The history of the formation and development of AI

Artificial Intelligence (AI) today has become a familiar part of modern life, appearing in every field from business to healthcare. However, few realize that the history of AI development began in the mid-20th century and went through many ups and downs before achieving the explosive breakthroughs we see today.

This article by INVIAI offers a detailed look at the history of AI's formation and development, from the initial early ideas, through the difficult "AI winters," to the deep learning revolution and the generative AI wave that exploded in the 2020s.

1950s: The Beginning of Artificial Intelligence

The 1950s are considered the official starting point of the AI field. In 1950, mathematician Alan Turing published the paper "Computing Machinery and Intelligence," in which he proposed a famous test to evaluate a machine's ability to think – later known as the Turing Test. This milestone introduced the idea that computers could "think" like humans, laying the theoretical foundation for AI.

Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

— Dartmouth Conference Declaration, 1956

Key Technical Developments

• Lisp Programming Language (1958) – John McCarthy invented Lisp, designed specifically for AI development
• Perceptron (1958) – Frank Rosenblatt introduced the first artificial neural network model capable of learning from data
• "Machine Learning" Term (1959) – Arthur Samuel first used this term to describe how computers could learn beyond their original programming

The 1950s marked the birth of artificial intelligence

These developments reflected strong optimism: pioneers believed that within a few decades, machines could achieve human-like intelligence.

1960s: Early Progress

Entering the 1960s, AI continued to develop with many notable projects and inventions. AI laboratories were established at prestigious universities (MIT, Stanford, Carnegie Mellon), attracting research interest and funding. Computers became more powerful, allowing experimentation with more complex AI ideas than in the previous decade.

Breakthrough Innovations

1960s witnessed significant early AI progress

1970s: Challenges and the First "AI Winter"

In the 1970s, AI faced real-world challenges: many high expectations from the previous decade were unmet due to limitations in computing power, data, and scientific understanding. As a result, confidence and funding for AI sharply declined by the mid-1970s – a period later called the first "AI winter".

Bright Spots Despite Difficulties

The first AI winter brought challenges and lessons

This period reminded researchers that artificial intelligence is far more complex than initially thought, requiring fundamentally new approaches beyond simple reasoning models.

1980s: Expert Systems – Rise and Decline

By the early 1980s, AI entered a renaissance period driven by the commercial success of expert systems and renewed investment interest from governments and businesses. Computers became more powerful, and the community believed that AI ideas could gradually be realized in narrow domains.

Major Government Initiatives

Neural Networks Revival

Amid the expert systems boom, the field of artificial neural networks quietly revived. In 1986, researcher Geoffrey Hinton and colleagues published the Backpropagation algorithm – an effective method for training multi-layer neural networks.

Expert systems era brought both success and lessons

1990s: AI Returns to Practicality

After the late 1980s AI winter, confidence in AI gradually recovered in the 1990s thanks to a series of practical advances. Instead of focusing on ambitious strong AI, researchers concentrated on weak AI – applying AI techniques to specific problems where they began to show impressive results.

Major Achievements Across Domains

The Rise of Data-Driven AI

The late 1990s saw the Internet boom, generating massive digital data. Techniques like data mining and machine learning algorithms were used to:

• Analyze web data and optimize search engines
• Personalize content recommendations
• Filter email spam automatically
• Provide product recommendations in e-commerce
• Improve software performance by learning from user data

AI quietly entered everyday life in the 1990s

The 1990s was a period when AI quietly but steadily entered everyday life. Instead of grand claims of human-like intelligence, developers focused on solving specialized problems, laying important foundations in data and algorithms for the explosive growth in the next decade.

2000s: Machine Learning and the Big Data Era

Entering the 21st century, AI transformed dramatically thanks to the Internet and the big data era. The 2000s witnessed the explosion of personal computers, the Internet, and sensor devices, generating enormous amounts of data. Machine learning became the main tool to exploit this "data goldmine."

Data is the new oil – the more data available, the more accurate AI algorithms could learn.

— Popular tech industry saying, 2000s

ImageNet: The Foundation for Deep Learning

Notable Application Milestones

AI Enters Business

Big data and machine learning transformed AI in the 2000s

The 2000s laid the groundwork for AI's explosive growth. Big data, powerful hardware, and improved algorithms were ready, just waiting for the right moment to ignite a new AI revolution.

2010s: The Deep Learning Revolution

If there is one period when AI truly "took off", it was the 2010s. Building on the data and hardware foundations of the previous decade, artificial intelligence entered the deep learning era – multi-layer neural network models achieved breakthrough results, breaking all records across a wide range of AI tasks.

The AlexNet Revolution

Deep Learning Spreads Across Domains

AlphaGo: AI Surpasses Human Intuition

The Transformer Revolution (2017)

In 2017, a breakthrough in natural language processing emerged: the Transformer architecture. Google researchers published the paper "Attention Is All You Need", proposing a self-attention mechanism that revolutionized language AI.

The Rise of Generative AI

AI in Everyday Life

• Smartphone cameras with automatic face recognition
• Virtual assistants in smart speakers (Alexa, Google Home)
• Content recommendations on social media
• Advanced self-driving car systems
• Real-time language translation
• Personalized learning platforms

Deep learning revolutionized AI in the 2010s

AI is the new electricity – a foundational technology transforming every industry.

— Andrew Ng, AI Pioneer

2020s: The Generative AI Boom and New Trends

In just the first few years of the 2020s, AI has exploded at an unprecedented pace, mainly driven by the rise of generative AI and large language models (LLMs). These systems have enabled AI to reach hundreds of millions of users directly, sparking a wave of creative applications and widespread social discussions.

The Era of Large Language Models

Generative AI Beyond Text

Ethical and Legal Challenges

AI Across Industries

Generative AI boom defines the 2020s

Conclusion: AI's Journey and Future Prospects

From the 1950s to today, the history of AI development has been an astonishing journey – full of ambition, disappointment, and resurgence. From the small 1956 Dartmouth workshop that laid the foundation, AI has twice fallen into "AI winters" due to overhyped expectations, but each time rebounded stronger thanks to scientific and technological breakthroughs.

Future Prospects

The next chapter of AI promises to be extremely exciting. With current momentum, we can expect AI to penetrate even deeper into life:

Key Lessons from AI History

• Avoid overhype – Set realistic expectations based on current capabilities
• Learn from failures – AI winters taught valuable lessons about sustainable development
• Prioritize safety – Develop AI with control, transparency, and ethical guidelines
• Focus on practical applications – Narrow AI solving specific problems delivers real value
• Embrace collaboration – Progress requires cooperation between researchers, industry, and policymakers
• Maintain human oversight – AI should augment, not replace, human judgment and values

Artificial intelligence has been, is, and will continue to be a testament to our ability to transcend limits. From primitive calculators that only computed, humans have taught machines to play games, drive cars, recognize the world, and even create art.

— Reflection on AI's Journey

AI today is like electricity or the Internet – a foundational technology infrastructure. Many experts are optimistic that AI will continue delivering leaps in productivity and quality of life if developed and managed responsibly. The future of AI is not predetermined – it will be shaped by the choices we make today about how to develop, deploy, and govern this transformative technology.