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Building an AI Learning Program in Scratch

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AIEnthusiast_Dev

Posted on January 23, 2024 • Advanced

🤖 How to create an AI learning program in Scratch?

Hey everyone! I’ve been researching AI and machine learning concepts and I’m fascinated by the idea of creating a learning program in Scratch. I understand the basic concepts but I’m not sure how to implement them given Scratch’s limitations.

I’m particularly interested in:

  • Simple neural network implementations
  • Pattern recognition systems
  • Decision tree algorithms
  • Working around the 5MB project size limit
  • Efficient data storage techniques

I know it’s challenging, but I’d love to explore what’s possible! Any guidance on where to start would be amazing! 🧠

ML

MachineLearning_Pro

Replied 4 hours later • ⭐ Best Answer

Excellent question @AIEnthusiast_Dev! While Scratch has limitations, you can definitely create impressive AI-like programs. Here’s a comprehensive guide to get you started:

🧠 AI Learning Program Architecture

Here’s how a simple AI learning system works in Scratch:

flowchart TD A[📊 Input Data] --> B[🔍 Feature Extraction] B --> C[🧮 Processing Layer] C --> D{Learning Algorithm} D -->|Neural Network| E[⚡ Weighted Connections] D -->|Decision Tree| F[🌳 Rule-Based Logic] D -->|Pattern Recognition| G[🎯 Template Matching] E --> H[📈 Training Phase] F --> H G --> H H --> I[💾 Store Learned Data] I --> J[🎮 Prediction/Output] J --> K{Feedback?} K -->|Yes| L[📝 Update Weights] K -->|No| M[✅ Final Result] L --> H style A fill:#e1f5fe style D fill:#f3e5f5 style H fill:#e8f5e8 style I fill:#fff3e0 style M fill:#fce4ec

🔧 Method 1: Simple Neural Network

Let’s start with a basic perceptron that can learn simple patterns:

    // Initialize neural network
when flag clicked
// Create weight variables for a simple 2-input perceptron
set [weight1 v] to (pick random [-1] to [1])
set [weight2 v] to (pick random [-1] to [1])
set [bias v] to (pick random [-1] to [1])
set [learning rate v] to [0.1]

// Training data (XOR problem simplified)
set [training data v] to [0,0,0|0,1,1|1,0,1|1,1,0]
set [current example v] to [1]
  
    // Training function
define train network
set [input1 v] to (item [1] of (split (item (current example) of (split (training data) by [|])) by [,]))
set [input2 v] to (item [2] of (split (item (current example) of (split (training data) by [|])) by [,]))
set [expected output v] to (item [3] of (split (item (current example) of (split (training data) by [|])) by [,]))

// Forward pass
set [weighted sum v] to (((input1) * (weight1)) + ((input2) * (weight2)) + (bias))
if <(weighted sum) > [0]> then
set [actual output v] to [1]
else
set [actual output v] to [0]
end

// Calculate error and update weights
set [error v] to ((expected output) - (actual output))
change [weight1 v] by ((learning rate) * (error) * (input1))
change [weight2 v] by ((learning rate) * (error) * (input2))
change [bias v] by ((learning rate) * (error))

🌳 Method 2: Decision Tree Learning

Create a decision tree that learns from examples:

    // Decision tree implementation
when flag clicked
// Initialize decision tree structure
set [tree rules v] to []
set [training examples v] to [sunny,hot,high,weak,no|sunny,hot,high,strong,no|overcast,hot,high,weak,yes|rainy,mild,high,weak,yes]

define build decision tree
// Simplified decision tree learning
repeat (length of (split (training examples) by [|]))
set [example v] to (item (counter) of (split (training examples) by [|]))
set [features v] to (split (example) by [,])

// Extract features
set [weather v] to (item [1] of (features))
set [temperature v] to (item [2] of (features))
set [humidity v] to (item [3] of (features))
set [wind v] to (item [4] of (features))
set [play v] to (item [5] of (features))

// Build simple rules
if <(weather) = [overcast]> then
add [overcast->yes] to [tree rules v]
end
if <(humidity) = [high]> then
if <(wind) = [strong]> then
add [high_humidity+strong_wind->no] to [tree rules v]
end
end
end

🎯 Method 3: Pattern Recognition

Create a system that recognizes and learns visual patterns:

    // Pattern recognition system
when flag clicked
// Initialize pattern storage
set [learned patterns v] to []
set [pattern confidence v] to []

define learn pattern (pattern data)
// Store pattern as costume or list data
if <(pattern data) contains [known pattern]> then
// Increase confidence
set [confidence v] to (item (position of (pattern data) in [learned patterns v]) of [pattern confidence v])
replace item (position of (pattern data) in [learned patterns v]) of [pattern confidence v] with ((confidence) + [1])
else
// Add new pattern
add (pattern data) to [learned patterns v]
add [1] to [pattern confidence v]
end

define recognize pattern (input pattern)
set [best match v] to []
set [highest similarity v] to [0]

repeat (length of [learned patterns v])
set [similarity v] to (calculate similarity (input pattern) (item (counter) of [learned patterns v]))
if <(similarity) > (highest similarity)> then
set [highest similarity v] to (similarity)
set [best match v] to (item (counter) of [learned patterns v])
end
end

if <(highest similarity) > [0.7]> then
say (join [Recognized: ] (best match)) for [2] seconds
else
say [Unknown pattern - learning...] for [2] seconds
learn pattern (input pattern)
end

💾 Method 4: Efficient Data Storage

Work around Scratch’s limitations with smart data storage:

    // Data compression and storage
define compress data (raw data)
// Use costume-based storage for large datasets
set [compressed v] to []
repeat (length of (raw data))
set [value v] to (item (counter) of (raw data))
// Convert to shorter representation
if <(value) = [0]> then
set [compressed v] to (join (compressed) [.])
else
set [compressed v] to (join (compressed) [#])
end
end

// Store in costume names or backdrop switching
switch costume to (compressed)

define load compressed data
// Retrieve data from costume
set [costume data v] to (costume [name v])
set [decompressed v] to []
repeat (length of (costume data))
set [char v] to (letter (counter) of (costume data))
if <(char) = [.]> then
add [0] to [decompressed v]
else
add [1] to [decompressed v]
end
end

🚀 Method 5: Advanced Techniques

For more sophisticated AI behavior:

    // Genetic algorithm for optimization
when flag clicked
set [population size v] to [20]
set [generation v] to [1]

define create population
delete all of [population v]
repeat (population size)
// Create random individual (weights for neural network)
set [individual v] to []
repeat [5] // 5 weights
add (pick random [-10] to [10]) to [individual v]
end
add (individual) to [population v]
end

define evolve population
// Evaluate fitness of each individual
repeat (length of [population v])
set [fitness v] to (evaluate fitness (item (counter) of [population v]))
replace item (counter) of [fitness scores v] with (fitness)
end

// Select best individuals and create new generation
set [new population v] to []
repeat (population size)
set [parent1 v] to (select best individual)
set [parent2 v] to (select best individual)
set [child v] to (crossover (parent1) (parent2))
set [child v] to (mutate (child))
add (child) to [new population v]
end

set [population v] to (new population)
change [generation v] by [1]

⚡ Optimization Tips

Make your AI programs run efficiently:

  • Batch Processing: Process multiple examples at once
  • Simplified Models: Use smaller networks and fewer parameters
  • Smart Caching: Store frequently used calculations
  • Progressive Learning: Start simple and add complexity
    // Efficient training loop
when flag clicked
set [batch size v] to [5]
set [epoch v] to [1]

repeat [100] // 100 epochs
set [batch start v] to [1]
repeat until <(batch start) > (length of [training data v])>
// Process batch
repeat (batch size)
if <((batch start) + (counter)) ≤ (length of [training data v])> then
train on example ((batch start) + (counter))
end
end
change [batch start v] by (batch size)
end

// Show progress
if <((epoch) mod [10]) = [0]> then
say (join [Epoch: ] (epoch)) for [1] seconds
end
change [epoch v] by [1]
end

Remember: Start with simple concepts and gradually build complexity. AI in Scratch is about understanding the principles rather than creating production-ready models! 🎓

DS

DataScientist_Alex

Replied 2 hours later

@MachineLearning_Pro This is fantastic! I’d like to add that you can also use costume-based storage for neural network weights:

    // Store weights in costume pixel data
define save weights to costume
set [weight string v] to []
repeat (length of [weights v])
set [weight string v] to (join (weight string) (join (item (counter) of [weights v]) [,]))
end
// Use the weight string as costume name or encode in pixels
switch costume to (weight string)

This technique can store much more data than variables alone! 💾

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AIEnthusiast_Dev

Replied 1 hour later

@MachineLearning_Pro @DataScientist_Alex This is incredible! Thank you both! 🤯

I successfully implemented a simple perceptron that can learn basic patterns. The costume storage technique is genius! One question - how can I visualize the learning process to see how the AI improves over time?

VB

Vibelf_Community

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