What is Artificial Intelligence?
📚 Definition
Artificial Intelligence (AI) is the branch of computer science that enables machines to simulate human intelligence by performing tasks such as learning, reasoning, problem-solving, perception, and decision-making.
🧠 Core Components
- Reasoning: Logical thinking and drawing conclusions
- Learning: Acquiring and applying knowledge
- Perception: Interpreting sensory information
- Problem-solving: Identifying and resolving challenges
- Linguistics: Understanding and using language
✅ Advantages
- 24/7 efficiency and productivity
- Enhanced decision-making with data analysis
- Personalized user experiences
- Innovation in robotics and autonomous systems
- Risk reduction in dangerous tasks
⚠️ Disadvantages
- Job displacement through automation
- High implementation costs
- Privacy and ethical concerns
- Over-dependency on technology
- Lack of emotional intelligence
Types of Intelligence
According to Howard Gardner's theory, there are 9 distinct types of intelligence:
Linguistic
Using language effectively
Logical-Mathematical
Reasoning and problem-solving
Spatial
Thinking in images
Musical
Rhythm and composition
Bodily-Kinesthetic
Physical skills
Interpersonal
Understanding others
Intrapersonal
Self-awareness
Naturalistic
Nature recognition
Existential
Deep questions about life
🆕 New Types of Intelligence
- Emotional (EQ): Managing emotions and empathy
- Creative: Generating innovative ideas
- Collaborative: Teamwork and communication
History of Artificial Intelligence
1956 - Birth of AI
John McCarthy coins "Artificial Intelligence" at Dartmouth Conference
1966 - ELIZA
First chatbot created by Joseph Weizenbaum
1972 - WABOT-1
First full-scale humanoid robot in Japan
1974-1980 - First AI Winter
Reduced funding due to unmet expectations
1997 - Deep Blue
IBM's Deep Blue defeats chess champion Garry Kasparov
2011 - IBM Watson
Wins Jeopardy! against human champions
2015 - Amazon Alexa
Voice-activated AI becomes popular
Three Domains of AI
📊 Data (AI with Data)
Focus: Learning from information through collection, storage, and analysis
Skills: Data collection, visualization, pattern recognition
Examples: Weather forecasting, recommendation systems, exam analysis
👁️ Computer Vision (AI with Vision)
Focus: Understanding and interpreting visual information
Skills: Image recognition, object detection, facial recognition
Examples: Self-driving cars, medical imaging, security surveillance
💬 Natural Language Processing (AI with Language)
Focus: Understanding and generating human language
Skills: Speech recognition, text analysis, translation
Examples: Voice assistants (Alexa, Siri), chatbots, translators
🎯 Match the Application
Click to reveal which domain each application belongs to:
AI Project Cycle
A step-by-step process to build AI solutions systematically:
Problem Scoping
Define the problem clearly
Data Acquisition
Collect relevant data
Data Exploration
Analyze and visualize
Modeling
Apply AI algorithms
Evaluation
Test accuracy
Deployment
Implement in real world
🎯 4Ws Problem Canvas
Used in Problem Scoping stage:
- Who: Identify stakeholders
- What: Define the problem
- Where: Specify location/context
- Why: Explain importance
🔄 Modeling Approaches
Rule-Based: Fixed logic with predefined if-then conditions. Cannot adapt or learn.
Learning-Based: Data-driven models that learn patterns and improve with more data.
📈 Evaluation Metrics
- TP (True Positive): Correctly predicted positive
- TN (True Negative): Correctly predicted negative
- FP (False Positive): Incorrectly predicted positive
- FN (False Negative): Incorrectly predicted negative
AI Ethics
⚖️ Key Principles
- Fairness: Avoid bias and treat all users equally
- Transparency: Explainable and understandable decisions
- Privacy: Protect personal data
- Accountability: Humans responsible for AI decisions
- Accessibility: Benefits available to all
- Sustainability: Support environmental and social well-being
🤔 Ethics vs Morality
Ethics: Formal principles guided by philosophy, law, or professional codes
Morality: Personal/cultural beliefs shaped by tradition, religion, or society
⚠️ AI Bias
Definition: When AI produces systematically unfair results to certain groups
Causes:
- Biased training data (incomplete, imbalanced)
- Unfairly designed algorithms
- Human assumptions and stereotypes
🚨 Ethical Scenarios
1. Bias and Discrimination: Use diverse datasets and conduct fairness audits
2. Privacy Risks: Apply anonymization and encryption
3. Transparency: Build explainable AI systems
4. Safety: Rigorous testing in critical sectors
5. Human Oversight: Expert validation of AI decisions
6. Job Displacement: Reskilling programs for workers
7. Cybersecurity: Strong security measures
8. Cultural Differences: Context-specific ethical frameworks