U1T3.1 - Applications of DT Flashcards Preview

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Flashcards in U1T3.1 - Applications of DT Deck (100)
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1

What is meant by artificial intelligence?

Study of machines to model types of intelligence/creativity of people. Makes machines more capable + helps improve human intelligence + cognitive behaviour.

2

What is cognitive science?

Understanding human mind + its thought processes. Can use computer models of info processing to explain how mind functions.

3

What are some of the applications of AI?

Expert systems, image processing + visions, speech recognition + natural language processing + machine learning.

4

What is the Turing Test?

Used to assess ability of machine to exhibit intelligent behaviour which can't be distinguished from that of a human. Human simultaneously asks questions of computer + human. If person doing test can't tell which is which, the machine has passed and can be classified as intelligent. Convo is through interface, text only. Test doesn't assess correctness, just similarity to human response so criticised.

5

What was the controversy in 2014 about the Turing Test?

Reliability questioned as chatbot called Eugene Goostman passed, designed to imitate 13 year old boy from Ukraine. Some experts discounted the results due to limited vocab of 2nd language young boy.

6

What is a neural network model?

Computational model (software simulation) used in comp science to model primitive brain. Simulates densely interconnected brain cells to support learning, pattern recognition + decision making process as human would.

7

What are the main features of a neural network?

Consists of artificial neurons (units) (50 - millions) depending on complexity of network. Units arranged in layers. Input units receive info from outside world. Series of hidden units (brain) process input from input units. Output units provide response from network, repping info it has learned after input processing. Usually fully connected meaning each unit connected to every other unit in the layers either side. Each connection weighted + algorithms calc weighted sum of any inputs into node to gen output value. Learns by itself.

8

How does a neural network learn?

Patterns of info enter via input units, triggering layers of hidden units until arrive at output units. Not all units fire all the time. Inputs into unit multiplied by weightings of connections they travel along. Weighted outputs added together + if sum above threshold value, unit fires units connected to it. Known as feedforward network.

9

What is backpropogation?

Feedback process used to support learning in neural network. Output produced compared to theoretical output. Diff used to modify connections between various units (go backward from output units, through hidden layers to input units). Over time, this reduces diff between actual + intended output, causing learning to occur.

10

What are expert systems?

Knowledge-based systems. Application of AI. Knowledge of human expert made available through comp package. Narrow knowledge field (knowledge domain of system) Knowledge of human expert programmed into expert system + represented using rules + facts. Creates knowledge base which holds knowledge about domain. (Can be stored as series of IF-THEN-ELSE rules.

11

What is needed to create an expert system?

Subject expert + team of programmers + technical experts (knowledge engineers). Extra rules + facts can be added to the knowledge base as time passes + users provide feedback about answer quality.

12

What is an expert?

Experienced practitioner in particular field.

13

What are the 2 types of knowledge that the knowledge base stores?

Factual, Heuristic.

14

What is factual knowledge?

Factual info acquired by knowledge engineers from human experts, relating to subject domain. Widely agreed type of info.

15

What is heuristic knowledge?

Captures info about accurate judgement + ability to estimate + evaluate. Not just from logic but person's experience. 'Rules of thumb'. Designed to work with uncertainty + simulate producing decisions based on experience.

16

Gives examples of facts in an expert system.

Water boils at 100c, any person over 17 can apply for a driver's licence.

17

Give examples of rules in an expert system.

If water reaches its boiling point, steam will be produced.
Insurance policies for young people tend to be more expensive.

18

What is done once the knowledge base has been created?

A system for consulting it is put into place. (Inference Engine)

19

What is an Inference Engine?

Software which interrogates knowledge base + draws inferences + conclusions based on rules stored about subject domain. Poses questions to user + uses answers provided by user to determine suitable response.

20

Give an example of how an inference engine works?

Qs -> Do you have a cough? Do you have sore ears>
Rules -> IF patient has cough AND sore ears, THEN diagnosis is severe head cold.

21

What does the user interface do in an expert system?

Allows user to communicate with system. Requests for info/advice passed from UI to IE. Processed by IE which applies rules to knowledge base + returns response to user.

22

What is the shell in an expert system?

Piece of software containing structure for creating expert system. 'Empty' expert system w/out knowledge base. Creator can enter appropriate rules + facts to gen expert system.

23

What is fuzzy logic?

Generalisation of standard logic. Used for reasoning about inherently vague concepts like 'tallness, richness, famousness, darkness' Allows computers to manipulate this info.
e.g. X is tall, with a degree of truth of 0.9

24

What is propositional (binary) logic?

Applies only to concepts which are true or false (1 or 0)
e.g. If it's raining, I'll take my umbrella.

25

What is standard logic?

Applies to concepts which can possess a degree of truth between 0.0 and 1.0
e.g. Chance of getting an odd number when I throw a dice is 0.5

26

What are some of the applications of fuzzy logic?

Modeling, evaluation, optimisation, decision making, control, diagnosis + info. Best suited for control systems + applied in areas like breakdown prediction of nuclear reactors, earthquake forecasting + subway control. Hoovers, microwaves + camcorders e.g. hoover applies more suction in dirty area.

27

How is ABS controlled by fuzzy logic?

Consists of parameters like speed, brake pressure, brake temp, interval between applications of brakes + angle of car's lateral motion to forward motion. Range of values continuous, open to interpretation.

28

What fields might expert systems be used in?

Medicine, car engine fault diagnosis + life insurance.

29

How are expert systems used in medicine?

Who treated first, modern drug knowledge, rare condition diagnosis, specialists in different parts of world + checking diagnosis.
Knowledge base = medical info, query = symptoms, advice = diagnosis.

30

How are expert systems used in car engine fault diagnosis?

Car manufacturers create own expert systems about own cars. Stored on comp which can be plugged into car to determine fault + system recommends how to fix problem.