Unit 6.3 - AI Flashcards
AI definition
branch of computer science dealing with the simulation of intelligent behaviours by computers
3 characteristics of AI
-narrow
-general
-strong
narrow AI definition
machine has superior performance to a human for one task (can’t think or feel like humans)
general AI definition
machine has similar performance to human doing a specific task (can multi-task like humans)
strong AI definition
machine has superior performance to a human for multiple tasks
5 examples of AI
-news generation
-smart home devices (Alexa, Siri)
-chatbots (Chat GPT)
-autonomous cars
-facial expression recognition
4 main characteristics of AI
-collection of data
-collection of rules for using that data
-ability to reason
(-ability to learn and adapt)
3 examples of narrow AI
-facial recognition
-chatbots
-alphago
expert systems definition
sub-set of AI that can mimic human knowledge, abilities, and experiences. (They use expert knowledge and inference to solve problems)
where are expert systems used? [5]
-diagnosis of patients’ illness
-tax and financial calculations
-strategy games (chess)
-identification of living things and scientific compounds
-oil and mineral prospecting
5 advantages of expert systems
-high accuracy (gives probability of their suggestion being correct)
-consistent results
-can store many ideas and facts
-fast response times
-unbiased reporting and analysis of facts
5 disadvantages of expert systems
-set up and maintenance is expensive
-expert system needs lots of training
-cold responses (might not be appropriate always)
-only as good as facts in the system
-can be wrong
5 parts of the expert system
-user interface
-inference engine
-explanation system
-knowledge base
-rules base
what is user interface and how are the questions asked [4]
method of interaction with user (dialogue boxes, command prompts)
–> questions asked are multiple choice / yes or no
–> questions are based on previous answers
what is inference engine [5]
-processing element
-examines knowledge base for information that matches users’ answers
-gathers information from user by asking questions based on previous answers
-uses inference rules in rules base
-uses rules base to find a match to the user
what is knowledge base [3]
-respository of facts
-collection of objects and attributes
-stores knowledge about an area of expertise, obtained from expert resources
what is rules base [2]
-set of inference rules
-so that (used by) inference engine to draw conclusions
how to set up an expert system [6]
1) information gathered from human experts / written sources / internet
2) gathered information populates knowledge base (first created)
3) rules base is created (series of inference rules)
4) inference engine is set up
5) user interface is developed for communication
6) fully tested by running the system with known outcomes (compare results, changes made)
what is machine learning
subset of AI where a program has the ability to automatically adapt its own processes and/or data due to their past experiences
where is machine learning used? explain how
Search engines
–>if user finds desired website on page 1, success. If user goes to page 2 onwards, it is a failure and changes should be made
Categorising email as spam
1) data is collected about emails (content, headers, senders name, email address, etc..)
2) stop words are removed (the, a, and)
2) frequently used words / phrases indicate that the email is likely spam (lottery, earn, full-refund)
3) machine learning model is built
4) training data set is used to train the model and make it learn using past emails that are spam
5) model is tested live
Recognising user buying history
–>If customer A is interested in same things as customer B, might recommend some other customer A’s shopping things to customer B
Detection of fraudulent activity
1) Data is gathered by survey or web scraping (shopping habits and personal data)
2) unnecessary data is removed
3) algorithm is trained through real examples of customer purchasing behaviour
–> If a customer goes out of buying habits (suddenly buying too expensive things) then high chance of fraudulent activity taking place
4) model is built based on the learning from the training data
5) model is fully tested with known data and outcomes and model is modified if needed
2 differences of AI and machine learning
-AI represents simulated intelligence in machines, ML is machines making decisions without being programmed to
-AI aims to build machines capable of human-like thinking, ML aims to make machines that learn through data acquisition, to solve new problems
