6.3 AI Flashcards
Define AI
artificial intelligence (AI) – AI is a branch of computer science dealing with the simulation of intelligent behaviours by computers
What are the 2 types of AI systems
define them
expert system – a form of AI that has been developed to mimic a human’s knowledge and expertise
machine learning – algorithms are trained and learn from past experiences
Some uses of expert systems
» diagnosis of a patient’s illness
» identification of plants, animals and chemical/biological compounds
» strategy games, such as chess
Advantages of expert systems
High accuracy
High consistency
Can store vast amounts of ideas and facts
unbiased reporting and analysis of the facts
Fast response times
Disadvtanges of expert systems
Considerable training to make sure system used correctly
High set up and maintenance costs
Only as good as the facts or information entered into the system
What are the components of an expert system
User interface
Inferenece system
rules base
knowledge base
Describe the user interface in an expert system
method by which the expert system interacts with a user through dialogue boxes, command prompts or other input methods
the questions being asked usually only have Yes/No answers and are based on the responses to previous questions
Explain the inference engine in an expert system
kind of search engine used in an
expert system which examines the knowledge base for information that matches the queries
What are inference rules
rules used by the inference engine and in expert systems to draw conclusions using IF statements
Explain knowledge base in expert system
a repository of facts which is a collection of objects and attributes
how to set up an expert system
»information needs to be gathered from human experts or from written sources
» knowledge base created
» knowledge base populated from collected info
»a rules base needs to be created - series of inference rules so that the inference engine can draw conclusions
» inference engine itself needs to be set up
»the user interface needs to be developed to allow the user and the expert system to communicate
once the system is set up, it needs to be fully tested
How to test a new expert system
running the system with known outcomes so that results can be compared and any changes to the expert system made
3 examples of ML
Categorising email as spam
Recognising user buying history
Detection of fraudulent activity
how does an ML categorise something as spam
» A machine learning algorithm collects data about emails
» It carries out a ‘cleaning’ process by removing stop words
» Certain words/phrases are frequently used in spam and indicate that the incoming email is very likely to be spam.
» The machine learning model is built and a ‘training data set’ is used to train the model and make it learn using past email known to be spam.
» Once it is evaluated, the model is fine-tuned and tested live.
3 main characteristics of AI
collection of data
the rules for using that data
the ability to reason - can include the ability to learn and adapt
