🤖 AI Literacy Training

Bridging the Gap from Traditional ML to Generative AI

Instructor: Pravin | Interactive Learning Session

"If you comprehend 50-60% of this content, you'll be abreast with cutting-edge knowledge"

🎯 Quick Warm-up

How many AI tools do you use daily?

0-2

3-5

6+

Not Sure

🔍 Search: Google, Bing

📱 Social: Instagram filters, TikTok recommendations

🛒 Shopping: Amazon suggestions, price comparisons

🎵 Entertainment: Spotify playlists, Netflix recommendations

🏢 The AI Ecosystem

🌟 ARTIFICIAL INTELLIGENCE

🤖

MACHINE LEARNING

Statistical Models
(Linear, Trees, etc.)

🧠

DEEP LEARNING

Neural Networks
(CNNs, RNNs, Transformers)

✨

GENERATIVE AI

Creative Models
(GPT, DALL-E, etc.)

🚀 Key Insight: We "pole-vaulted" directly into Gen AI era!

📚 Real-World Examples

🤖 Traditional ML:
• Email spam detection
• Credit card fraud alerts
• Weather predictions

🧠 Deep Learning:
• Face recognition in photos
• Self-driving cars
• Medical image analysis

✨ Generative AI:
• ChatGPT conversations
• DALL-E image creation
• GitHub Copilot coding

Which category does "Siri understanding your voice" belong to?

Traditional ML

Deep Learning

Generative AI

🧠 What is Intelligence?

🎭

Creative

Steven Spielberg
Taylor Swift

AI: DALL-E, Midjourney

🏃

Kinetic

Usain Bolt
Athletes

AI: Robotics

🗣️

Linguistic

Polyglots
Translators

AI: ChatGPT, Claude

🧮

Mathematical

Mathematicians
Scientists

AI: Calculators, ML

🔄 Human vs. Machine Learning

👨‍👩‍👧‍👦 Human Learning

Parents → Teachers → Friends ↓ Social Media → Books → Internet ↓ 🧠 EXPERIENCE (Ever-evolving) ↓ 🤔 DECISION MAKING

🤖 Machine Learning

📊 DATA → 🔄 ALGORITHM ↓ 🎯 TRAINING → 📈 WTF* ↓ 🔮 PREDICTION

*WTF = "What's The Function"

🎯 Pattern Recognition Challenge

✅ Easy Pattern

1, 4, 9, 16, 25

Answer: Y = X²

❌ Impossible Pattern

2, 1, 100, 5, 6, 10

Answer: ???

🎯 Machines excel where human pattern recognition fails!

1 million data points × 10 features = Human: Impossible | AI: Possible

💰 The Loan Prediction Example

Feature	Value	Weight
Age	30 years	W₁
Salary	$190,000	W₂
Country	USA	W₃
Gender	Female	W₄

Formula: Loan = (W₁×Age) + (W₂×Salary) + (W₃×Country) + (W₄×Gender) + Bias

🧮 Interactive Loan Calculator

Adjust the sliders to see how ML weights work:

Age Weight: 0.5

Salary Weight: 0.8

Loan Approval Score: 65%

What happens if we give Gender a high weight?

Better accuracy

Introduces bias

Faster processing

🧠 Why Deep Learning?

📊 Traditional ML

Structured Data (2D)

Age	Salary	Country	Loan
25	100K	USA	Yes

🧠 Deep Learning

Unstructured Data (3D+)

📸 Images: Height × Width × RGB
🎵 Audio: Time × Frequency × Amplitude
📝 Text: Thousands of dimensions
🎬 Videos: Height × Width × RGB × Time

🎵 The Marshall Speaker Analogy

5

Basic Radio Knobs

Simple ML

1K

Professional Mixer

Deep Learning

170B

GPT-4 Parameters

Generative AI

🎛️ Each knob = Parameter (Weight or Bias)

More knobs = More control = Better output (but harder to understand!)

🧠 Perceptrons: From Biology to AI

Biological Neuron vs Artificial Perceptron

Source: mriquestions.com

The perceptron is the fundamental building block of neural networks, inspired by biological neurons. Just as a biological neuron receives signals through dendrites, processes them in the cell body, and sends output through the axon, an artificial perceptron:

Receives inputs (x₁, x₂, x₃...) - Like dendrites collecting signals
Applies weights (w₁, w₂, w₃...) - Determines signal importance
Adds bias (B) - Adjusts the activation threshold
Computes weighted sum (Σ) - Combines all inputs
Applies activation function (f) - Decides final output

💡 Key Insight: Multiple perceptrons connected together form neural networks, enabling complex pattern recognition and decision-making capabilities.

🧠 Neural Network Architecture

X₁

H₁

Y

X₂

H₂

🧠 Human Neuron

Dendrites → Cell Body → Axon

🤖 Artificial Neuron

Inputs → Aggregator → Output

🎮 Neural Network Simulator

Send signals through the network:

Click buttons to see neural activation!

🔢 Simple Example:
Input: [Age=25, Salary=50k]
Hidden: Processes patterns
Output: Loan approval probability

🖼️ Complex Example:
Input: 1000s of pixels
Hidden: 100s of layers
Output: "This is a cat"

🔒 The Black Box Problem

📊 Input Data → [🔒 138 Million Parameters] → 📈 Output ↑ "We don't know what's happening here!"

❓ The Challenge

How did it decide?
Which factors mattered?
Can we trust it?
Is it biased?

🔍 The Solutions

Explainable AI (XAI)
LIME & SHAP techniques
Human-in-the-loop
Feature importance analysis

🎭 Discriminative vs. Generative AI

Before 2020 (Discriminative)

✅ "Is this a cat or dog?"
✅ "What's in this image?"
✅ "Classify this email as spam"

After 2020 (Generative)

✨ "Create an image of a cat that never existed"
✨ "Write a story about space pirates"
✨ "Generate code for a web app"

🚀 The shift from recognition to creation!

🚀 GPT-4 Mind-Blowing Stats

170B

Parameters

120

Layers

$M

Training Cost

32

Bits per Parameter

💰 Why NVIDIA Got Rich: Hardware requirements exploded overnight!

🎯 AI Types - Where Are We?

🎯 Narrow AI

Current Reality

Knows pizza is food
❌ Doesn't know taste

🧠 General AI

Future Goal

Understands concepts
Has consciousness

🚀 Super AI

Distant Future

Self-evolving
Beyond human capability

⚠️ AI Challenges & Ethics

🔒

Data Privacy

Cambridge Analytica
"No free lunch" reality

⚖️

Bias & Fairness

Amazon hiring algorithm
Historical discrimination

💼

Job Displacement

Work WITH AI
Continuous upskilling

🎭

Deepfakes

Misinformation
Artist rights violations

💡 Key Takeaways

🔑 Fundamental Truths

AI = Pattern Recognition at Scale
Data Quality Determines Everything
We're Still in Narrow AI Phase
Ethics Must Guide Development

🎯 Success Mindset

Work WITH AI, not against it
Question everything
Stay curious
Think ethically

"We're not replacing human intelligence, we're augmenting it responsibly"

🧠 Quick Knowledge Check

1. What does AI fundamentally do?

A) Replaces human thinking completely

B) Recognizes patterns in large datasets

C) Only works with structured data

D) Creates consciousness like humans

Correct! AI is fundamentally about pattern recognition at scale - finding relationships in data that humans can't easily detect.

🎯 Activity: Build the AI Hierarchy

Drag and drop the terms to build the correct AI hierarchy:

Available Terms:

Artificial Intelligence

Machine Learning

Deep Learning

Generative AI

Build Hierarchy (Top to Bottom):

Drop items here in correct order

🤔 Reflection: Your AI Experience

Think about your daily AI interactions:

🔍 Identify 3 AI tools you use:

💭 What surprised you most today?

🎯 Challenge: Pattern Detective

Can you spot the pattern in this sequence?

2, 6, 12, 20, 30, ?

A) 40

B) 42

C) 36

D) 38

Pattern: n × (n+1) where n = 1,2,3,4,5,6...
2=1×2, 6=2×3, 12=3×4, 20=4×5, 30=5×6, 42=6×7

🧠 Quiz: ML vs Deep Learning

Which scenario REQUIRES Deep Learning over traditional ML?

A) Predicting house prices from size, location, age

B) Recognizing faces in photos

C) Analyzing customer purchase history

D) Forecasting sales from historical data

Correct! Face recognition requires processing unstructured image data (pixels in 3D: height × width × RGB), which needs Deep Learning's neural networks.

⚖️ Ethics Challenge

Scenario: AI Hiring System

Your company's AI hiring system shows 90% accuracy but tends to reject candidates from certain backgrounds. What do you do?

A) Keep using it
90% accuracy is excellent

B) Investigate bias
Audit the training data and algorithm

Best Choice: B - High accuracy doesn't justify discrimination. Bias often comes from historical data reflecting past inequalities.

🌍 Apply Your Knowledge

Design an AI Solution:

🏥 Healthcare Challenge

Design an AI system to help doctors diagnose diseases faster.

What type of AI? What data needed?

🎓 Education Challenge

Create an AI tutor that adapts to each student's learning style.

Key features? Ethical concerns?

📝 Final Assessment - Part 1

1. What's the main difference between Discriminative and Generative AI?

A) Speed of processing

B) Recognition vs Creation

C) Cost of training

D) Hardware requirements

2. Why is GPT-4 called a "black box"?

A) It's painted black

B) We can't explain how it makes decisions

C) It only works in darkness

D) It's proprietary software

📝 Final Assessment - Part 2

3. Which statement about current AI is TRUE?

A) AI has achieved consciousness

B) We're still in the Narrow AI phase

C) AI can solve any problem

D) AI doesn't need human oversight

4. What makes Deep Learning different from traditional ML?

A) It's faster

B) It handles unstructured data better

C) It's cheaper

D) It requires less data

5. The Marshall Speaker analogy teaches us that:

A) More parameters = more control but less interpretability

B) Simple is always better

C) AI is like music

D) Hardware doesn't matter

🔧 AI Algorithms & Frameworks Toolkit

Essential building blocks for real-world AI systems

🖼️ Image Processing

CNN (Convolutional Neural Network):
Detects patterns in images like edges, shapes, objects

ResNet-50/101:
Advanced CNN with "shortcuts" - can be 50 or 101 layers deep

📈 Time Series & Sequences

LSTM (Long Short-Term Memory):
Remembers important info over time - great for weather prediction

GRU (Gated Recurrent Unit):
Simpler version of LSTM - faster but still remembers patterns

🎯 Algorithm Matching Challenge

Which algorithm is best for analyzing chest X-rays?

LSTM

CNN/ResNet

GRU

Transformer

🏥 Healthcare: ResNet-101 for medical images

🎓 Education: LSTM for learning progress tracking

🌱 Environment: CNN + LSTM for satellite + weather data

🎓 Capstone Project: AI Solution Design

🎯 Project Goal

Apply everything you've learned to solve a real-world problem using AI principles.

🏥

Healthcare AI

Design an AI system to help doctors diagnose diseases from medical images

🎓

Education AI

Create an AI tutor that adapts to individual learning styles

🌱

Environmental AI

Build an AI system to predict and prevent environmental disasters

4 Phases: Problem Analysis → Technical Design → Ethics & Bias → Implementation

Total Points: 100 (25 points each phase)

📋 Capstone: Phase 1 & 2

Phase 1: Problem Analysis (25 points)

📊 Data Requirements:

🤖 AI Type Selection:

Phase 2: Technical Design (25 points)

🧠 Algorithm Choice:

📈 Success Metrics:

📋 Capstone: Phase 3 & 4

Phase 3: Ethics & Bias (25 points)

⚖️ Potential Biases:

🛡️ Mitigation Strategies:

Phase 4: Implementation Plan (25 points)

💰 Resources Needed:

🚀 Deployment Strategy:

🏆 Congratulations! Generate Your Certificate

📜 AI Literacy Certification

You've completed the comprehensive AI Literacy training program!

Enter your full name for the certificate:

🎉 Congratulations!

Your AI Literacy Journey is Complete!

🧠

What You've Learned

AI Hierarchy & Types
ML vs Deep Learning
Pattern Recognition
Neural Networks
Generative AI Revolution
Ethics & Challenges

🚀

Next Steps

Experiment with AI tools
Stay updated on developments
Think critically about AI claims
Consider ethical implications
Share your knowledge

Your Final Score: 0/150

Keep learning and exploring!