How Did We Get Here? The History of AI

10 min readMar 21, 2024

Timeline and Milestone in AI development — The History of Artificial Intelligence

Defining Artificial Intelligence

What do a labyrinth, a water clock, and AI have in common? They all show how humans have tried to find their way through complex systems for centuries. However, Artificial Intelligence can perform this task without taking a break unless you exceed your daily limit on some AI chatbots like Microsoft Bing Chat and Claude 2.1.

The earlier question references the Theseus, an early AI system by Claude Shannon that could navigate a labyrinth, and the ancient invention of a self-regulating water clock, which is considered one of the first automatic systems. Both are milestones in the journey towards modern AI.

In 1955, Dartmouth Professor John McCarthy coined the term “artificial intelligence.” He created the term as part of an academic grant to fund the first AI workshop. This workshop aimed to determine if early computers could behave in ways that everyday people would identify as intelligent.

In the early 1950s, single computers were taking up entire floors, and even with their enormous size, they had much less processing power than modern smartphones. Hence, they didn’t make much progress in creating a fully intelligent artificial entity.

Single computers taking up entire floors in 1950s

What they did, however, was create a new term that ignited everyone’s imagination. The term “artificial intelligence” inspired a new generation of journalists, writers, academics, and computer scientists. It opened the door to large grants that early computer scientists used to build out a whole new area of research.

In fact, if Professor McCarthy had come up with a different name, this 1955 workshop would have faded into obscurity. As a fledgling technology, artificial intelligence is defined as any system that exhibits behaviour that could be interpreted as human intelligence. But this simple definition cuts to the very heart of one of the significant challenges in AI and human relationships.

Computers vs. Humans: The Main Challenge in AI

Human intelligence is the ability of humans to process complex information, learn from experience, adapt to new situations, manipulate their environment, and handle abstract concepts. It is a multifaceted concept encompassing various cognitive abilities, including problem-solving, creativity, innovation, communication, and memory recall capabilities.

Human intelligence allows us to understand the world and make informed decisions based on our experiences and knowledge.

Artificial Intelligence involves building intelligent machines that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and perception.

AI is based on human insights and strives to replicate human-like behaviour in machines.

Human intelligence comes in many different forms. For example, great artists may not be great mathematicians, and vice versa. Therefore, there is no one standard for human intelligence, which makes it challenging to identify whether a computer is intelligent.

Human Intelligence versus Artificial Intelligence — Comparison of a human brain and a computer chip

Computers are highly skilled at many tasks, often surpassing human capability. For example, computers have been able to beat humans in chess for decades. IBM Watson beat some of the best champions in Jeopardy, and Google’s DeepMind has beaten the best players in the 2,500-year-old Chinese game Go. However, while computers can follow the rules and identify patterns exceptionally well, they need help understanding the game’s purpose or why they play it.

The main challenge is that artificial intelligence vs human intelligence operate differently. While computers are excellent at identifying and matching different patterns, they need help understanding context, abstract concepts, and nuanced meanings.

Furthermore, humans use intuition, creativity, and experience to solve problems, while AI uses algorithms and computational power. Humans also have emotional intelligence and self-awareness, which AI lacks.

Although AI has made significant strides in replicating certain aspects of human intelligence, there are still many areas where human cognition and understanding surpass current AI capabilities. Therefore, understanding these differences is crucial to developing and applying AI effectively while recognizing its limitations.

The History of AI

The origins of artificial intelligence were a mix of ambition and self-discipline. Some scientists were quick to overpromise what could be done with early machines. At the same time, you could see the potential in these machines to solve complex problems.

First, here’s a quick breakdown of the timeline of AI development:

The Foundation Era (1940s — 1950s)

1930s: Alan Turing lays the computational groundwork for AI.
1943: Warren McCulloch and Walter Pitts design the first artificial neurons.
1950: Alan Turing introduces the Turing Test.
1950: The creation of the Logic Theorist program by Allen Newell and Herbert A. Simon showcases machines’ ability to emulate human problem-solving skills.
1950: Claude Shannon built Theseus, a maze-navigating mouse.
1956: The term “Artificial Intelligence” is coined at the Dartmouth Conference by Professor John McCarthy

The AI Winter (1960s — 1980s)

1965: Joseph Weizenbaum creates ELIZA, an early natural language processing computer program.
1972: Dendral, the first expert system, is developed.
1980: The first National Conference on Artificial Intelligence is held.
1986: The concept of backpropagation in neural networks gains popularity.

The Resurgence and Advancement ( 1990s — Present)

1997: IBM’s Deep Blue defeats world chess champion Garry Kasparov.
1998: Furby, an early domestic robot designed by Tiger Electronics.
2002: The Roomba, an autonomous robotic vacuum cleaner, became one of the first commercially successful robots designed for a domestic environment.
2010: Apple’s Siri was one of the first voice-activated personal assistants to be integrated into smartphones.
2011: IBM’s Watson wins Jeopardy! against former champions.
2016: Google’s DeepMind AI, AlphaGo, defeats a Go professional player (Lee Sedol).
2020s: AI systems begin outperforming humans in various domains.
2022: ChatGPT (aka Chat Generative Pre-trained Transformer) first launch. Based on a large language model (LLM).
2023: The release of GPT-4 by OpenAI, a state-of-the-art language model known for its advanced text generation and comprehension capabilities.
2023: Meta’s introduction of the LlaMa family of large language models, contributing to the open-source AI landscape.
2024: The launch of PyTorch 2.2.0 marks a significant update to the popular machine learning library that supports AI research and development.

The General Problem Solver and the Importance of Symbols in AI

In 1956, you had one of the first attempts to create a machine with general intelligence. Allen Newell and Herbert Simon created a computer program called the General Problem Solver. This program was designed to solve any problem presented as mathematical formulas.

One of the key parts of the General Problem Solver was what Newell and Simon called the physical symbol system hypothesis. Their paper argued that symbols were the key to general intelligence. You would have an intelligent machine if you could get a program to connect enough of these symbols.

Symbols are a huge part of how you interact with the world. When you see a stop sign, you know to look for traffic. When you see the letter a, you know the word will make a certain sound. When you see a sandwich, you might think of eating.

Newell and Simon argued that if a machine were trained to understand these symbols, they could behave more like humans. They thought a key part of human reasoning was simply connecting these different symbols. In one sense, our language, ideas, and concepts were just broad groupings of interconnected symbols, but not everyone bought into this idea.

The Chinese Room Argument and Symbolic Connections

In 1980, philosopher John Searle argued that you could never call these symbolic connections real intelligence. To explain, he created something called the Chinese room argument. In this experiment, you should imagine yourself in a windowless room with one narrow slot on the door, almost like a mail slot.

You can use the slot to communicate with the outside world. In the room, you have a book on a desk and a bunch of Chinese symbols on the floor. This book is filled with long lists of matching patterns. It says that if you see this sequence of Chinese symbols, then you should respond with that sequence of Chinese symbols.

To start, John Searle imagines someone who speaks fluent Chinese, writes a note, and shoves it through the slot. You pick up the note, but you don’t speak a word of Chinese, so you really have no idea what it says. Instead, you simply go through the tedious process of looking through your book and matching the sequence of Chinese letters.

Once you find the matching sequence of letters, you look at how the book tells you how to respond. Then, you tape together the response from the Chinese symbols and push your note through the same slot on the door. A native Chinese speaker on the other side might believe that they’re having a conversation.

In fact, they may even assume that the person in the room is intelligent, but Searle argues that this is far from intelligence since the person in the room can’t speak Chinese and doesn’t have any idea what you’re talking about.

It was just simply matching patterns. You can try a similar experiment with your smartphone. If you ask Siri or Cortana how they’re feeling, they’ll give you a response. They’ll usually say they feel fine, but that doesn’t mean they really feel fine. In reality, they also don’t really know what you’re asking; they’re just matching your question to a pre-programmed response, just like the person in the Chinese room.

So Searle argues that simply matching symbols is not a true path to intelligence, that a computer is acting just like the person in the room. They don’t understand the meaning or the content. They’re just matching symbols from a long list of instructions. You can also see how the book of Chinese responses might become larger and larger as you try to create more matching statements. This is called a combinatorial explosion.

The Limitations of Symbolic Connections and Combinatorial Explosions

There are so many different combinations that matching becomes overwhelming. Think about all the things that people might ask in Chinese, or imagine all the different responses Siri must be prepared to answer. Even with these challenges, physical symbol systems were still the cornerstone of AI for 25 years, yet creating all these matching connections took up too much time in the end. It was also difficult for these machines to match all the different patterns without running into combinatorial explosions.

AI in Organizations: Who Will Benefit the Most?

Games are an ideal environment for computers as they have established rules and limited possibilities. For instance, IBM’s Watson, during its appearance on Jeopardy, utilized natural language processing to comprehend questions and pattern matching to look for potential answers in its database.

Chess program can easily out beat even the most skilled human players. Computer scientists have developed the first versions of chess programs just a few years after the first AI workshop. Computers could thrive in a world with fixed rules and limited possibilities even in those early days.

The first versions of chess programs computer scientists developed

They were capable of matching the moves of players with plausible countermoves. A computer could play out thousands of scenarios before its opponent made a move. That’s why artificial intelligence is most impressive when computers operate within their area of expertise.

This implies that organizations that benefit most from AI will work within a well-defined space with established rules. It’s no surprise that companies like Google are wholeheartedly embracing artificial intelligence since their entire business model is based on pattern matching. They match your questions against a massive database of possible answers. AI has improved their business operations by reducing search times and enhancing language translation accuracy.

Since we are humans, we don’t think about specific tasks like a computer. If you are considering whether AI will impact your organization, then think about the tasks computers are good at. For instance, does your work involve a lot of pattern matching? Does it have established rules and limited possibilities? Such work will be the first to benefit from artificial intelligence.

Read: AI and Automation: A Threat or an Opportunity for Nigeria? | by Debra Lawal | Medium

Final Thoughts for Now!

Understanding the origins of AI is a lengthy task, so I have decided to write a two-part article series discussing the topic. In the second part, I will delve into the earliest challenges in creating a fully intelligent artificial entity, types of AI, and examples of their applications in various fields.

It is important to remember that understanding AI’s history is crucial before implementing it in daily tasks or businesses. Knowing how AI was created, its components, and what it can do so far helps us understand its applications. Therefore, I suggest holding onto this thought until the next part or rereading the article from a similar perspective to gain a better understanding.

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