Branches of AI - telivaina/ai GitHub Wiki

Branches of AI ๐ŸŒ

1. Rule-Based Systems ๐Ÿ’ก

AI systems that make decisions based on predefined "if-then" rules, without learning from data.

Subfields

  • Expert Systems: Simulate human expertise in specialized areas like medical diagnostics.
  • Fuzzy Logic Systems: Handle uncertainty by incorporating degrees of truth in decisions.

Related Fields

  • Knowledge Representation: Representing knowledge in a form that a system can use to solve complex tasks.

Famous Applications

  • MYCIN: A medical rule-based expert system.
  • XCON: Configured computer systems for DEC.

2. Symbolic AI ๐Ÿค–

AI that represents knowledge using symbols and logical rules to mimic human-like reasoning.

Subfields

  • Knowledge Representation: Forms like graphs and ontologies to represent information.
  • Automated Reasoning: Drawing conclusions based on logical rules.

Related Fields

  • Cognitive AI: Mimics human thinking using symbolic logic and reasoning.

Famous Applications

  • IBM Watson: Uses symbolic reasoning for complex domain questions.

3. Machine Learning (ML) ๐Ÿ“Š

A subset of AI where systems learn from data to improve performance over time.

Subfields

  • Supervised Learning: Learning from labeled data to make predictions.
  • Unsupervised Learning: Finding patterns in unlabeled data.
  • Reinforcement Learning: Learning through rewards and penalties in an environment.

Related Fields

  • Deep Learning (DL): A subset of ML using neural networks with many layers.

Famous Applications

  • Google: Uses ML for Search and Translate.
  • Netflix: Movie recommendations.

4. Natural Language Processing (NLP) ๐Ÿ—ฃ

AIโ€™s ability to process and generate human language.

Subfields

  • Text Classification: Categorizing text into predefined groups.
  • Machine Translation: Automatically translating text between languages.
  • Sentiment Analysis: Analyzing sentiment in text (positive, negative, neutral).

Related Fields

  • Computational Linguistics: Uses computational methods to process language.
  • Speech Recognition: Converting speech to text.

Famous Applications

  • Google Translate: Machine translation service.
  • Siri / Alexa: Voice-activated assistants using NLP.

5. Computer Vision ๐Ÿ‘๏ธ

AI that enables computers to interpret and make decisions based on visual data.

Subfields

  • Image Recognition: Identifying objects or faces in images.
  • Object Detection: Locating specific objects within images.
  • Image Segmentation: Dividing an image into regions for analysis.

Related Fields

  • Image Processing: Enhancing and manipulating images for use in vision tasks.

Famous Applications

  • Tesla Autopilot: Uses computer vision for self-driving cars.
  • Facebook: Uses computer vision for facial recognition in photos.

6. Robotics ๐Ÿค–

AI applied to robots, enabling them to perform tasks autonomously or semi-autonomously.

Subfields

  • Autonomous Navigation: Robots navigating using sensors and data.
  • Robot Perception: Allowing robots to understand their environment.

Related Fields

  • Human-Robot Interaction: Focus on interactions between humans and robots.

Famous Applications

  • Boston Dynamics: Known for robots like Spot.
  • iRobot: Creator of Roomba, an autonomous vacuum cleaner.

7. Planning & Reasoning ๐Ÿง 

AI systems that make decisions based on goals, constraints, and environments.

Subfields

  • Automated Planning: Creating a sequence of actions to achieve a goal.
  • Decision Making: Making the best decision in a given situation.

Related Fields

  • Optimization: Finding the best solution to a problem.

Famous Applications

  • Uber: Uses automated planning to optimize routes.
  • Air Traffic Control: Uses planning and reasoning for managing air traffic.

8. Deep Learning (DL) ๐Ÿ”

A subset of ML using neural networks with many layers to handle complex tasks.

Subfields

  • Convolutional Neural Networks (CNNs): Used for image processing.
  • Recurrent Neural Networks (RNNs): Used for time-series or sequential data.
  • Generative Adversarial Networks (GANs): Used for generating realistic data like images.

Related Fields

  • Neural Networks: Computational models inspired by the human brain.

Famous Applications

  • DeepMind: Known for AlphaGo, which beat human players in the game of Go.
  • OpenAI: Uses deep learning for language models like GPT.

9. Reinforcement Learning (RL) ๐ŸŽฎ

A type of machine learning where agents learn by interacting with the environment and receiving rewards or penalties.

Subfields

  • Model-Free RL: Learning from experience without modeling the environment.
  • Model-Based RL: Building a model of the environment to plan future actions.

Related Fields

  • Game Theory: A framework for analyzing competitive interactions, often applied in RL.

Famous Applications

  • DeepMindโ€™s AlphaZero: Uses RL to play board games like Chess and Go.
  • OpenAI Five: Uses RL to play Dota 2.

10. Artificial General Intelligence (AGI) ๐ŸŒ

AI capable of performing any intellectual task that a human can.

Subfields

  • Cognitive Modeling: Simulating human thought processes in AI systems.
  • Cross-Domain Intelligence: Enabling AI to generalize knowledge across various domains.

Related Fields

  • Cognitive Science: Studies the mind and intelligence, relevant for AGI development.

Famous Applications

  • OpenAI: Developing AGI with a focus on GPT models.
  • SingularityNET: Working on decentralized AGI.

11. Generative AI (GAI) ๐Ÿง 

AI systems that create new content like images, text, or music based on learned data.

Subfields

  • Generative Adversarial Networks (GANs): Models that generate new, realistic data by pitting two networks against each other.
  • Variational Autoencoders (VAEs): A type of generative model used for image generation.

Related Fields

  • Creative AI: AI that generates creative content such as art, music, or literature.

Famous Applications

  • DeepArt: AI that creates artwork based on famous art styles.
  • OpenAI DALLยทE: A model that generates images from textual descriptions.

12. Evolutionary Algorithms ๐Ÿงฌ

Optimization algorithms inspired by natural selection, using concepts like mutation and selection to evolve solutions.

Subfields

  • Genetic Algorithms: Solving problems by evolving solutions through selection, mutation, and crossover.
  • Differential Evolution: A variant of evolutionary algorithms that optimizes problems based on solution differences.

Related Fields

  • Optimization: The process of improving solutions.

Famous Applications

  • NASA: Uses evolutionary algorithms for spacecraft design and mission planning.

13. Swarm Intelligence ๐Ÿ

AI based on collective behavior in decentralized systems, inspired by social insects like ants and bees.

Subfields

  • Ant Colony Optimization: Mimics ant foraging behavior for optimization tasks.
  • Particle Swarm Optimization: Optimizing solutions by mimicking the flocking behavior of birds.

Related Fields

  • Multi-Agent Systems (MAS): Systems where multiple agents interact to solve problems.

Famous Applications

  • Optimization Systems: Used for logistics and network design in companies.

14. Expert Systems ๐Ÿง‘โ€โš–๏ธ

AI systems designed to emulate human expertise in specialized domains like medical diagnosis.

Subfields

  • Knowledge Base: A repository of domain-specific knowledge.
  • Inference Engine: Applies rules to knowledge to make decisions or conclusions.

Related Fields

  • Rule-Based Systems: Foundation for expert systems that use predefined rules for decision making.

Famous Applications

  • MYCIN: Medical expert system for diagnosing blood infections.
  • DENDRAL: A system for chemical analysis.

15. Cognitive Computing ๐Ÿง 

AI systems designed to simulate human thought processes, including learning, understanding, and decision-making.

Subfields

  • Machine Perception: Understanding sensory inputs like vision and sound.
  • Natural Language Understanding: Understanding and processing human language.

Related Fields

  • Artificial Intelligence: The broader field encompassing cognitive computing.

Famous Applications

  • IBM Watson: A cognitive computing platform used in healthcare and finance.
  • Apple Siri: Uses cognitive computing for voice recognition and interaction.