Part 1: Understanding AIChapter 3 of 12

The Secret Behind ChatGPT - Transformers Explained

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The Evolution of ChatGPT and Transformers

Imagine you're reading this sentence: "The dog chased the cat because it was playful."

Your brain automatically knows "it" refers to the dog, not the cat. How? You paid attention to the right words. That's exactly what Transformers do - they pay attention to relationships between words.

👨‍🍳Transformers: The Master Chef Analogy

Old Way (RNN - Reading One Word at a Time)

Read: "First"

Remember: "First"

Read: "add"

Remember: "First add"

Read: "flour"

[...continues slowly...]

Problem: By the time you read "bake for 30 minutes", you might forget it was about chocolate cake!

New Way (Transformer - Seeing the Whole Recipe)

See entire recipe at once:

"First add flour then sugar then eggs then chocolate then mix then bake for 30 minutes"

Can instantly connect:

✓ "chocolate" → "cake"

✓ "30 minutes" → "bake"

✓ "eggs" → "mix"

Advantage: Perfect context, even in long texts!

The Restaurant Review Example

Let's see how Transformers understand context:

Review: "The food was cold but the service made up for it"

Step 1: Break into tokens (words)

[The] [food] [was] [cold] [but] [the] [service] [made] [up] [for] [it]

Step 2: Attention Scores (What words relate to what?)

→"cold" strongly connects to "food" (negative)

→"made up for" strongly connects to "service" (positive)

→"it" refers back to "cold food" (the problem)

→"but" signals contrast (bad thing → good thing)

Step 3: Understanding

• Overall sentiment: Mixed (bad food, good service)

• Recommendation: Probably yes (service compensated)

• Key insight: Service quality can override food issues

Multi-Head Attention: Looking at Everything from Different Angles

Attention Mechanism: How Transformers Understand Context

Imagine you're buying a used car. Different experts look for different things:

🔧

Mechanic

Checks engine, transmission, brakes

🎨

Body Shop

Looks for rust, dents, paint quality

🪑

Interior Designer

Evaluates seats, dashboard, comfort

💰

Accountant

Analyzes price, value, depreciation

Transformers use "multi-head attention" - like having 12-32 different experts looking at each sentence:

• Head 1: Grammar structure (subject-verb-object)
• Head 2: Sentiment (positive/negative)
• Head 3: Time references (past/present/future)
• Head 4: Entity relationships (who did what to whom)
• Heads 5-32: Various other patterns

Why Transformers Changed Everything

Before Transformers (2016 and earlier)

Translating: "I love you" to French

Step 1: Process "I"

Step 2: Process "love" (remembering "I")

Step 3: Process "you" (trying to remember "I love")

Result: "Je t'aime" (hopefully)

⏱️ Time: Slow, sequential

⚠️ Problem: Long sentences lose early context

After Transformers (2017 onwards)

Translating: "I love you" to French

All at once: See whole sentence, understand relationships instantly

Result: "Je t'aime" (accurate)

⚡ Time: Fast, parallel

✓ Advantage: Perfect context, even in long texts

The Birth of ChatGPT: Transformers + Scale

GPT-1 (2018)

117 million parameters

Decent at completing sentences

→

GPT-2 (2019)

1.5 billion parameters

Could write coherent paragraphs

→→

GPT-3 (2020)

175 billion parameters

Could write essays, code, stories

→→→

GPT-4 (2023)

~1 trillion parameters (estimated)

Can pass bar exams, write novels, debug code

→→→→

Visual Representation: The Attention Matrix

Imagine this grid where darker squares = stronger connections:

        The  cat  sat  on  the  mat  because  it  was  soft
The      ■    □    □   □   □    □     □      □   □    □
cat      □    ■    □   □   □    □     □      ■   □    □
sat      □    ■    ■   ■   □    □     □      □   □    □
on       □    □    ■   ■   ■    ■     □      □   □    □
the      □    □    □   □   ■    □     □      □   □    □
mat      □    □    □   ■   □    ■     □      □   ■    ■
because  □    □    □   □   □    □     ■      □   □    □
it       □    ■    □   □   □    ■     □      ■   □    □
was      □    □    □   □   □    ■     □      ■   ■    ■
soft     □    □    □   □   □    ■     □      □   ■    ■

■ = Strong connection
□ = Weak/no connection

Notice: "it" connects strongly to "cat" and "mat"
        "soft" connects strongly to "mat"

This is how Transformers maintain context - every word can attend to every other word simultaneously!

🎯

Try This: See Transformers in Action

Experiment 1: Context Understanding

1.Go to ChatGPT
2.Type: "The trophy didn't fit in the suitcase because it was too big."
3.Ask: "What was too big?"
4.Watch it correctly identify "the trophy" (not the suitcase)

Experiment 2: Long-Distance Relationships

1.Type a long sentence: "The scientist who discovered penicillin in 1928 while working at St. Mary's Hospital in London, which completely revolutionized medicine, was Alexander Fleming."
2.Ask: "Who worked at St. Mary's Hospital?"
3.Notice how it connects information across the entire sentence

This is the power of attention - maintaining context across any distance!

🎓 Key Takeaways

✓Attention is the key - Transformers understand relationships between all words simultaneously
✓Multi-head attention - Like having multiple experts analyzing text from different angles
✓Parallel processing - Unlike old sequential models, Transformers see everything at once
✓Scale matters - From GPT-1's 117M to GPT-4's 1T parameters, bigger brought huge improvements
✓Context preservation - Perfect memory of relationships, even in long texts

How AI Learns - Like Training a Puppy

NEXT CHAPTER

AI Models - From Small to Giant

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In Chapter 4, discover the differences between small and giant models, and which one is right for your needs!

Continue to Chapter 4