Artificial Intelligence: Computer Vision

Definition: A field of AI that enables computers to interpret and process visual information from the world.
Key Components:
- Image Recognition: Identifying objects, people, or scenes in images.
- Object Detection: Locating and classifying objects within an image.
- Image Segmentation: Dividing an image into segments to simplify analysis.
- Facial Recognition: Identifying or verifying a person from a digital image or video.
Techniques:
- Convolutional Neural Networks (CNNs): Deep learning algorithms specifically designed for image processing.
- Optical Character Recognition (OCR): Converting different types of documents into machine-readable text.
Applications:
- Autonomous vehicles: Navigating and interpreting surroundings.
- Medical imaging: Assisting in diagnosis through analysis of images like X-rays and MRIs.

Definition: A subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data.
Types:
- Supervised Learning: Algorithms learn from labeled data (e.g. classification, regression).
- Unsupervised Learning: Algorithms identify patterns in unlabeled data (e.g. clustering, association).
- Reinforcement Learning: Algorithms learn through trial and error to maximize a reward.
Key Algorithms:
- Decision Trees: A model that makes decisions based on a series of questions.
- Support Vector Machines (SVM): A classification method that finds the optimal hyperplane for data separation.
- Neural Networks: Computational models inspired by the human brain, used for complex pattern recognition.
Applications:
- Predictive analytics: Forecasting trends based on historical data.
- Natural language processing: Understanding and generating human language.

Healthcare:
- Diagnosis and treatment recommendations through data analysis.
- Personalized medicine based on genetic information.
Finance:
- Fraud detection systems analyzing transaction patterns.
- Algorithmic trading using predictive models.
Entertainment:
- Recommendation systems for movies, music, and products.
- Content creation through AI-generated art and writing.
Manufacturing:
- Predictive maintenance for equipment using machine learning.
- Robotics for automating production lines.
Customer Service:
- Chatbots providing 24/7 customer support.
- Sentiment analysis to gauge customer feedback.

Field of AI: Enables interpretation of visual information from the surrounding world.
Key Components:
- Image Recognition: Identifies objects, people, or scenes in images.
- Object Detection: Locates and classifies specific objects within an image.
- Image Segmentation: Breaks down an image into segments for easier analysis.
- Facial Recognition: Identifies or verifies individuals using digital images or videos.
Techniques:
- Convolutional Neural Networks (CNNs): Designed to process images through deep learning algorithms.
- Optical Character Recognition (OCR): Transforms documents into machine-readable text.
Applications:
- Autonomous Vehicles: Helps navigate and understand surroundings.
- Medical Imaging: Aids in diagnosing health issues through analysis of images like X-rays and MRIs.

Definition: Subset of AI focusing on algorithms that learn from data and make predictions.
Types:
- Supervised Learning: Learns from labeled datasets; used for classification and regression tasks.
- Unsupervised Learning: Detects patterns in unlabeled data; includes clustering and association techniques.
- Reinforcement Learning: Learning through trial and error to achieve maximum rewards.
Key Algorithms:
- Decision Trees: Decision-making models based on sequential questions.
- Support Vector Machines (SVM): Classification technique that identifies optimal data separation hyperplanes.
- Neural Networks: Mimic the human brain's architecture for complex pattern recognition tasks.
Applications:
- Predictive Analytics: Uses historical data to forecast future trends.
- Natural Language Processing: Involves understanding and generating human language.

Healthcare:
- Enhances diagnosis and offers treatment suggestions via data analysis.
- Develops personalized medical approaches based on genetic data.
Finance:
- Utilizes fraud detection systems to analyze transaction patterns.
- Engages in algorithmic trading through predictive modeling.
Entertainment:
- Implements recommendation systems for media and product suggestions.
- Facilitates content generation through AI-driven art and writing.
Manufacturing:
- Incorporates predictive maintenance for equipment efficiency.
- Deploys robotics to automate industrial production lines.
Customer Service:
- Uses chatbots to provide constant customer support around the clock.
- Applies sentiment analysis for assessing customer feedback.