1. Conceptual Beginnings (1940s-1950s)
- 1943: McCulloch & Pitts model the first artificial neuron.
- 1950: Alan Turing’s “Computing Machinery and Intelligence” introduces the Imitation Game (Turing Test).
- 1956: Dartmouth Workshop – the term “Artificial Intelligence” coined; formal birth of AI discipline.
McCulloch & Pitts Neuron (1943): The First Artificial Neuron
Warren McCulloch and Walter Pitts published “A Logical Calculus of the Ideas Immanent in Nervous Activity,” introducing the first mathematical model of an artificial neuron—the McCulloch–Pitts neuron. Their simple binary, threshold-based neuron showed how networks of such units could compute logical functions and even match the power of a Turing machine. This foundational idea—that cognition could be modeled computationally—launched early research into neural networks and set the stage for modern AI.
Core Idea
A neuron behaves like a binary switch: it fires (1) or stays silent (0).
Inputs
- Excitatory inputs → push the neuron to fire
- Inhibitory inputs → completely block firing
- A fixed threshold decides when firing happens
Rule
The neuron fires only if enough excitatory signals reach the threshold AND no inhibitory input is active.
Why It Matters
- First formal model showing that neural circuits can compute logic (AND, OR, NOT).
- Demonstrated that networks of neurons could be as powerful as a Turing machine.
- Foundation for neural networks, perceptrons, and modern deep learning.
1950: Alan Turing’s Defining Moment: In his seminal paper, “Computing Machinery and Intelligence,” Alan Turing introduced the “Imitation Game,” which we now call the Turing Test. He proposed that if a machine could converse with a human in a way that was indistinguishable from another human, it could be considered intelligent.
1956: The Dartmouth Workshop: In the summer of 1956, a group of researchers gathered at Dartmouth College (USA) for a workshop proposed by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester. The proposal introduced, for the first time, the term “Artificial Intelligence.” They proposed to explore the idea that every aspect of human intelligence—learning, reasoning, perception, and problem-solving—could be precisely described and simulated by machines.
Perceptron:
Inputs are scaled by weights which decide the contribution of each input to the output of the perceptron. These weights are determined by learning process. Activation Function: The weighted sum of inputs is passed through an activation function Eg. step function to determine the output as 0 or 1.
2. Symbolic AI Era (1960s-1970s)
The first wave of AI research focused on Symbolic AI, where researchers believed intelligence could be replicated by manipulating symbols and rules with a logical, top-down approach.
1958: Lisp, a language well-suited to symbolic AI, is developed.
1966: The ELIZA Chatbot: Joseph Weizenbaum created ELIZA, a simple natural language processing program that could engage in a surprisingly convincing conversation by matching keywords and applying pre-written responses. While not truly intelligent, it highlighted the potential for human-machine interaction.
1969: The Perceptron Controversy: The field faced its first major setback when Marvin Minsky and Seymour Papert published their book Perceptrons. It highlighted the mathematical limitations of single-layer neural networks, which significantly reduced funding and research in that area for years.
1972: Prolog was developed
3. Expert Systems & First AI Winter (1970s-1980s)
The immense optimism of the 1960s was met with disappointment as AI programs failed to scale beyond simple, “toy” problems. This led to a significant drop in funding, known as the First AI Winter.
However, a new, more pragmatic approach called expert systems brought AI back into the commercial spotlight.
1978: The Expert Systems Boom: This new wave of AI used vast knowledge bases and “if-then” rules to solve problems in a specific, narrow domain. The first commercially successful expert system, XCON, developed by Carnegie Mellon for Digital Equipment Corporation, was used to configure computer orders and saved the company millions.
1981: Japan’s Fifth Generation Project: The Japanese government announced a major, state-funded initiative to build a new generation of supercomputers with AI capabilities. This ignited a global race in AI research.
Widespread Adoption: Companies began heavily investing in expert systems, particularly in finance, medicine, and manufacturing. This created a new, billion-dollar industry.
Symbolic AI tried to build general intelligence using logic, while Expert Systems focused on practical, domain-specific intelligence using large collections of rules.
Symbolic Era of AI & Expert Systems Era
| Aspect | Symbolic AI Era (1956–early 1970s) | Expert Systems Era (mid-1970s–late 1980s) |
|---|---|---|
| Core Idea | Intelligence = manipulating symbols via logic | Intelligence = encoding human expert knowledge in rules |
| Goal | Build machines that reason like humans using logic, search, and problem decomposition | Capture domain-specific expertise to make practical decisions |
| Key Methods | Logic, theorem proving, search algorithms, semantic networks, planning | Rule-based systems, inference engines, if–then rules, knowledge bases |
| Knowledge Source | Abstract models of reasoning | Human experts in domains (medicine, finance, troubleshooting) |
| Strengths | Good for math-like reasoning, puzzles, formal problem-solving | Very strong for narrow domains with clear rules |
| Limitations | Too brittle; lacked real-world knowledge; could not scale | Knowledge bottleneck, maintenance complexity, poor learning ability |
| Representative Systems | General Problem Solver (GPS), Logic Theorist, SHRDLU | MYCIN, DENDRAL, XCON, CLIPS |
| Outcome | Led to AI’s theoretical foundations | Led to first commercial success and later to the 1980s AI boom and bust |
4. Machine Learning Resurgence (1990s-2000s)
The expert systems boom was short-lived. The expert system models proved difficult to maintain and scale. They were brittle, lacking the flexibility to handle situations outside their rigid rule-sets. The specialized hardware and high maintenance costs led to another period of reduced funding.
Shift from rules to statistical learning from data.
1997: IBM’s Deep Blue: In a landmark event, the IBM supercomputer Deep Blue defeated world chess champion Garry Kasparov. This was a significant victory for statistical brute-force computation and search algorithms over the symbolic reasoning that defined earlier AI.
The Internet as a Data Catalyst: The widespread adoption of the internet created an unprecedented volume of data. This was the perfect fuel for data-hungry machine learning algorithms, which began to outperform older, rule-based systems.
5. Deep Learning Revolution (2010s)
This period marks the beginning of the modern AI era. Thanks to a combination of more powerful GPUs, large-scale data, and new algorithmic techniques, multi-layered neural networks—or deep learning—became a reality.
2012: The AlexNet Breakthrough: A deep convolutional neural network named AlexNet won the annual ImageNet competition by a wide margin. Its dramatic success in image recognition proved the effectiveness of deep learning and sparked a new gold rush in AI research.
2017: The Transformer Architecture: Google researchers published the paper “Attention Is All You Need,” which introduced the Transformer architecture. This new model, which used a powerful self-attention mechanism, was able to process long sequences of data in parallel, fundamentally changing the landscape of natural language processing.
6. Generative AI / Foundation Models (2020s – Present)
The Transformer architecture, combined with vast computational resources, has led to the development of powerful foundation models that can create, rather than just analyze. This has brought AI from the research lab to the hands of the public.
2018-2023: The Rise of LLMs: Building on the Transformer, OpenAI released GPT-1, the first in a series of highly influential Large Language Models. Models like GPT, BERT, and Gemini demonstrated an unprecedented ability to generate human-like text, translate languages, and write code.
2022: Creative AI for the Masses: The release of tools like DALL-E and Midjourney made text-to-image generation accessible to the public, showcasing the creative potential of generative AI.
The story of AI is far from over. Today’s AI is focused on fine-tuning these models for specific tasks, building multimodal systems that can understand and generate multiple types of data, and addressing the significant ethical and safety challenges that come with this powerful technology.
References
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
Turing, A. M. (1950). “Computing Machinery and Intelligence.” Mind, 59(236), 433-460.
Krizhevsky et al. – “ImageNet Classification with Deep Convolutional Neural Networks.”
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Dr. Selvaraj Vadivelu 