CSM10

Intelligent Information Systems

Introductions

Content and coursework

What is intelligence?

CSM10 Spring Semester 2007

Intelligent Information Systems

Professor Ian Wells

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Welcome …

… now introduce ourselves!

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Useful details

• Professor Ian WellsConsultant Computer Scientist, Department of Medical Physics

Royal Surrey County Hospital, Guildford GU2 7XX

• at UniS on Tuesdays only: 19 BB 02

• shares with Dr Terry Hinton + Mr Peter Ainsley

• student hour: Tuesdays from 6pm to 7pm

• email: ian.wells@royalsurrey.nhs.uk

• or: i.wells@surrey.ac.uk (forwarded to above)

• hospital direct line (if important): 01483 464 039

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Web resources

• course web site (new site only!)

• www.cs.surrey.ac.uk/teaching/csm10/

• lecture notes will be posted after lecture

• notices and other handouts

• ULearn (as soon as feasible!)

• training only last week!

• access from hospital uncertain

• how many of you use it and how do you find it?

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Buzzword prevention ...

www-lib.usc.edu/~karl/Bingo/

… make sure you interrupt and ask!!

Philosophy

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• from NHS so: patients students first!

• teach from practical experience over 25 years

• update and enliven lectures each year

• engage despite spectrum of technical preference

• improve 4D expert system shell

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Course history

• from ‘Intelligent Decision Support Systems’

to ‘Intelligent Information Systems’

• 14 students in 2000 to 25 in 2004 to ?? in 2007

• from IS only to IS & IC & maths & EE students

• from broad coverage to greater depth

• from one lecturer (+ guest speakers) to two

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Teaching methods

• two lecturers teaching their prime research subject

• Rule-based systems (Prof Ian Wells)

• Neural networks (Dr Tony Browne)

• lectures, case studies, tutorials and videos

• laptops needed for coursework

• heuristic and dynamic approach

• participation and feedback essential!

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Assessment

• coursework (50%)

• group project and individual report

• terminology and creativity

• focus on rule-based systems

• written examination (50%)

• problem solving

• focus on neural networks

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Deliverables

• understand how humans solve problems

• learn how to replicate this on a computer

• build and evaluate a working expert system

• discuss the wider implications of the concepts

• discover how to ‘think differently’

• and above all have fun!

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Demands

• read the course book!

• then read wider in areas that you find attractive

• participate during the discussions

• feed back both during and after the course

• form yourselves into balanced groups

• start your coursework in good time

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Book review

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Book review

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Book review

Content and conceptsCoursework

Course contentIntroduction to basic concepts

Coursework projectSoftware tools

Report

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Rule-based systems

• introduction, concepts and intelligence

• cognitive processes and problem solving

• semantic networks and production rules

• knowledge representation (in databases)

• software tools (4D), project and reports

• frames, cases, uncertainty and ubiquity

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Coursework

• group development + individual report

• start forming into groups as soon as possible

• dynamics of group composition

• all programmers or no programmers a disaster!

• need coding, content and control

• choice of suitable subject and domain expert

• must be non-trivial and not already attempted

• must be agreed with Prof Wells before work commences

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From CLIPS to 4D

• CLIPS - expert system construction tool

• developed by NASA for internal use

• written in C, portable, free, good books

• http://www.ghg.net/clips/CLIPS.html

• 4D - mid range database development

• modern, powerful and free to students

• http://www.4duk.com/index.html

• Penny expert system shell developed for this course!

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Advantages of 4D

• modern RAD / database software

• used and supported worldwide

• developed in France in 1985

• principles can be used in other environments

• level playing field for all groups

• runtime licence free so all students can install it

• student licence available from 4D UK (? free ?)

• register at www.4duk.com/academic.html

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Penny expert system shell

• written by Lo Farnan as OU MSc project

• version 3 much improved, version 4 shortly

• feedback, bug reports and suggestions please

• download at:

• www.4dcoop.com/penny/pennydownload.htm

• will be updated in response to your input

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What is intelligence?

Case study from medicine:

Are doctors intelligent?

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When did you last see a doctor?

• what processes took place?

• how were the outcomes decided?

• was the doctor intelligent?

• if so - on what grounds?

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A visit to the hospital or GP

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Collecting information

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Physical evidence

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Data or information?

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Interpreting the evidence

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What is taking place?

• medical history (notes and/or computer)

• questions -> history update

• examination -> observations -> information

• application of intelligence?

• what type of reasoning and which direction... ?

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Outcomes ...

• do nothing or play for time

• 80% of patients will get better regardless!

• advice, therapy or referral to specialist

• high volume low yield problem

• doctors spend most of their time circulating information

• … but all the time making decisions

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The problem …

“Medicine is the art of making

acceptable decisions in an imperfectly

understood problem space using

insufficient and often erroneous

information”

With acknowledgment to Edward Shortliffe

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How do doctors reason?

• what type of reasoning and which direction... ?

• deductive approach to diagnosis

• reason backwards from selection of hypotheses

• pure inductive reasoning not used

• working forwards from all the facts

• so intelligence is ... ?

• knowledge + perception + reasoning skills

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Conclusion

Elstein 1978 p 111

“The (medical) deductive process

is not automatic, even though

experienced practitioners

can make it seem to be”

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Early computer model

Strategic LayerStrategic rules

Foreign clauses and rules

User input

Deductive LayerDeductive rules

Management of uncertainty

StaticFacts

DynamicFacts

ConclusionsExplanations

Actions

PROSE: Ahmad & Wells 1985

DeductionsUnknowns

What is intelligence?

Tutorial discussion

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Drive a car Wise Paint a picture

Compose music WealthyControl air

traffic

Perform an operation

Intelligence Win at chess

Minister of religion

University lecturer

Tell a story

Appreciate art Diagnose disease School teacher

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Where to put intelligence?

Wisdom

Knowledge

Information

DataNoise

Value

Volume

Difficulty

Meta-knowledge

Character

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???

Intelligence = appropriate application of knowledge?

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Some definitions

• data

• information

• knowledge

• wisdom

• intelligence

• algorithm

• heuristic

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Some definitions

• data - identifiable and reproducible (not noise)

• information - data understood and in context

• knowledge - apply information to actions

• wisdom - appropriate use of knowledge

• intelligence - knowledge + perception to action

• algorithm - always succeeds but may take too long

• heuristic - quick result but may not be correct

Intelligence ... ?

• ability to understand and learn things

• reason about actions instead of automatic response

• ability to acquire and apply knowledge and skills

• vary state or action in response to current situations and past experience

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Artificial Intelligence (AI) ...

• emulate human intelligence on a computer

• teach computers how to learn and solve problems

• make computers appear less stupid

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Definitions of AI

• Nobel Prize contender - understand intelligence

• computer scientist - make computers faster

• accountant - make computers more useful

• doctor - make computers more helpful

• consumer - make computers more reliable

Detection of AI

• Turing Test

• Alan Turing - statue in front of AP building

• 1950 - article in Computing Machinery and Intelligence

• “if a computer could think how could we tell?”

• Loebner Prize

• $100,000 and gold medal

• held annually since 1990

• see www.loebner.net

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Computeror Human

Human

Judge

Observer

Some buzz words ...

• expert system and expert system shell

• KBS and IKBS - knowledge-based system

• knowledge engineer / engineering

• intelligent or ‘smart’ system or device

• AI and ‘fifth generation’ computing

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Intelligence in information systems

• understanding - the domain

• formulating - the problem

• relating - the problem to the user

• interpreting - the results or outcome

• explaining - questions, reasoning, decisions

• adjusting - questions and responses to suit user

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How What

Shallow knowledge

Millionaire (game show)

Bottom up

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Perceptual model

Environment

Top downExpected features

Deductive

Bottom upFeature analysisInductive

Neisser’s cyclic model of perception

Intelligence

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“Behaviour which is admired but not understood”

Marvin Minsky (c 1963)

Intelligence = Perception + Cognition + Motor Control

Professor Khurshid Ahmad (1999)

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Artificial Intelligence

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Next week ...

What do you do all the time and not realise?

Do you know the connection between women, fire and dangerous things?

Can you solve a problem quicker than a white rat?

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