Introduction
• Definition of Artificial Intelligence
- Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI also encompasses subfields like machine learning, deep learning, and natural language processing (NLP).
• Historical Background
o. Early Concepts and Theories
- Mythology and Early Ideas: Concepts of artificial beings with intelligence appear in ancient myths and literature, such as the Greek myth of Talos or the Golem of Jewish folklore.
- Philosophical Foundations: Philosophers like Aristotle and René Descartes explored ideas related to human cognition, logic, and reasoning, laying the groundwork for later AI theories.
- 19th Century Beginnings: Charles Babbage’s Analytical Engine and Ada Lovelace’s notes, which included the first algorithm intended to be processed by a machine, are often cited as the earliest conceptual steps toward AI.
o. Milestones in AI Development
- 1940s-1950s: Birth of AI as a Field
- Alan Turing’s 1950 paper “Computing Machinery and Intelligence” introduced the concept of the Turing Test, a criterion of intelligence for machines.
- The Dartmouth Conference of 1956 is often considered the birth of AI as an academic discipline, where the term “artificial intelligence” was first coined by John McCarthy.
- 1960s-1970s: Early AI Programs and Research
- Development of early AI programs like ELIZA (a natural language processing program) and Shakey the Robot (the first general-purpose mobile robot).
- Research in symbolic AI, focusing on logic and problem-solving.
- 1980s-1990s: Expert Systems and AI Winter
- The rise of expert systems, which used rule-based reasoning to simulate the decision-making ability of a human expert.
- The “AI Winter,” periods of reduced funding and interest due to unmet expectations and limitations in early AI technologies.
- 2000s-Present: Resurgence and Modern AI
- The resurgence of AI driven by advances in machine learning, especially deep learning, fueled by large datasets and increased computational power.
- Breakthroughs in natural language processing (e.g., GPT series), computer vision (e.g., ImageNet), and robotics.
• Importance and Relevance of AI in Modern Society
- Transformation Across Industries: AI is revolutionizing industries like healthcare, finance, education, and transportation by improving efficiency, accuracy, and enabling new capabilities such as personalized medicine and autonomous vehicles.
- Economic Impact: AI is a key driver of economic growth, fostering innovation and creating new job opportunities, though also raising concerns about job displacement and the future of work.
- Social and Ethical Implications: AI’s influence extends to societal and ethical issues, such as privacy, surveillance, bias, and the need for regulation to ensure fair and responsible use.
- Global Competitiveness: Nations are investing heavily in AI research and development to maintain or gain leadership in the global economy, making AI a critical element of geopolitical strategy.
