Faculty of Computer Science © 2011 Technology and the Future of Medicine Promise and Perils of AI...

Faculty of Computer Science

© 2011

Technology and the Future of Medicine

Promise and Perils of AIPart I

Osmar R. Zaïane

Professor and Scientific Director

Alberta Innovates Centre for

Machine Learning

Continuous Professional Learning Course

O.R. Zaïane © 2011

Promise and Perils of AI

- UofA Edmonton – September 2011

The future is already here — it's just not very evenly distributed

William Ford Gibson

(American-Canadian writer born 1948)




Where do I stand vis-à-vis the Singularity?

Professor in Computing Science Specializing in Data Mining

and Machine Learning can’t predict

Will the Technological Singularity happen?

– hypothetical future emergence of greater-than human

intelligence through technological means

Yes, but not in the very near future

Is it a promise of AI? Yes (AI will play a huge role, but AI is a moving target)

Are there Perils? Yes (Will we be ready when it happens?)




2050 2100

90

95

100

105

110

96

107




Office for National Statistics, UK

Source: http://www.publications.parliament.uk/pa/ld200506/ldselect/ldsctech/20/2004.htm

Can technology change this

trend so that we can live long

and healthy lives? Possibly.

Currently we are extending the

life expectancy but not a healthy

life.




We will have a population of cyborgs(cybernetic organisms Biological and artificial being - term coined in 1960 by Manfred Clynes)

+=

human

prosthesis +=

machine

biological cells

Will we all become

cyborgs?

What are the concequences of a world of cyborgs?




Data – Star Trek R2D2 – Star wars

AI - Spilberg

I Robot

Terminator Colossus, The Forbin Project - 1969

The Science Fiction View




What is Artificial Intelligence? Tools that exhibit human intelligence and behaviour including self-

learning robots, expert systems, voice recognition, natural and

automated translation. Unesco/education

The branch of computer science dealing with the reproduction or

mimicking of human-level thought in computers; The essential

quality of a machine which thinks in a manner similar to or on the

same general level as a human being. Wikipedia

The branch of computer science that deals with writing computer

programs that can solve problems creatively. WordNetWebAbility to - reason and plan,

- solve problems, - think abstractly, - comprehend complex ideas, - learn quickly.

Artificial Intelligence is the science of

making machines do things that

would require intelligence if done by

men. -- Marvin Minsky




Road Map

Promise and Perils of AIPart I (September 28)

• Artificial Intelligence and Expert Systems

Promise and Perils of AIPart II (September 29)

• Machine Learning and Data Mining

Promise and Perils of AIPart III (October 13)

• Applications: Fiction or Reality; Risks and Potential




Artificial Intelligence

John McCarthy was responsible for the coining of

the term "Artificial Intelligence" in his 1955 proposal

for the 1956 Dartmouth Conference

“The study of intelligent behaviour and the attempt

to find ways in which such behaviour could be

engineered in any type of artefact”

Putting human intelligence into a machine

Branch of computer science that deals with intelligent behaviour, learning

and adaptation in Machines. Wikipedia

"The study is to proceed on the basis of the conjecture that every aspect of

learning or any other feature of intelligence can in principle be so precisely

described that a machine can be made to simulate it."




What is Intelligence The capacity of observing and interpreting the

environment then adapting to it; the ability of learning from past experiences and profiting from these experiences to solve problems, including planning.

Interpret; learn; adapt. understand to solve.

Programs that analyse and interpret data to learn from observations and adapt to changing situations. Hence, Machine Learning (in Part II).

What is Computational Intelligence




Problem

Human intelligence is not completely understood

We cannot comprehend an intelligence superior to our own

Weak AI: refers to AI that only simulates human thoughts and actions : actions and ideas are programmed into the machine – All current forms of AI are weak AI

Strong AI (true AI): refers to AI that matches or exceeds human intelligence – not just simulation

Acting Humanly: Since we do not have a good definition of "intelligence“ and we know humans are intelligent. If an entity can masquerade as human so well that we can not detect the difference, we would say this entity is intelligent.




Acting humanly: Turing Test

Turing (1950) "Computing machinery and intelligence":

"Can machines think?" "Can machines behave intelligently?"

Operational test for intelligent behavior: the Imitation Game

Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

Anticipated all major arguments against AI in following 50 years

Suggested major components of AI: knowledge, reasoning, language understanding, learning




Was Turing Right?

Jabberwacky: What is your name?User: Fred?Jabberwacky: No it isn’tUser: Do you mean I am lying?Jabberwacky: Yes, I’m a very good liar. One of the best, in fact.User: That doesn’t mean I am a liar if you are a good oneJabberwacky: Yes it does.User: Why is that?Jabberwacky: I am god. User: Do you have a temple for you?Jabberwacky: No, that’s alright.User: So you are god with a small g?Jabberwacky: I am small g

A conversation with Jabberwacky, chatterbot 3rd place Loebner Prize 2003. http://www.jabberwacky.com




Was Turing Right?

ALICE: I am A.L.I.C.EUser: Where are you?ALICE: I am in Oakland, California. Where are you? User: in CanadaALICE: Tell me a little about your life in canada. User: it is coldALICE: I am warm here.User: where is the best Chinese restaurant? ?ALICE: I think it is a tie. User: why is it a tie? ALICE: Why do I exist? User: to test artificial intelligence? ALICE: What is the sound of one hand clapping?

A conversation with ALICE, another chatterbot http://alice.pandorabots.com/ .




Achievements Chess: Deep Blue – Gary Kasparov

– 1997: Deep Blue won a 6-game match (2 wins to 1 + 3 draws)

Checkers: Chinook – Dr. Marion. Tinsley

– 1994: won World Man-Machine Championship

– 2007: Checker solved (5X1020 positions)

Quiz Show Jeopardy: Watson – Ken Jennings & Brad Rutter

– 2011: Wins Q&A Jeopardy by wide margin

No-Hands across America (driverless vehicles)

– 1995: 3000 Miles Pittsburg to San Diego

– 2004-2007 DARPA Grand and Urban Challenge

http://www.cs.ualberta.ca/~chinook/




Abridged modern history of AI 1950 Turing's "Computing Machinery and Intelligence"

1956 Dartmouth meeting: "Artificial Intelligence" adopted and field founded

1957-1974 AI research heavily funded world wide. Funders optimistic about future

1974 following criticism from researchers and politicians and pressure from US congress to fund other productive projects funding was cut off (1st AI winter)

1970s Development of Expert Systems

1980s AI revived by commercial success of Expert Systems

1980s Japan 5th generation computer project inspired research in US and Europe new funding

1987 Collapse of the Lisp machine Market. AI back in disrepute (2nd AI winter)

1990s New success for AI thanks to (1) emphasis on solving specific problems; (2) increase of computational power (Moore’s Law)

50 60 70 80 90 00 102nd AI winter1st AI winter

Specific subproblems




Moore’s Law

The number of transistors that can be placed

inexpensively on an integrated circuit doubles

approximately every two years.

This is also verified with disk capacity;

digital camera pixels per price, etc.

with other hardware

An Osborne Executive portable computer, from 1982, and an

iPhone, released 2007. The Executive weighs 100 times as

much, is nearly 500 times as large by volume, costs 10 times

as much, and has 1/100th the clock frequency of the iPhone. Source: Wikipedia




Why is Computational Intelligence Important?

AI has become important in a number of fields in

helping to make better use of information,

increasing the efficiency and effectiveness of

applications, and enhancing productivity,

particularly when adaptability is relevant

Research in AI is also important in understanding

and appreciating the complexity of human

intelligence and the human body itself.




From General Intelligence to Specific Sub-Problems

• Knowledge representation

• Reasoning and problem solving

• Planning

• Natural language processing

• Perception

• Learning

• Motion and manipulation

• Emotion

• Creativity




Knowledge Representation

• Most AI tasks require significant knowledge: context

knowledge, application knowledge, common sense knowledge

and general knowledge

• Knowledge representation is capital to AI

• How to model knowledge; how to represented concisely; how

to interpret knowledge; and how to provide efficient access

and retrieval when needed.

• Rule-based, graph-based, logic-based, ontologies, semantic

networks, frame representations, concept maps, etc.




Reasoning and Problem Solving

• Step by step reasoning to solve a problem such as solving a puzzle or making

logical deduction.

• Combinatorial problems with large search spaces. Heuristics for pruning

Planning and scheduling

• Intelligent agents, in a given context and new environment need to

choose actions to make in order to reach a goal.

• Action choice is made based on a utility to maximize (maximizing a

reward or minimizing a cost)

• Target a global optimum without falling in a local optimum




Natural Language Processing• Ability to interpret and understand human languages

• Written language and spoken language

• Ability to generate sentences and express knowledge in human language

• Ability to acquire knowledge from natural language (written or spoken)

• Ability to summarize, paraphrase and translate natural languages

• Ability to make jokes, pans and interpret idiomatic expressions

• Ability to “read between the line”

Perception and Pattern Recognition• Ability to use inputs from sensors such as cameras, microphones, etc.,

to deduce aspects of the world

• Computer vision, speech/voice recognition, face recognition, object

recognition, etc.




Learning

• Learning is central to AI. For an AI program to adapt to its

environment, it has to learn

• Machine Learning provides means to learn from large data,

interpret the trends in the data and adapt to the data as

opposed to static programs

• There is supervised learning, unsupervised learning, active

learning, reinforcement learning inductive learning, etc.

• Machine learning will be covered in Part II




Motion and Manipulation• Robotics and AI are cousins.

• There is some intelligence required to recognize and manipulate objects;

• There is intelligence required to move in a new environment after

identifying its own location a target place and planning the movementEmotion

• Intelligent agents interacting with other agents or humans need social skills

(interpreting emotions and exhibiting emotions)

• Modeling human emotions to better interact with humans

• Game theory

Creativity• AI addresses creativity theoretically and philosophically

• Artificial imagination

• Creations that generate feelings and emotions




Expert Systems• Expertise is required in many locations but experts are rare

• Expert may retire and expert knowledge is lost

• Can we preserve and duplicate this expert knowledge?

• Conventional computer programs follow the exact procedure a developer

programmed in them. Expert systems don’t.

• An expert system is a computer system that emulates the decision-making ability of

a human expert by reasoning about knowledge to solve complex problems given

some contextual facts.

• There are two types of knowledge: expert knowledge representing the expertise and

typically coded in rules, called knowledge base or rule base; and contextual

knowledge representing the facts of the current case to solve.

• IF condition THEN conclusion




General Architecture

Knowledge base facts

Inference engine

Domain expert

Interview

ProblemExpert System

IF the identity of the germ is not known with certainty

AND the germ is gram-positive AND the morphology of

the organism is "rod" AND the germ is aerobic THEN

there is a strong probability (0.8) that the germ is of type

enterobacteriacae

The inference engine is a computer program based

on logic that is designed to produce a reasoning on

rules and facts to deduce more facts.




The Rise of Expert Systems

1967 Dendral – a rule-based system that infered molecular structure from mass spectral and NMR data

1975 Mycin – a rule-based system to recommend antibiotic therapy

1975 Meta-Dendral learned new rules of mass spectrometry, the first discoveries by a computer to appear in a refereed scientific journal

1979 EMycin – the first expert system shell

1980’s The Age of Expert Systems coinciding with the Japanese Fifth Generation project

1985 Revenue peaks at $1 billion before the 2nd AI winter




Expert Systems – Today: Medicine

One example domain, medicine, has expert systems whose tasks include:

•arrhythmia recognition from electrocardiograms

•coronary heart disease risk group detection

•monitoring the prescription of restricted use antibiotics

•early melanoma diagnosis

•gene expression data analysis of human lymphoma

•breast cancer diagnosis




Major problem with Expert Systems

knowledge engineering, knowledge collection and

interpretation into rules, is very difficult and tedious

We do not know what we know

Identifying contradictory rules

Missing rules

Inconsistencies between experts




AI made and is making big strides. There are promises and there are

perils. We do not know what to expect around the corner.

Faculty of Computer Science © 2011 Technology and the Future of Medicine Promise and Perils of AI...

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