Summary of AI Concepts and Terminologies
1. What is AI?
- AI (Artificial Intelligence) refers to computer systems that solve cognitive problems like learning, image recognition, and decision-making.
- AI aims to create self-learning systems that can derive meaning from data.
- Examples: Alexa, ChatGPT, fraud detection, automation of repetitive tasks.
2. Machine Learning (ML)
- ML is a branch of AI where systems improve through data and algorithms, mimicking human learning.
- Training: ML uses large datasets to identify patterns and make predictions.
- Example: Product recommendations on e-commerce sites.
3. Deep Learning
- Deep Learning is a type of ML inspired by the human brain, using neural networks to process complex data.
- Example: Speech recognition, image identification (like facial recognition).
4. AI Applications in Industry
- Healthcare: AI helps read X-rays, diagnose diseases, and predict pandemics.
- Manufacturing: AI monitors assembly lines and predicts equipment maintenance.
- Retail: AI makes personalized product recommendations.
- Finance: AI detects fraudulent transactions by analyzing patterns.
- Human Resources: AI matches candidates with job roles.
- Entertainment: AI recommends personalized content (e.g., Discovery, Netflix).
5. AI in Business Efficiency
- AI can forecast demand, optimize resource allocation, and predict customer behavior.
- Example: Taxi companies use AI to position cars at optimal locations.
6. Regression Analysis and Inferences
- Regression: AI uses historical data to predict future trends.
- Inferences: Predictions made by AI based on data patterns, often probabilistic.
- Anomalies: AI detects deviations from expected patterns (e.g., call center traffic drops).
7. Computer Vision
- AI processes images and video to identify objects, faces, and anomalies.
- Example: Detecting defects on a product or missing components on a circuit board.
8. Natural Language Processing (NLP)
- NLP allows machines to understand, interpret, and generate human language.
- Example: Real-time translation between languages, chatbots (like booking systems or customer service).
9. Generative AI
- Generative AI creates original content like text, images, music, etc., based on prompts.
- Example: A song generated from a simple text prompt.
Key Terms to Remember:
Generative AI: AI that creates original content.
AI: Solves cognitive problems (learning, recognition, etc.).
ML: Learning from data using algorithms.
Deep Learning: Advanced ML using neural networks.
Inference: AI’s prediction or educated guess based on data.
Anomaly: A deviation from expected data patterns.
NLP: AI that understands and generates human language.
