A Complete History of Artificial Intelligence: From Turing to Deep Learning

The journey of AI began with Alan Turing’s question, “Can machines think?” Let’s explore this grand history in an easy and engaging way.

• The background and early theoretical foundations of AI
• Causes and overcoming two ‘AI winters’
• How the deep learning revolution transformed our lives and industries

“Can machines think?” – The Beginning of AI History

The grand prologue of artificial intelligence history opened in 1950 with the fundamental question posed by British genius mathematician Alan Turing: “Can machines think?” This question went beyond mere technical possibility, sparking philosophical reflections on the nature of intelligence and serving as a guiding beacon for AI research over the following decades. Instead of the vague concept of ’thinking,’ Turing proposed a practical experiment to determine how similarly a machine could behave intelligently compared to a human — the ‘Turing Test.’

The core of the Turing Test lies in a functionalist perspective, focusing on the ‘function’ of intelligence. If a human interrogator cannot distinguish the machine from a human through text conversation, the machine is considered intelligent. This shift redefined intelligence as a software problem of information processing, separate from the brain as hardware, providing a theoretical foundation for computer scientists to pursue intelligent systems.

This article traces AI history deeply from Turing’s question, through the heated 1956 Dartmouth Conference, two harsh ‘AI winters,’ to the deep learning revolution permeating our daily lives, right up to the eve of the large language model (LLM) era.

Chapter 1: The Beginning of AI History: Birth and Dawn (1940s-1950s)

1.1. Theoretical Foundations: Cybernetics and Early Neural Networks

Before the term artificial intelligence was coined, the discovery that the brain is a vast electrical network of ’neurons’ sparked the idea: could machines imitate the brain?

This idea took shape combined with Norbert Wiener’s ‘Cybernetics’ and Claude Shannon’s information theory.

In 1943, Warren McCulloch and Walter Pitts introduced the first mathematical model of a brain neuron, the ‘artificial neuron.’ This simple structure outputs a signal when inputs exceed a threshold, proving it could perform basic logical operations (AND, OR, NOT). This became the theoretical cornerstone of artificial neural networks.

This theory was physically realized in 1951 by Marvin Minsky’s neural network machine, ‘SNARC,’ demonstrating its potential.

1.2. The 1956 Dartmouth Conference: The Official Birth of ‘Artificial Intelligence’

In summer 1956, a historic workshop at Dartmouth College united scattered research on ’thinking machines’ into a single academic field.

Young mathematician John McCarthy proposed the bold hypothesis that any aspect of learning or intelligence could be simulated by machines, coining the term ‘Artificial Intelligence’ and establishing the field’s identity.

Participants like Allen Newell and Herbert Simon optimistically predicted computers would beat chess champions within a decade. Though premature, the Dartmouth Conference’s enthusiasm catalyzed AI’s emergence as an independent discipline.

1.3. Early Successes and Two Approaches

Early AI researchers succeeded in ‘closed worlds’ with clear rules, such as games and logic proofs. Arthur Samuel’s checkers program learned by itself, embodying early ‘machine learning.’

The most striking achievement was the 1956 unveiling of the ‘Logic Theorist’ by Allen Newell and Herbert Simon, which proved 38 of 52 mathematical theorems, sometimes finding more elegant proofs than humans.

These successes highlighted two core AI approaches:

1. Connectionism: Bottom-up modeling mimicking brain structures.
2. Symbolism: Top-down modeling of human logic with symbols and rules.

At the time, the clear success of the Logic Theorist shifted AI research’s focus sharply toward symbolism.

Chapter 2: The Rise of Symbolism and the First AI Winter (1960s–early 1980s)

2.1. The Era of Symbolic AI (GOFAI)

The 1960s and 70s were dominated by Symbolic AI, also called ‘Good Old-Fashioned AI (GOFAI).’ This top-down approach explicitly programmed human expert knowledge into symbols and rules.

Its greatest strength was explainability—the ability to trace reasoning step-by-step. Symbolic AI was like a ’textbook genius’: perfect logic within fixed rules but fragile when facing ambiguous, unpredictable real-world problems.

2.2. Expert Systems: Commercializing Knowledge

Symbolic AI achieved commercial success through ‘Expert Systems’ that encoded domain experts’ knowledge for applications like medical diagnosis and mineral exploration.

Expert systems combined a ‘knowledge base’ and ‘inference engine,’ solving problems using IF-THEN rules. This demonstrated AI’s economic value and attracted substantial investment.

2.3. Limitations and the First ‘AI Winter’

However, by the mid-1970s, rosy expectations met harsh realities, triggering the first AI winter (c. 1974–1980).

• Combinatorial explosion: Real-world problems had too many variables for computers of the time.
• Knowledge acquisition bottleneck: Encoding expert knowledge and vast ‘common sense’ into code was nearly impossible, making systems brittle.
• Criticism of connectionism: Marvin Minsky’s 1969 book “Perceptrons” proved simple neural networks couldn’t solve basic logic problems like XOR, drying up funding for neural network research.

These limitations led to drastic cuts in AI research funding and a long period of stagnation.

Comparison: Symbolic AI vs Connectionist AI

Understanding the differences between these two approaches is key to grasping AI history.

• Strong for well-defined rule problems. | - Learns from data automatically.
• Robust to noise and incomplete data. | | Disadvantages | - Brittle with ambiguity and uncertainty.
• Knowledge acquisition bottleneck.
• Inflexible to new situations. | - Difficult to interpret (black box).
• Requires large training data.
• Computationally intensive learning. | | Historical Examples | Logic Theorist, expert systems (MYCIN), Deep Blue. | Perceptron, SNARC, AlphaGo. |

Chapter 3: The Revival of Connectionism and the Second AI Winter (1980s–early 2000s)

3.1. Backpropagation and Multilayer Neural Networks

Despite the first AI winter, a few researchers kept connectionism alive. In the mid-1980s, the ‘backpropagation algorithm’ emerged as a breakthrough to revive neural networks.

Backpropagation adjusts connection weights by propagating errors backward through the network, enabling effective training of multilayer networks with multiple ‘hidden layers.’ This brought connectionism back to the forefront of AI research.

In 1989, Yann LeCun’s handwritten digit recognition system demonstrated the practical utility of backpropagation-based deep neural networks in industry.

3.2. Deep Blue vs Kasparov: A Milestone in Machine Intelligence

In 1997, symbolic AI regained public attention when IBM’s chess supercomputer ‘Deep Blue’ defeated world champion Garry Kasparov.

Deep Blue’s victory was the pinnacle of brute-force search, calculating 200 million moves per second. It was more a triumph of computational power than intelligence but renewed public belief in AI’s potential.

3.3. The Second ‘AI Winter’ Returns

Despite signs of revival, AI faced a second winter from the late 1980s to early 2000s.

• Commercial failures: Expert systems failed to solve real-world problems at scale, collapsing their market.
• Technical limits: Computing power was insufficient to train deep networks effectively; the ‘vanishing gradient problem’ made training deep layers extremely difficult.

These issues led to funding cuts again, leaving AI with powerful algorithms but lacking sufficient data and computing resources, resulting in prolonged stagnation.

Chapter 4: The Deep Learning Revolution: Becoming the Mainstream of AI History (Mid-2000s Onward)

4.1. The Three Catalysts of the Deep Learning Revolution

After two winters, three key factors converged in the mid-2000s, ushering in AI’s spring.

1. Big Data: The internet enabled unprecedented data accumulation, notably the ‘ImageNet’ dataset with over 14 million images proved decisive.
2. GPU Computing: Graphics Processing Units, developed for gaming, were found highly efficient for parallel neural network training, enabling affordable deep network training.
3. Algorithmic Advances: In 2006, Geoffrey Hinton and others proposed methods to mitigate the ‘vanishing gradient problem,’ enabling stable training of deep networks and marking the start of the ‘Deep Learning’ era.

4.2. Defining Moments: ImageNet Competition and Speech Recognition

Deep learning’s potential was proven in 2012 at the ‘ImageNet Large Scale Visual Recognition Challenge (ILSVRC).’ Geoffrey Hinton’s team’s ‘AlexNet’ won with overwhelming performance, heralding the deep learning era. By 2015, deep learning models surpassed human-level image recognition error rates (~5%).

Simultaneously, deep learning dramatically reduced error rates in speech recognition, boosting smartphone voice assistants and automatic translation services.

4.3. The AlphaGo Shock: Intelligence on a New Level

The pinnacle of the deep learning revolution came in March 2016 when Google DeepMind’s ‘AlphaGo’ defeated world top Go player Lee Sedol 4–1.

While Deep Blue’s win showcased computational power, AlphaGo’s victory shocked the world. Go’s complexity (with more possible moves than atoms in the universe, ~10^360) makes brute-force search impossible. AlphaGo combined deep learning and reinforcement learning to emulate human-like intuition.

AlphaGo played creative moves never seen in human games, demonstrating AI’s ability not just to imitate but to create new knowledge. The AlphaGo shock symbolized deep learning’s capacity for complex strategic thinking and creative problem-solving.

Chapter 5: AI Integrated into Our Lives: Changes in Daily Life and Industry

Since the deep learning revolution, AI has left labs and fundamentally transformed our daily lives and industries.

5.1. AI in Everyday Life: Invisible Intelligence

Behind the convenience we experience daily are deep learning algorithms.

• Content Recommendation Systems: YouTube and Netflix analyze user preferences with AI to personalize recommendations.
• Personal Assistants and Smart Homes: AI voice assistants like Apple Siri and Google Assistant understand natural language commands and perform various tasks.
• Image Recognition and Processing: Smartphone cameras automatically detect scenes and adjust settings; apps apply fun AR filters by recognizing faces.

5.2. AI in Industry: New Horizons of Efficiency

• Manufacturing: In ‘smart factories,’ AI predicts equipment failures and automatically detects minute defects via vision systems.
• Finance: AI chatbots provide 24/7 customer service, robo-advisors manage assets, and AI prevents financial fraud.
• Healthcare: Deep learning models detect tiny cancer cells in medical images, aiding early diagnosis.

5.3. Autonomous Driving: Changing the Paradigm of Mobility

Autonomous vehicles are a culmination of AI technologies. They fuse sensor data to create 3D environmental maps, use deep learning-based computer vision to identify other vehicles, pedestrians, and traffic signals, and predict their movements.

Based on this perception, AI plans routes, controls the vehicle, and makes safe driving decisions. Full autonomy is still distant, but AI is already present in advanced driver-assistance systems (ADAS) like lane-keeping assist.

These consumer AI services are both the ‘products’ of AI technology and a vast ‘data engine’ generating data to train the next generation of AI, creating a powerful virtuous cycle.

Conclusion: Questions the Past Poses to the Future

AI history began with Turing’s question, endured two harsh ‘AI winters,’ and became part of our lives through the deep learning revolution built on big data and GPUs.

Looking back at AI’s path up to the LLM era reveals key insights:

1. AI development is cyclical. It has progressed through tensions between symbolism and connectionism, repeating cycles of hype (boom) and disappointment (winter).
2. Innovation results from convergence. The deep learning revolution was not just an algorithm but the synergy of big data, computing power, and algorithmic improvements.
3. Past challenges remain relevant. Ethical and social issues like data bias, privacy, the ‘black box’ problem, and job displacement, raised in the past, are even more critical today.

Ultimately, revisiting AI’s past is essential to understanding the current LLM revolution and wisely navigating future changes. The decades-old question, “How can machine intelligence be aligned with human values and goals?” remains the most important challenge of our time.

Based on these lessons, how can we use the powerful tool of LLMs for the benefit of humanity? Please share your thoughts in the comments.