Robots".
1943	The foundation of neural networks.
1950	Alan Turing introduces the Turing Test, evaluating machine intelligence.
1956	John McCarthy coins "Artificial Intelligence"; the first AI program is demonstrated.
1958	Development of the LISP programming language, foundational for AI.
1964	MIT shows that computers can solve algebraic problems through natural language.
1969	Shakey the Robot, capable of perception and problem-solving, is developed at Stanford Research Institute.
1979	Stanford Cart becomes the first autonomous vehicle.
1997	Deep Blue, an AI-based chess program, defeats world champion Garry Kasparov.
2000	Robot pets become available; MIT unveils Kismet, a robot expressing human-like emotions.