CHAPTER ONE | DEMYSTIFYING ARTIFICIAL INTELLIGENCE
The following is a brief overview of the history of AI. From the first recorded concepts of AI and ancient history to AI today
The history of artificial intelligence (AI) is a fascinating journey that spans centuries.
Ancient Concepts (Antiquity):
The concept of creating artificial beings with human-like intelligence can be traced back to ancient myths and legends. Stories of automatons and mechanical beings can be found in Greek, Egyptian, and Chinese mythology.
The term Automata for example derives from an ancient Greek (αὐτόματον) word meaning “self-acting” which aligns with the concept of mechanical devices that imitate humans.
The Mechanical Turk (18th Century):
In the 18th century, Wolfgang von Kempelen built the Mechanical Turk, a mechanical chess-playing automaton. While it had a human operator inside, it sparked interest in the idea of creating machines that could mimic intelligent behavior.
Early Computing Machines (20th Century):
The development of electronic computers in the mid-20th century laid the foundation for modern AI. Mathematician and logician Alan Turing played a crucial role, introducing the concept of a universal machine and the Turing Test.
Dartmouth Conference (1956):
The term “artificial intelligence” was coined at the Dartmouth Conference in 1956. This conference marked the beginning of AI as an interdisciplinary field, bringing together computer scientists, mathematicians, and cognitive psychologists.
Symbolic AI (1950s-1960s):
Early AI systems were based on symbolic reasoning, using rules and logic to perform tasks. One notable project was the General Problem Solver (GPS) developed by Herbert Simon and Allen Newell.
AI Winter (1970s-1980s):
Despite early enthusiasm, progress in AI slowed down during this period due to high expectations that weren't met. Funding was reduced, leading to what is known as the “AI winter.”
Expert Systems (1980s-1990s):
AI research shifted towards expert systems, which were rule-based systems designed to emulate human expertise in specific domains. While successful in certain applications, they had limitations in handling uncertainty and complexity.
Machine Learning Renaissance (1990s-Present):
Advances in machine learning, especially neural networks, reignited interest in AI. Breakthroughs in algorithms, increased computing power, and the availability of large datasets led to significant progress.
Deep Learning and Neural Networks (2010s-Present):
Deep learning, a subset of machine learning involving neural networks with many layers, has revolutionized AI. This approach has achieved remarkable success in image recognition, natural language processing, and other tasks.
Current State of AI (2020s):
AI is now integrated into various aspects of daily life, from virtual assistants like Siri and Alexa to sophisticated applications in healthcare, finance, and autonomous vehicles. Ethical considerations and responsible AI development are also gaining prominence.
This timeline offers a glimpse into the evolution of AI, from ancient myths to the complex and powerful systems we have today.
3. Different types of AI: From machine learning to deep learning