The Wild History of NLP: How AI Learned to Talk

Ever argued with a chatbot that just did not get it? Or seen a translation so bad it made you laugh? That is Natural Language Processing at work, a technology that lets computers ‘understand’ human language, but one that has been on a rollercoaster of breakthroughs and failures.

NLP is behind Google Search, Siri, chatbots, spam filters, and AI-generated textual content, but it has not always been smooth. From the Cold War codebreakers to AI-powered chatbots that convinced people they were sentient, NLP’s history is filled with genius ideas, embarrassing mistakes, and leaps forward that nobody saw coming.

So, how did we go from ancient philosophers dreaming about thinking machines to AI models writing essays? Let’s look into the fascinating evolution of NLP, and why it is still far from perfect.

What Exactly is NLP?

Before we get into the wild history, let’s break it down:

Natural Language Processing (NLP) is a branch of AI that helps computers read, interpret, and generate human language. It is what allows:

• Chatbots to (kind of) hold a conversation

• Google Search to figure out what you actually mean when you type gibberish

• Spam filters to separate your important emails from “You’ve Won a Free iPhone” scams

• Voice assistants like Siri and Alexa to (sometimes) understand what you are saying

But how does AI process language when it does not think like a human?

Instead of actually understanding words, NLP relies on patterns, probabilities, and linguistic rules to process text. It uses techniques like:

• Tokenisation – Breaking sentences into words or phrases

• Syntax and grammar parsing – Figuring out sentence structure

• Named entity recognition – Detecting names, locations, and organisations

• Sentiment analysis (albeit, simple) – Determining whether text is happy, angry, sad, neutral etc

Sounds smart, right? Well, NLP did not start out that way. Let’s rewind to the very beginning.

The Wild History of NLP: From Ancient Dreams to AI That Talks Back

1. The 1600s – Philosophers Dream of Thinking Machines

You might think NLP is a modern invention, but people have been obsessed with language automation for centuries.

In the 1600s, philosophers like René Descartes and Gottfried Wilhelm Leibniz believed that human thought could be broken down into structured logic. Leibniz even envisioned a "thinking machine" that could process symbols and generate truth.

Of course, there were no computers back then, just big ideas. But these early attempts at creating a universal language of logic were the foundation of NLP centuries later.

2. The 1940s – WWII Codebreaking and the First NLP Challenge

World War II pushed technology forward at a terrifying pace, and one of the biggest breakthroughs was machine-based codebreaking.

British mathematician Alan Turing, often called the father of AI, built the Bombe machine: a device that helped crack Nazi Germany’s Enigma code. While this was not NLP in the modern sense, it proved that machines could process patterns in language-like data.

Turing later posed one of the most famous AI questions: "Can machines think?" His ideas would influence AI and NLP for decades.

3. The 1950s & 60s – AI’s First Translation Disaster

During the Cold War, the US and the Soviet Union were desperate to translate each other’s languages instantly. The US poured funding into machine translation projects, hoping AI could turn Russian into English and vice versa.

The result? A disaster.

Early systems translated word-for-word, which led to hilarious mistakes. The most famous being:

"The spirit is willing, but the flesh is weak"

was translated into Russian and back as:

"The vodka is good, but the meat is rotten."

After years of frustration, researchers finally admitted defeat. In 1966, the US shut down its machine translation program, calling it a total failure. A lesson was learned. Understanding language is more than just swapping words.

4. The 1970s & 80s – The First Chatbot That Fooled People

By the 70s, AI researchers shifted focus from translation to human-like conversation.

In 1966, computer scientist Joseph Weizenbaum built ELIZA, the first-ever chatbot. It was programmed to act like a therapist, repeating back user input as a question:

User: "I feel sad today."

ELIZA: "Why do you feel sad today?"

ELIZA was a simple trick, but people fell for it. Some even formed emotional attachments, believing the chatbot truly understood them.

It was a glimpse into AI’s potential and how easy it is to mistake pattern recognition for real intelligence.

5. The 1990s & Early 2000s – NLP Gets Practical

As computers got faster, NLP shifted from experimental to essential. Some of the biggest breakthroughs included:

Spam filters – Detecting scam emails

Google Search – Figuring out what you actually meant when you typed “restauranr near me”

Speech recognition – Turning spoken words into text

One of the biggest moments? Google Translate (2001). It still made mistakes, but it was miles ahead of the Cold War translation blunders.

6. The 2010s – The AI Language Boom

Then came deep learning, and everything changed.

Instead of relying on fixed rules, AI began being trained on massive datasets, and coded for incremental learning. That led to:

Siri, Alexa, and Google Assistant – AI-powered voice assistants

Google Translate (Neural Networks) – Near-human accuracy

GPT-2 and GPT-3 – AI models that could write full articles, poems, and code

By 2020, NLP had evolved from simple rule-based processing to AI models that could generate entire conversations.

Where NLP is Headed Next

So, what is next for NLP?

Even more human-like AI conversations – Chatbots are getting smarter and more context-aware.

AI that understands tone and sarcasm – Future models may detect emotion in text.

Privacy-focused NLP – Leonata is pioneering systems that work without cloud servers and without data scraping …perhaps NLPs will follow?

NLP has come a long way from wartime codebreaking and mistranslated Russian vodka. Today, it powers everything from search engines to voice assistants.

The next challenge is keeping NLP ethical and private.

As AI gets more powerful, the question may no longer be Can machines understand us? but Who controls the AI that understands us?

What do you think? Is AI already getting too good at language, or are we just getting started?