1. Intelligence
Intelligence refers to the ability to learn, understand, reason, solve problems, adapt to new situations, and make decisions. Human intelligence involves memory, creativity, emotions, and social skills. Machine intelligence, on the other hand, focuses on enabling systems to perform tasks that typically require human intelligence.
2. Decision Making
Decision making is the process of choosing the best option from multiple possibilities. In humans, this involves logic, experience, intuition, and sometimes emotions.
In AI systems, decision making is based on data, algorithms, and mathematical models. AI attempts to make decisions faster, more accurately, and more consistently than humans.
3. What is AI and What is NOT AI
✔ What is AI?
AI refers to machines or software that can perform tasks that normally require human intelligence, such as:
- Understanding language
- Recognizing images
- Learning from data
- Making predictions
- Solving complex problems
✖ What is not AI?
Not everything that’s automated is AI. Examples:
- A calculator performing arithmetic
- A washing machine with predefined cycles
- Simple “if-else” rules in software
These systems do not learn or adapt — they follow fixed instructions.
4. Introduction to AI, ML, and DL
Artificial Intelligence (AI)
The broad field of creating smart machines that can mimic human intelligence.
Machine Learning (ML)
A subset of AI where machines learn from data instead of being explicitly programmed.
Deep Learning (DL)
A specialized branch of ML using neural networks with many layers, enabling advanced tasks like speech recognition, autonomous driving, and natural language understanding.
5. Introduction to AI Domains
AI has several major domains:
- Machine Learning – pattern recognition and predictions
- Natural Language Processing (NLP) – human language understanding
- Computer Vision – interpreting images and videos
- Robotics – physical machines performing tasks
- Expert Systems – rule-based decision making
- Speech Recognition – converting spoken words into text
- Reinforcement Learning – learning through rewards and penalties
6. Applications of AI
AI is used across almost every industry today:
- Healthcare: diagnostics, drug discovery
- Finance: fraud detection, trading algorithms
- Transportation: self-driving cars
- Education: personalized learning systems
- E-commerce: recommendation engines
- Agriculture: crop monitoring, yield prediction
- Entertainment: content generation, video enhancement
- Manufacturing: predictive maintenance, automation
AI has become an integral part of modern life.
7. AI Ethics
AI ethics focuses on ensuring AI is used responsibly. Key principles include:
- Transparency: knowing how AI makes decisions
- Fairness: avoiding bias
- Privacy: protecting user data
- Accountability: humans responsible for AI actions
- Safety: ensuring AI doesn’t cause harm
Ethical AI is essential to build trust and ensure technology benefits society.
