One can say the history of artificial intelligence goes way back to the 17th century. It’s when René Descartes thought that we can add a rational system to all our thoughts. Or maybe even before, with the Egyptians and Greeks, and many other cultures, relying on a humanoid-like entity.
Its inception can also be traced in modern classics, where many literary experts believe that George Orwell was the first to explicitly talk about the perils of machines having a creative say.
Then there’s also Mary Shelly, who laid the foundation of Science fiction and ignited the spirit of curiosity about the extent of human possibility with things that aren’t human.
We wouldn’t be wrong if we say humanity has been fascinated by the idea of replicating our intelligence: sometimes driven by innovation, and other times, the darker themes reflected in fiction.
The thing is, we have dedicated close to seven or eight decades of trials and errors, failures, repercussions, blood, and sweat to ensure a setup like ChatGPT can do some great things in seconds.
This blog traces all that effort in a systematic timeline to help you understand how far we have come and what possibility lies ahead.
The Birth of AI: Alan Turing (1940-1950)
Interest in artificial intelligence peaked with the progress that was being made in neurology. The concept that the brain is an electronic network prompted scientists, along with other experts in the 1940s and 50s, to set the foundation for AI research.
It’s during that time that Alan Turing explored the possibility of “machine intelligence” in theory. He will be forever immortalized in the history of AI, as it is because of him that artificial intelligence evolved and became a recognized academic discipline in 1956.
It was also during this time that he published his landmark paper Computing Machinery and Intelligence and convinced the world that there is a possibility of machines that can think.
💡Fun Fact: The Turing Test, created by Alan Turing in 1950, is a simple way to check if a machine can mimic human conversation. If you chat with a computer and can't tell it's not a person, then it should be declared that the machine is thinking.
Hebbian Theory (1942 to 1947)
Not many people know this, but it's a psychologist from Canada who not only laid the foundation of modern neuroscience but also of machine learning. Doctor Donald Hebb worked on his groundbreaking work, Organization of Behavior, during 1942-45, but it wasn't published until 1946.
World War II delayed his pursuit and also led to a lot of his contemporaries already talking about the ideas he presented as groundbreaking. However, now that we look back, we know it’s because of Alen Turing and Donald Hebb that ChatGPT knows how to write that work email or SaaS blog.
Quick Overview of 1950-1955
Before we look into significant developments in AI, it is necessary to have a quick overview of the years that helped pave the way for it to become a reality.
1951: SNARC and Game AI
- Minsky and Dean Edmonds built the first neural net machine called the SNARC. Minsky would later become one of the pioneers and innovators of AI.
- Christopher Strachey and Dietrich Prinz developed early game-playing programs for checkers and chess using the Ferranti Mark 1. These were some of the first AI programs ever written.
1955: Logic Theorist
In 1955, Allen Newell and Herbert A. Simon developed a program called the Logic Theorist. The first program to mimic human problem-solving using symbolic reasoning. It solved math theorems and laid the groundwork for AI’s cognitive revolution. In short, this is the reason ChatGPT can “understand” you.
Dartmouth Workshop (1956)
This is where it all began, when John McCarthy first introduced the term Artificial Intelligence in the year 1956.
It was in the Dartmouth workshop that AI became an academic discipline, when scientists Marvin Minsky and John McCarthy were joined by senior scientists Claude Shannon and Nathan Rochester of IBM.
The purpose of the workshop was simple: they intended to understand if a machine can replicate the human mind when it comes to learning, solving problems, or making decisions.
Hence, it is also one of the many reasons why Dartmouth is significantly considered the birthplace of AI, where it not only got its name but also its mission, its first major success, and the key players.
| 1956 was also a big year for Psychology, and we started asking relevant questions about how the mind thinks. For AI, it meant scientists started modeling machines after human thought processes, not just behavior |
The Golden Years (1956–1974)
We struck gold in natural language processing history during this period. AI started showing signs of huge potential when researchers achieved great success with it solving problems, applying logic, and even engaging in human-like conversations.
This brought in government funding with DARPA, then known as "ARPA" entering the picture along with Artificial Intelligence laboratories being set up in the 1950s and early 1960s.
Natural Language: It became an important goal of the AI research to enable the machines to communicate in English. An early success of this attempt was Daniel Bobrow's program STUDENT.
- STUDENT: Written in the year 1964 for his PhD, it was designed to read and solve word problems found in School Algebra books.
- ELIZA: Before Siri and Alexa, we had ELIZA, who is regarded as the first chatbot known to mankind. Developed by Joseph Weizenbaum, it had a reputation for confusing people with its human-like responses.
Micro World: In the late 1960s, Marvin Minsky and Seymour Papert of MIT redirected the focus of AI on simple situations to follow the path led by other branches of science. This led to many innovative works in machines and in AI.
- SHRDLU: Developed by Terry Winograd, SHRDLU was a natural language understanding program that was designed to understand simple English sentences and execute them within a limited context.
The First AI Winter (1974–1980)
We have been forever obsessed with the concept of intelligent machines taking over the narrative. So, the growing optimism from the scientists unleashed a frenzy in the general public. But things soon came to a halt.
The lack of results prompted the government, especially in the US and UK, to start pulling back on the funding. Reports like the Lighthill Report in 1973 criticized AI's lack of practical results and talked about the wave of funding cuts. This period was popularly known as the first AI winter as interest declined, labs closed, and the spotlight began to fade.
But, we must also remember that many historians, including Thomas Haigh, have categorically established (2023) that there was no winter, as the funding cuts were limited only to a few libraries.
The Rise of Expert Systems (1980s)
As more work started to happen with AI, we eventually saw a boom in the early 1980s. With Japan's fifth-generation computer project and the U.S. Strategic Computing Initiative.
💡Fun Fact: The AI industry boomed from a few million dollars in 1980 to billions of dollars in 1988.
- The Beginning of the Knowledge Revolution: AI researchers, with time, realized that intelligence depends heavily on domain-specific knowledge. This led to the rise of knowledge engineering. Where projects like Cyc aimed at making machines learn one human fact at a time.
- Scientists started Doubting: This period also saw a need to explore alternatives like neural networks, embodied AI, and so on. You will see that Sutton and Barto’s work in the '80s and '90s made this practical, and it later powered famous AIs like TD-Gammon, AlphaGo, and AlphaZero.
- The AI Community Split: Symbolic AI and newer methods of neural networks led to a debate, and the AI community picked sides. The distinction between robotics-first and embodied cognition approaches started to become clear.
The Second AI Winter (Late 1980s–Early 1990s)
What captured the imagination of the world in the 1960s began to disappoint the big businesses who were expecting the world. This damage to AI’s reputation would last up to the 21st century and led to a fracture in the branches of AI.
We then see one group tackle logic and reasoning, others focus on learning from data, while newer groups explored robotics, perception, or how the body and environment shape intelligence. Therefore, it is safe to say that AI became a collection of competing and clashing concepts.
A New Dawn: Machine Learning and Statistical AI (1990s–2000s)
By the early 2000s, AI had achieved most of its oldest goals and was showing signs of being one of the promising investments in the history of humanity. We see a significant amount happening with AI, which is as follows:
- Statistical Methods: The machine learning history saw a new era with decision trees (ID3, C4.5) and Bayesian networks, which used probability to make sense of uncertain data.
The product recommendations that you see on your favorite website are one of the many examples of the Bayesian network.
- Data-Driven AI: This era also saw the rise of data-driven AI, where artificial intelligence achieved breakthroughs in speech recognition, image classification, and so on.
- Deep Blue: A Landmark in Game AI: A significant chapter in our history with Artificial Intelligence, where IBM’s Deep Blue defeated world chess champion Garry Kasparov, becoming the first computer to beat a champion.
Big Data & Deep Learning: The 2010s Revolution
The 2010s were once again an impactful time for AI, and it saw three powerful forces: faster GPUs, massive datasets (Big Data), and new neural network architectures changing history forever.
It also started the conversation on deep learning, where machines learn from large amounts of data using layers of networks designed to work like the human brain.
- AlexNet and the ImageNet Breakthrough (2012): AlexNet, a deep learning model, had learned to recognize images by studying millions of labeled pictures. It surpassed older methods in the ImageNet visual recognition challenge and proved to the world AI's accuracy in seeing things with precision.
- Google DeepMind’s AlphaGo’s shock: AlphaGo defeated a world champion at the ancient game of Go, something no one thought AI could do. It taught itself by playing millions of games and gave rise to the possibility that machines can, in fact, master complex strategy.
- The Rise of NLP and Transformers: Transformers like BERT helped machines understand language by looking at words in full context, not one by one. This made AI much better at translating, summarizing, and chatting. This is where AI started to sound like a human.
The Age of Generative AI: 2020s and Beyond
The 2020s will be forever remembered for two things: the fear and panic of the pandemic and generative AI changing the content scene forever. It brought in a new wave of AI that doesn’t just analyze data but can also create.
Models like PT-3, GPT-4, DALL·E, and ChatGPT showed the world that AI is no longer limited to just analyzing data. But can now write essays, generate images, draft code, and even hold conversations that feel human.
From writing blog posts and ad copy to designing logos and automating customer support, generative AI is transforming the way we create and work. It has completely reshaped our perception of AI—from a futuristic tool to an everyday assistant used by businesses, marketers, students, and creators worldwide.
Ethical Debates
But all this development presently comes with a price. We suffer from a moral dilemma where we have made a gradual shift from figuring out what AI can do to realising how much it should.
As these tools become powerful (and sometimes too convincing), concerns about plagiarism, misinformation, bias, and job displacement have grown louder and sparked important conversations.
But we have figured out an ideal way to thrive in a situation like this. We are not rejecting the technology, but are learning to adapt and collaborate with it.
That’s what prompted us to create a tool like Humanize AI, which bridges the gap between raw AI output and authentic human expression. It does so by helping writers, marketers, and creators keep their voice intact. Moreover, making sure everyone gets fair access to this remarkable progress so far.
Conclusion: What the Past Taught Us About the Future of AI
Well, that’s all, folks, for now! A sneak peek into AI’s journey is nothing short of a human marvel. In a way, artificial intelligence mirrors our own evolution from doubtful existence to taking control of the narrative.
As we step into a future powered by collaboration between humans and machines, the real challenge isn’t whether AI can think. It’s more about how we think and plan to use it.
The tool that helps you generate a quick blog or write the best marketing copy is not only a scientific marvel. But decades of hard work by people who believed in the power of human innovation.
At this point, the baton is in your hands to make sure you take their legacy forward by using it responsibly and yet boldly.
So when someone reads a blog like this a hundred years from now, they won’t just see how far AI has come. They will also see how we chose to shape it.