- Artificial Intelligence Tutorial
- AI - Home
- AI - Overview
- AI - History & Evolution
- AI - Types
- AI - Terminology
- AI - Tools & Frameworks
- AI - Applications
- AI - Real Life Examples
- AI - Ethics & Bias
- AI - Challenges
- Branches in AI
- AI - Research Areas
- AI - Machine Learning
- AI - Natural Language Processing
- AI - Computer Vision
- AI - Robotics
- AI - Fuzzy Logic Systems
- AI - Neural Networks
- AI - Evolutionary Computation
- AI - Swarm Intelligence
- AI - Cognitive Computing
- Intelligent Systems in AI
- AI - Intelligent Systems
- AI - Components of Intelligent Systems
- AI - Types of Intelligent Systems
- Agents & Environment
- AI - Agents and Environments
- Problem Solving in AI
- AI - Popular Search Algorithms
- AI - Constraint Satisfaction
- AI - Constraint Satisfaction Problem
- AI - Formal Representation of CSPs
- AI - Types of CSPs
- AI - Methods for Solving CSPs
- AI - Real-World Examples of CSPs
- Knowledge in AI
- AI - Knowledge Based Agent
- AI - Knowledge Representation
- AI - Knowledge Representation Techniques
- AI - Propositional Logic
- AI - Rules of Inference
- AI - First-order Logic
- AI - Inference Rules in First Order Logic
- AI - Knowledge Engineering in FOL
- AI - Unification in First Order Logic (FOL)
- AI - Resolution in First Order Logic (FOL)
- AI - Forward Chaining and backward chaining
- AI - Backward Chaining vs Forward Chaining
- Expert Systems in AI
- AI - Expert Systems
- AI - Applications of Expert Systems
- AI - Advantages & Limitations of Expert Systems
- AI - Applications
- AI - Predictive Analytics
- AI - Personalized Customer Experiences
- AI - Manufacturing Industry
- AI - Healthcare Breakthroughs
- AI - Decision Making
- AI - Business
- AI - Banking
- AI - Autonomous Vehicles
- AI - Automotive Industry
- AI - Data Analytics
- AI - Marketing
Artificial Intelligence - Research Areas
Artificial Intelligence is technology than allows systems to mimic human behavior, intelligence, and characteristics. It is not the AI, but different concepts in AI help tackle real-world problems. This chapter discusses about the main branches of artificial intelligence and the basic components of AI.
Key Aspects of AI
Some of the key aspects of AI that allows the systems to process, interpret, synthesize, and understand information are −
- Learning: This aspect allows AI systems to analyze data and interpret patterns with human intervention. For example, voice assistants like Siri or Alexa improve their grasping ability through continuous learning.
- Decision Making: The AI systems employ logical rules, probabilistic models, and algorithms to make conclusions and decisions. These systems are often designed to apply reasoning to get accurate outcomes. For example, tools like Grammarly decided when to insert commas and other punctuation marks.
- Problem-Solving: This aspect in AI involves processing data, manipulating it, and applying it solve problems in various scenarios. For example, in a chess game the AI analyzes the opponent's moves and strategies the next moves based on the game's rules and future scenarios.
- Perception: This aspect related to how the technology utilizes actual or simulated sensory organs. AI systems analyses data to recognize objects and understand their spatial relationships to these entities. This is usually included in tasks like image identification, object recognition, image partitioning, and video examination.
Branches of AI
Artificial intelligence includes many specialized fields, each with a unique functionality and application. Below are the ten key branches of AI and their respective roles −
Machine Learning
Machine learning(ML)is one of the most crucial branch of AI that enables machines to learn autonomously from data without explicit programming. ML systems enhance their performance continuously based on analyzing patterns and applying algorithms.
This approach is widely used to enable businesses to forecast trends and recommendation systems. ML algorithms are used in various applications including image recognition, spam filtering, and natural language processing.
Natural Language Processing
Natural Language Processing (NLP) allows computers to understand, interpret, and generate human language. By using these algorithms and linguistic rules, NLP systems analyze text and speech, to bridge the gap between humans and computers. Applications like chatbots, voice assistants, and google translator. use NLP.
Computer Vision
Computer Vision is the technology that allows machines to interpret the world visually i.e., it allows to identify objects in images and videos. Algorithmic models help computers teach themselves to differentiate one image from another. Some of the major applications of computer vision are object tracking, image classification, and facial recognition across various industries
Robotics
Robotics uses AI to develop and design programmed robots or machines that perform tasks automatically. AI is applied to make intelligent robots which can perform the tasks similar to humans. Some of the major areas of applications include manufacturing automation, medical robots, service robots, and space exploration.
Expert Systems
Expert Systems is a branch of AI which rely on obtaining the knowledge of human experts and program that knowledge into a system. This makes these systems have the ability of decision making and problem-solving. Some of the common areas of application are medical diagnosis, financial forecasting, and troubleshooting systems.
Neural Networks
Neural Networks are systems which are developed inspired by the biological neurons in human brain. This technology is applied in self-driving cars, speech recognition, image recognition, automatic machine translation, and more. The main challenges while implementing neural networks is it requires lots of data to get trained with lots of computational power.
Fuzzy Logic
Fuzzy Logic is a branch of AI that helps solve issues or statements and recognize if they are true or false. It is a mathematical method for identifying uncertainty in decision-making, and it is used in wide range of applications like control systems, decision-making systems, and pattern recognition.
Evolutionary Computation
Evolutionary Computation is a branch of AI that mimics biological evolution processes using algorithms like genetic algorithm, evolutionary programming, evolution strategies, and genetic programming. It involves creating a initial solution, evaluating its property, selecting solutions based on evaluation results, conducting evolutionary operations, and obtaining the next solution until the requirement is fulfilled.
Cognitive Computing
Cognitive Computing is a branch of AI that simulates human thought processes in a computerized model. The key features of the technology include contextual understanding, adaptive learning, and interactive capabilities. The key areas of application include healthcare diagnostics, financial analysis, and customer service.
Swarm Intelligence
Swarm Intelligence is a branch of AI which refers to the collective behavior of decentralized, self-organized systems observed in nature. This is used to explain how simple agents can work together to achieve complex tasks, without a centralized control or a leader.