Week 06

Question 1

What does GIS Stand for?

Answer 1

Geographic Information Systems

Question 2

What are GIS’s? with examples

Answer 2

Deal with Geospatial Data - data that identifies the geographic location of features. Typically coordinates

Ex of spatial databases available:
Ordanace Survey
openstreetmap
Google Maps

Question 3

What do spatial techniques do

Answer 3

Identify trends not obvious from other forms of analysis.

Ex: John Snows work in 1854 on the cholera spreading and identifying the water pump at the epiccentre of the disease.

Question 4

What are the different types of spatial autocorrelation

Answer 4

Positive spatial autocorrelation

No spatial autocorrelation

Negative spatial autocorrelation

Question 5

What is choropleth mapping

Answer 5

Display of attribute (non spatial) data associated with spatial entities.

Easier to see paterns.

Question 6

What are spatial operations

Answer 6

Identify spatially related objects Ex: Beside, enclosed within, within a radius of

Question 7

What is a Buffer?

Answer 7

Buffers are bands around spatial objects.

e.g. buffer around an airport might identify areas subject to noise

Question 8

Why is buffer method not very sophisicated?

Answer 8

Circles are not overly accurate - as often drawn as the crow flies rather than travel distance.

Question 9

Who are the traditional GIS users & list the types of spatial decision making they use?

Answer 9

Traditional users:
Utilities- EBS
Forestry- Coillte
Government

Spatial decision making:
location analysis
transport/logistics decisions
Marketing
Insurance

Question 10

What are Insurance Uses of GIS

Answer 10

• Existing policies. Show who and where has policies, what is protected, and for which sum.
• Firmographics. This type shows which
  businesses are located side by side. e.g. is a bookshop near a fire station or a firework shop.
• Historical loss information. Spatial record of all losses for the entire observation period.
• Location risk index. For customers, each Location features a specific risk index.
• Natural disasters. fires, floods, tornadoes, earthquakes, etc

Question 11

What is AI?

Answer 11

Behaviour which if performed by a human would be considered intelligent.

Question 12

What are the three task domains of AI? - What will be the most difficult for computers to adjust to

Answer 12

Expert Tasks, Mundane tasks (most difficult for computers) and formal tasks.

Question 13

Give an example of expert tasks as part of the task domains of AI?

Answer 13

Engineering, medical diagnosis, financial analysis

Question 14

Give an example of mundane tasks as part of the task domains of AI?

Answer 14

Perception, robotics, common sense reaosning

Question 15

Give an example of formal tasks as part of the task domains of AI?

Answer 15

Natural Language, Mathematics, Games

Question 16

Explain AI techniques under accessing existing knowledge

Answer 16

• Expert systems
  – performs a task that would otherwise be performed by a human expert
  – reasoning approach
  – “storage” of pre-established knowledge
• Case Based Reasoning
  – What did we do last time?

Question 17

Explain AI techniques under creating new knowledge idea

Answer 17

Machine learning - system itself updates knowledge.

Question 18

Explain what is an expert system?

Answer 18

A computer program that emulates the behaviour of human experts who are solving real problems associated with a particular domain of knowledge.

Not used by experts, but stores expert knowledge. – Only in very specific detailed areas

Question 19

What are the benefits of expert systems?

Answer 19

• faster decisions – foreign exchange dealing
• better decision quality
• capture of scarce expertise – limited number of experts and an expert may not be in correct place
• integration of several opinions
• cheaper control devices
• spread of knowledge
• reliability

Question 20

What are some problems with expert systems?

Answer 20

• Getting knowledge from expert is hard – Too busy or they may not understand the nature of the information required
• System is frozen in time – No systematic updating
• Expert rules can be inflexible

Question 21

Explain what is case-based reasoning and what are steps required for CBR.

Answer 21

Solving problems automatically by doing what you did last time - but no problem is exactly the same.
CBR learns so long as main problem does not change

Steps:
1.Case base describing problems and solution
2.Match best previous examples
3.Then modify the solution to suit the current problem
4.If solution works add it to the case base

Question 22

What are the four r’s of CBR cycle?

Answer 22

Retrieved (similar case)
Reuse (Adaption solution)
revise (verify it works)
Retain (learning)

Question 23

Explain CBR in Insurance

Answer 23

Underwriting Decisions:
– If a new policy proposal is like previous examples, then it can be routinely accepted.

• Claims Handling:
  – Refer to similar previous examples
• Pricing and Premium Setting:
  – Previous examples can be a starting point for calculations
• Fraud Detection
• Regulatory compliance
  – If a case is similar to a previously compliant one

Question 24

What is Machine Learning?

Answer 24

Machine learning is an approach to the field of Al Systems trained to recognize patterns within data to acquire knowledge

Question 25

How does machine learning process differ from traditional programming process?

Answer 25

Traditional: Computer takes in data and programs and produces an output Machine learning: Takes data and outputs and produces a program

Question 26

Explain difference between machine learning and statistics

Answer 26

Statistics – Based on mathematical principles – Requires data to meet requirements for those techniques – Prediction less important than understanding Machine Learning – Based on what works – Solutions proposed from training datasets – Solutions validated by test datasets – More flexible in data input – Prediction important, understanding less so

Question 27

Explain supervised learning

Answer 27

Provide ML algorithm with curated data set

Question 28

What are the steps to supervised learning?

Answer 28

Classification: Predicting to which discrete class an entity belongs Regression: Predicting continuous values of an entity’s characteristic. Forecasting: Estimation of macro (aggregated) variables such as total monthly sales Attribute Importance: Identifying the variables (attributes) that are the most important for prediction

Question 29

Explain unsupervised learning?

Answer 29

Uses machine learning algorithms to analyze and cluster unlabeled datasets.

Question 30

Explain the steps in unsupervised learning

Answer 30

Clustering: Finding natural groupings in the data. Association models: Analysing “market baskets” (e.g. combinations of the products that are bought together) – Checking for Correlation without explanation

Question 31

What are issues with machine learning?

Answer 31

* Garbage in/ Garbage out. – You have the data, but is it quality data? – Previous processing of data may create bias. * You still need large computing power – Cost and energy consumption * Results cannot be easily explained * If circumstances change then your model may be useless.

Question 32

What jobs will have increased chances of automation

Answer 32

Higher skilled jobs have less risk while lower skilled jobs have a very high chance of being redundant

Question 33

Why do we often used combined systems of AI?

Answer 33

Combination of approaches is the best -Combining human rules with automated rules -Using machine learning to establish similarity in case based reasoning Business analytics - focus is not on the techniques but on the business

Question 34

Insurance uses of GIS

Answer 34

* Existing policies. Show who and where has policies, what is protected, and for which sum. * Firmographics. This type shows which businesses are located side by side. e.g. is a bookshop near a fire station or a firework shop. * Historical loss information. Spatial record of all losses for the entire observation period. * Location risk index. For customers, each location features a specific risk index. * Natural disasters. fires, floods, tornadoes, earthquakes, etc

Question 35

Advantages of ML

Answer 35

Identification of trends and patterns * Largely automatic, with limited human work needed * Modern technology – Now large amounts of data that can be used – Storage and cloud computing are cheap – Cloud computing offers ML models