Artificial Intelligence

• Learning: Acquiring information and rules for using that information (through algorithms or experience).
• Reasoning: Applying rules to reach conclusions or make decisions.
• Problem-solving: Finding solutions to complex or unfamiliar problems.

• Hypothetical AI with the ability to perform any intellectual task a human can.

• Can transfer knowledge from one domain to another.

• Still under research and not yet realized.

• Designed for a specific task.

• Examples: Google Maps, recommendation systems, facial recognition, chatbots.

• Most AI today falls into this category.

• It can outperform humans in specific tasks.

• A future, theoretical form of AI that surpasses human intelligence in all fields.

• It raises ethical, safety, and control concerns among scientists and philosophers.

• It would possess the ability to improve and evolved

 Algorithms that allow systems to learn from data and improve over time without being explicitly programmed.

Supervised Learning: A type of ML where the model is trained on labeled data (i.e., input-output pairs), enabling it to make predictions on new, unseen data.

Unsupervised Learning: A type of ML that works with unlabeled data to find hidden patterns, groupings, or structures within the dataset (e.g., clustering, dimensionality reduction).

Enables machines to understand, interpret, and respond to human language.

Text Analysis and Sentiment Detection: NLP techniques are used to analyze text for meaning, emotions, and opinions—commonly used in social media monitoring and customer feedback.

Language Generation and Translation: NLP powers tools that can automatically generate human-like text (e.g., chatbots, writing assistants) and translate between different languages.

A subset of ML that uses neural networks (inspired by the human brain) to process complex patterns in data.

Convolutional Neural Networks (CNNs): Specialized deep learning models primarily used for image and video recognition tasks, such as facial recognition or object detection.

Recurrent Neural Networks (RNNs): Deep learning models designed to handle sequential data, making them well-suited for tasks like speech recognition, language modeling.

AI that interprets visual data from the world, such as images and video.

Image Classification and Object Detection: Enables systems to recognize and label objects or features within images or videos, used in applications like facial recognition and autonomous vehicles.

 Facial Recognition and Tracking: Allows systems to identify or often used in security, authentication, and social media tagging.

• Diagnosing diseases, analyzing medical images, drug discovery.
• Remote patient monitoring, AI-assisted robotic surgery.

• Self-driving cars, traffic management, predictive maintenance for vehicles

• Route optimization and logistics planning, driver behavior analysis.

• Fraud detection, algorithmic trading, customer service chatbots.
• Credit risk assessment, financial forecasting.

• Personalized shopping recommendations, inventory management.

• Automated checkout systems.

• customer sentiment analysis.