The Genesis of Language AI: From Turing to Transformers (1950-2017)
Kishore Gunnam
Developer & Writer
Complete Guide to LLMs · Part 1 of 8
How did we get from Turing's 1950 thought experiment to ChatGPT? Here's the story.
That's exactly what we're exploring in this 8-part series on Large Language Models.
If you're a beginner, here’s the promise: by the end of this series, you’ll be able to explain (in normal words) why chatbots can write, why they hallucinate, why context windows matter, and what changed in 2017 that kicked off the modern AI boom.
The Timeline
Turing Test Proposed
Alan Turing asks 'Can machines think?'
ELIZA Chatbot
MIT creates the first conversational program
Statistical Revolution
Counting patterns beats hand-written rules
Word2Vec
Words become numbers that capture meaning
Transformers
The architecture behind every modern AI
1950: Can Machines Think?
It's 1950. Computers are the size of rooms. And Alan Turing asks:
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Turing proposed a test: if you're texting with someone and can't tell whether it's a human or computer - does it matter if the machine is "really" thinking?
When ChatGPT launched in November 2022, many argued we'd finally passed it. 72 years later.
The 1960s: ELIZA
In 1966, MIT built ELIZA - a program that pretended to be a therapist:
Talking to ELIZA
Pattern matching: ELIZA finds keywords and responds with templates
ELIZA understood nothing. It just matched patterns: find "mother" → respond about family.
But people genuinely confided in it. We want to believe machines understand us.
Why Rules Failed
Through the 1970s-80s, researchers tried encoding grammar rules. It didn't work:
Language is infinite creativity. You can't write rules for every expression.
Here’s the real reason this matters: once you accept that rules won’t cover real language, you need systems that learn from examples. That one decision (learn from data) is the throughline that leads to Word2Vec, transformers, and today’s chatbots.
The 1990s: Just Count Things
The breakthrough: instead of rules, count patterns in real text.
After reading millions of sentences, patterns emerge:
- After "I want to eat..." → "pizza" 30%, "lunch" 20%
- After "the cat sat on the..." → "mat" 40%
It's pattern recognition. And it works.
Deep dive: why 'counting' beat rules(optional)
Rules fail because language is messy. People shorten words, break grammar, invent slang, and rely on context. Statistical methods don’t need perfect grammar—they just need enough examples to learn what usually follows what. That shift (from “encode language” → “learn from data”) is basically the root of everything that comes later.
2013: Words Become Numbers
Google's Tomas Mikolov published Word2Vec: represent each word as a list of numbers. Words in similar contexts get similar numbers.
The magical part? You can do math with words:
Word Vector Arithmetic
Word Vector Arithmetic
Nobody taught the model these relationships. It learned them from reading text.
Word2Vec doesn’t store a dictionary definition. It stores a vector that’s useful for predicting context. If two words appear in similar contexts (“king” and “queen”), the vectors end up close together. The “king - man + woman = queen” trick is a fun demo of the geometry, not the whole point.
The important beginner takeaway: this is the moment “words become numbers” stops being a metaphor. Once you can represent language as vectors, you can build bigger systems on top of those vectors.
2014-2016: The Memory Problem
Word2Vec handled single words. But language is about sequences.
"The bank was steep" - river bank.
"The bank was closed" - financial bank.
Context is everything.
Recurrent Neural Networks (RNNs) tried to solve this:
RNN Forward Pass
Click play or wait for the animation to start
RNN processes words one at a time, updating a hidden state that carries context forward
Problem: RNNs had terrible long-term memory. By word 50, word 1 is forgotten. And they're slow.
So the world needed a model that could:
- Keep long-range context without forgetting
- Train fast on GPUs
- Scale up with more data and compute
June 2017: Everything Changes
Eight researchers at Google published:
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They introduced the Transformer. Every AI you use today - ChatGPT, Claude, Gemini - is built on this paper.
What Made Transformers Special
The key: self-attention. Instead of reading words sequentially, look at ALL words at once:
Attention: what does "it" look at?
Self-attention lets each word decide which other words are relevant to its meaning
When processing "it," the model calculates which words matter. "cat" is highly relevant. "The" is not.
We dive deep into how transformers work in Part 2.
The Explosion
GPT-1
117M parameters. First GPT.
GPT-2
'Too dangerous to release'
GPT-3
175B parameters. World notices.
ChatGPT
100M users in 2 months.
GPT-5
PhD-level intelligence
The complete GPT history is covered in Part 3.
Key Takeaways
Rules don't scale. You can't hand-code human language.
Statistics work. Count patterns instead of defining rules.
Words can be numbers. Word2Vec proved meaning is mathematical.
Attention changed everything. Transformers process all words at once.
Common beginner mistakes
- Thinking this history is trivia: it explains why modern models behave the way they do.
- Thinking “language understanding” is the same as fluent text: ELIZA was fluent-ish; it still didn’t understand.
- Thinking the transformer was a small improvement: it was the scaling unlock.
What's Next?
In Part 2, we crack open the transformer. Self-attention, Query-Key-Value, the architecture that powers everything.
Complete Guide to LLMs · Part 1 of 8