Ai

• Artificial Intelligence (AI)


  A branch of computer science that involves creating computer systems that can perform tasks that would normally require human intelligence
• Goal of AI


  • Simulate intelligent behaviour in machines, including: problem-solving, decision-making, natural language processing
• Areas where AI is often used

  • Robotics
  • Natural language processing
  • Expert systems
  • Machine learning
• Machine learning


  A subset of AI that focuses on giving computers the ability to learn and improve from data, without being explicitly programmed
• Types of AI


  • Weak AI (narrow AI)
  • Strong AI (artificial general intelligence, AGI)
  • Superintelligence
• Weak AI (narrow AI)

  Designed to perform a specific task or set of tasks
• Strong AI (artificial general intelligence, AGI)


  Designed to perform any intellectual task that a human can do
• Superintelligence


  A hypothetical AI that would surpass human intelligence in all areas
• AI has advantages such as increased efficiency, accuracy, and scalability However, AI also has disadvantages such as the potential for job loss, biassed decision-making, and ethical concerns around its use
• Characteristics Collection of data and rules AI systems require large amounts of data to perform tasks The data is processed using rules or algorithms that enable the system to make decisions and predictions Ability to reason AI systems can use logical reasoning to evaluate information and make decisions based on that information Ability to learn and adapt This will mean it can change its own rules and data AI systems can be designed to learn from past experiences and adjust their behaviour accordingly
• Components There are two main types of AI systems: Expert Systems: Have a knowledge base A database of facts to generate rules that are used to solve problems and make decisions Have a rule base A set of rules or logic that is used to apply the knowledge in the knowledge base to specific problems Have an inference engine A program that applies the rules in the rule base to the facts in the knowledge base to solve problems Have an interface A way for users to interact with the system and provide input
• Main types of AI systems

  • Expert Systems
  • Machine Learning
• Expert Systems


  • Have a knowledge base
  • Have a rule base
  • Have an inference engine
  • Have an interface
• Knowledge base


  A database of facts to generate rules that are used to solve problems and make decisions
• Rule base


  A set of rules or logic that is used to apply the knowledge in the knowledge base to specific problems
• Inference engine


  A program that applies the rules in the rule base to the facts in the knowledge base to solve problems
• Interface


  A way for users to interact with the system and provide input
• Machine Learning


  • The program has the ability to automatically adapt its own processes and/or data
  • Uses algorithms to analyse data and identify patterns or relationships
  • The system can learn from the data and improve its performance over time
• Machine Learning types


  • Supervised
  • Unsupervised
• Supervised machine learning


  Uses labelled data to train the system
• Unsupervised machine learning


  Uses unlabelled data
• Weak AI (Narrow AI)


  A type of artificial intelligence that is designed to perform a specific task or set of tasks. It operates within a limited set of constraints and is not capable of generalizing its knowledge to new situations or environments. Examples include Siri, Google's search algorithms, self-driving cars, and chess-playing computers.
• Artificial Intelligence (AI)


  The simulation of human intelligence in machines that are programmed to think and learn like humans.
• Specific task or set of tasks


  The area of expertise or focus for a weak AI system. The system is designed to excel in this specific domain, but it lacks the ability to transfer its knowledge to other areas or to exhibit true intelligence or consciousness.
• Limited set of constraints


  The limitations or boundaries within which a weak AI system operates. The system is not capable of generalizing its knowledge to new situations or environments outside of these constraints.
• Knowledge Base


  A collection of facts that the inference engine uses to solve problems. These facts can be things that are known to be true or assumptions that are made for the purpose of solving a problem.
• Rule Base


  A set of rules that the inference engine uses to draw conclusions from the facts in the knowledge base. These rules are typically expressed in the form of "if-then" statements, such as "if A is true, then B is also true."
• Inference Engine


  The program that applies the rules in the rule base to the facts in the knowledge base to solve problems. It does this by following these steps: a. Select a rule from the rule base. b. Check the knowledge base to see if all the conditions in the rule are true. c. If all the conditions are true, add the conclusion of the rule to the knowledge base. d. Repeat steps a-c until no more rules can be applied or a solution to the problem has been found.
• Knowledge Base


  Collection of facts used by the inference engine to solve problems.
• Rule Base


  Set of rules used by the inference engine to draw conclusions from the facts in the knowledge base.
• Inference Engine


  Program that applies the rules in the rule base to the facts in the knowledge base to solve problems.
• Knowledge Base


  Collection of facts used by the inference engine to solve problems. For example, the knowledge base might contain the fact "The sky is blue" or the assumption "If it is raining, then the ground is wet."
• Rule Base


  Set of rules used by the inference engine to draw conclusions from the facts in the knowledge base. For example, the rule base might contain the rule "If the sky is blue, then it is not raining."
• Inference Engine


  Program that applies the rules in the rule base to the facts in the knowledge base to solve problems. For example, if the inference engine has the fact "The sky is blue" in its knowledge base and the rule "If the sky is blue, then it is not raining" in its rule base, it can infer that it is not raining.
• Expert System


  A type of computer system that uses artificial intelligence (AI) to solve complex problems that are difficult for humans to solve on their own. It consists of three main components: a Knowledge Base, a Rule Base, and an Inference Engine.
• Knowledge Base


  The foundation of an expert system. It contains the factual and experiential knowledge that the expert system uses to solve problems. This knowledge can be acquired from human experts or from other sources, such as books, articles, and databases.
• Rule Base


  The set of rules that the inference engine uses to draw conclusions from the facts in the knowledge base. These rules are typically expressed in the form of "if-then" statements and are used to represent the decision-making and problem-solving processes of human experts.
• Inference Engine


  The part of the expert system that applies the rules in the rule base to the facts in the knowledge base to draw conclusions and solve problems. It follows a systematic approach, such as forward or backward chaining, to apply the rules and reach a solution.