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Artificial Intelligence and its applications Lecture 1 Introduction Dr. Patrick Chan [email protected] South China University of Technology, China 1 Artificial Intelligence (AI) AI was usually found in the Hollywood Movie’s world The Terminator Artificial Intelligence Avengers: Age of Ultrons iRobot Alita: Battle Angel Artificial Intelligence and its applications - Lecture 1: Introduction 2

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Page 1: Artificial Intelligence (AI) · Artificial Intelligence Era AI is everywhere now! 3 Artificial Intelligence and its applications -Lecture 1: Introduction Artificial Intelligence Era

Artificial Intelligence and its applications

Lecture 1

Introduction

Dr. Patrick [email protected]

South China University of Technology, China

1

Artificial Intelligence (AI)

AI was usually found in the Hollywood Movie’s world

The TerminatorArtificial Intelligence

Avengers: Age of Ultrons

iRobotAlita: Battle Angel

Artificial Intelligence and its applications - Lecture 1: Introduction2

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Artificial Intelligence Era

AI is everywhere now!

Artificial Intelligence and its applications - Lecture 1: Introduction3

Artificial Intelligence Era

Sophia, made by Hanson Robotics

World's 1st robot citizen in Saudi Arabia

Artificial Intelligence and its applications - Lecture 1: Introduction4

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AI Impact

Companies invested USD 26 -39B in AI in 2016

AI created USD 3.5-5.8T in value annually in 2018

Artificial Intelligence and its applications - Lecture 1: Introduction5ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER? https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning

AI Impact

Ke Jie(3 – 0)

Sedol Lee(4 – 1)

6

AlphaGo Zerowithout using data from human games, and stronger than any previous version

https://en.wikipedia.org/wiki/AlphaGohttps://deepmind.com/

(2017)

Artificial Intelligence and its applications - Lecture 1: Introduction

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AI Impact

7https://en.wikipedia.org/wiki/OpenAI_Five

OpenAI Five (2018)

Dota 2 Bot

Defeat the professional team twice99.4% win in 42,729 matches with public players

Artificial Intelligence and its applications - Lecture 1: Introduction

AI Impact

8https://www.research.ibm.com/artificial-intelligence/project-debater/live/

“We should subsidize preschool.”• Project Debater (Agree)• Harish Natarajan (Disagree)

Poll Agree Disagree Undecided

Before 79% 13% 8%

After 62% (-17%)

30% (+17%)

8%

58%: Project Debater better enriched their knowledge about the topic compared to Harish’s 20%

15 mins Preparation4 mins Opening statement4 mins Rebuttal2 mins Summary

IBM: Project Debater (2019)

Artificial Intelligence and its applications - Lecture 1: Introduction

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What is Intelligence?

Intelligence

Different meaning to different people

Culture and situation specific

“Intelligence”, your first impression?

Artificial Intelligence and its applications - Lecture 1: Introduction9

What is Intelligence?

When does human intelligence begin?

Human is intelligent, but begins at when?

After graduation?

5 years old?

6 months old?

Before birth?

Artificial Intelligence and its applications - Lecture 1: Introduction10

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What is Intelligence?

Do Animals have Intelligence?

Artificial Intelligence and its applications - Lecture 1: Introduction11

What is Intelligence?

General Discussion

Different in degrees

More / Less intelligent

Intelligence is relative

Intelligence test score 100 is the average of all people currently

Different aspects

Memory

Learning

Thinking

Language

Creativity

Emotion

Perceptual Abilities

Motor Abilities

Artificial Intelligence and its applications - Lecture 1: Introduction12Rolf Pfeifer, Christian Scheier, “Understanding Intelligence”, MIT Press, 1999

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What is Intelligence?

Views from Psychology Experts

In 1921, the Journal of Educational Psychology asked 14 leading expertsin the field to define intelligence

Artificial Intelligence and its applications - Lecture 1: Introduction13Rolf Pfeifer, Christian Scheier, “Understanding Intelligence”, MIT Press, 1999

What is Intelligence?

Views from Psychology Experts

Here are some of them

The ability to carry on abstract thinking (L. M. Terman)

Having learned or ability to learn to adjust oneself to the environment (S. S. Colvin)

The ability to adapt oneself adequately to relatively new situation in life (R. Pintner)

The capacity to acquire capacity (H. Woodrow)

The capacity to learn to profit by experience (W. F. Dearborn)

Most of them hold that intelligence is the ability to solve a problem

Artificial Intelligence and its applications - Lecture 1: Introduction14Rolf Pfeifer, Christian Scheier, “Understanding Intelligence”, MIT Press, 1999

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What is Intelligence?

Summary

Artificial Intelligence and its applications - Lecture 1: Introduction15

Artificial Intelligence (AI)

A computer has human intelligence

Solve the problem by themselves

Artificial Intelligence and its applications - Lecture 1: Introduction16

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Artificial Intelligence (AI)

Automation

Completely relieve human beings of repetitive or dangerous tasks

Undertake intelligent analysis of huge amount of information

Enrich entertainment

animation, digital camera & TV

User-Friendly

Aware of users’ needsArtificial Intelligence and its applications - Lecture 1: Introduction17

AI Development

AI started from 1956, coined by American computer scientist John McCarthy

“Long” history of development

Artificial Intelligence and its applications - Lecture 1: Introduction18

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Can AI become HI?

How to evaluate an intelligent computer (machine intelligence)?

– The most famous method is

called Turing Test– This test was invented by

Alan M. Turing• English mathematician, logician

and cryptographer

• 1912-1954

Artificial Intelligence and its applications - Lecture 1: Introduction19

Can AI become HI?

Turing Test

Two contestants: Machine and Human

A human judge will talk with the two contestants and decide which is human, and which is machine

To keep it fair, the conversation is usually text-based, (like instant messaging service)

If the judge is less than 50% accurate, the computer passes the test

Artificial Intelligence and its applications - Lecture 1: Introduction20

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Can AI become HI?

Turing Test

Many algorithms achieves satisfying results in chatting with human

E.g. Voice assistant Cortana, Siri, Google Now,

Blackberry Assistant

Do they really understand you?

Are they really intelligent?

Artificial Intelligence and its applications - Lecture 1: Introduction21

Can AI become HI?

Turing Test: Digger wasp

When the female wasp brings food to her burrow, she deposits it on the threshold, goes inside the burrow to check for intruders, and then if the burrow is clear, the wasp brings in the food

Is this behavior intelligent?

Artificial Intelligence and its applications - Lecture 1: Introduction22

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Can AI become HI?

Turing Test: Digger wasp

If the experimenter moves the food away from burrow while the wasp is inside the burrow checking, what will happen?

The wasp repeats the entire procedure again and she can be made to repeat this cycle of behavior forty times

Obviously, it is a meaningless rule-based action

Is the wasp as intelligent as you think?

Artificial Intelligence and its applications - Lecture 1: Introduction23

Can AI become HI?

Turing Test

“Look intelligent” means real intelligence?

Maillardet’s Automaton (Henri Malliardet, 1805)

Draw several complex images

Artificial Intelligence and its applications - Lecture 1: Introduction24

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Can AI become HI?

The Chinese Room

In 1980, American philosopher John Searleargued convincingly that a computer can never be truly intelligent even it passes the Turing testbecause it is never able to understand anything

He illustrated the argument using “the Chinese Room”

Artificial Intelligence and its applications - Lecture 1: Introduction25

Can AI become HI?

The Chinese Room

Searle placed himself in an imaginary locked room

Through a slot in the door of the room, he was fed a Chinese question

• He passed out the correctanswer in response to the question, by following a complex set of instructions

• It appeared to people outside the room that he understood Chinese but he didn’t!

Artificial Intelligence and its applications - Lecture 1: Introduction26

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Can AI become HI?

The Chinese Room

Searle claims that two kinds of AI

Weak AI

Perform tasks similar to what human will do

No understanding behind the task e.g., emotion, rationale, motivation, background

Strong AI

Able to think and possess understanding (a mind)

Searle claims that strong AI is impossible

Artificial Intelligence and its applications - Lecture 1: Introduction27

Can AI become HI?

Specific Problems

Handling general tasks not easy for AI

Smaller problems are more achievable

E.g. hand-writing recognition and game-playing

Outcomes: Optimal: Not possible to perform any better

Strong super-human: Performs better than all humans

Super-human: Performs better than most humans

Par-human: Performs similarly to most humans

Sub-human: Performs worse than most humans

Artificial Intelligence and its applications - Lecture 1: Introduction28http://en.wikipedia.org/wiki/Progress_in_artificial_intelligence

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Can AI become HI?

In Weak AI sense, Yes

Act like human

Useful computer systems to solve specific task

Some successful results have been achieved

In Strong AI sense, unknown…

Think like human

Artificial minds

The debate is still raging on

Artificial Intelligence and its applications - Lecture 1: Introduction29

AI Development

Artificial Intelligence and its applications - Lecture 1: Introductionhttps://medium.com/future-today/understanding-artificial-intelligence-f800b51c767f

30

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AI Development

Artificial Intelligence and its applications - Lecture 1: Introductionhttps://atos.net/en/artificial-intelligence

31

Human Involvement

Powerful (Knowledge & Ability)

AI vs Machine Learning (ML)

ML is powerful but not suitable for all applications

Everything can learn from data??

Artificial Intelligence and its applications - Lecture 1: Introduction32

Artificial Intelligence

Machine Learning

Ability of a machine to think / act like humans do

E.g. Problem solving, reasoning, control, etc.

A machine to learn from examples without

being explicitly programmed

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Syllabus

1. Introduction

2. Search (Problem Solving)

3. Constraint Satisfaction Problems

4. Game playing

5. Markov Decision Processes

6. Reinforcement Learning

7. Supervised Learning Introduction

8. Artificial Neural Network

9. Other Classifiers and Ensemble

10.Deep Learning

11.Recommender System

12.Unsupervised LearningArtificial Intelligence and its applications - Lecture 1: Introduction33

Non-Machine Learning

Machine Learning

Syllabus: Part 1

Searching & CSP

Searching

Common technique in AI

E.g. Planning, Maze

An agent finds the action sequence which leads to a goal state with minimal cost

Constraint Satisfaction Problems

Find a solution satisfying constraints

Artificial Intelligence and its applications - Lecture 1: Introduction34

S E

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Syllabus: Part 1

Game Playing & MDP

Game Playing

Competition with an adversary

Game opponent acts according to your decision

Markov Decision Processes

Consider an uncertainty of the environment

Artificial Intelligence and its applications - Lecture 1: Introduction35

Syllabus: Part 1

Reinforcement Learning

Learn from a reward or punishment, but not a teacher

Design a policy according to consequences of a sequence of actions

ReinforcementEncourage an action

PunishmentDiscourage an action(Negative Reinforcement)

Artificial Intelligence and its applications - Lecture 1: Introduction36

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Syllabus: Part 1

Reinforcement Learning

An agent learns by interacting with the environment

Agent takes action and receives feedback in the form of rewards

No supervisor (to tell you right or wrong) but only reward

Artificial Intelligence and its applications - Lecture 1: Introduction37

Action (At)State (St)

Reward (Rt)

Environment

Agent

Syllabus: Part 1

Reinforcement Learning

Example

Artificial Intelligence and its applications - Lecture 1: Introduction38

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Syllabus: Part 1

Reinforcement Learning

Artificial Intelligence and its applications - Lecture 1: Introduction39

Search

Game Playing

Markov Decision Processes

Reinforcement Learning

Dif

fic

ult

y

Constraint Satisfaction Problems

• From start state to goal state

• Consider constraints

• Consider an adversary

• Consider an uncertainty

• No information is given

Syllabus: Part 2

Machine Learning

What if a machine can learn…

Artificial Intelligence and its applications - Lecture 1: Introduction40

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Syllabus: Part 2: Machine Learning

Plenty of Data

Source: International Data Corporation

Bytes B (1 Bytes)

Kilobyte KB (1 000 Bytes)

Megabyte MB (1 000 000 Bytes)

Gigabyte GB (1 000 000 000 Bytes)

Terabyte TB (1 000 000 000 000 Bytes)

Petabyte PB (1 000 000 000 000 000 Bytes)

Exabyte EB (1 000 000 000 000 000 000 Bytes)

Zettabyte ZB (1 000 000 000 000 000 000 000 Bytes)

Zettabyte

"all words ever spoken by human beings" could be stored in approximately 5EB

40ZB = 40 000 EB = 8 000 x 5 EB

40 ZB

41 Artificial Intelligence and its applications - Lecture 1: Introduction

Syllabus: Part 2: Machine Learning

Plenty of Data

The Internet provides a platform for Big Data

Collection

Storage

Sharing

Processing

Artificial Intelligence and its applications - Lecture 1: Introduction42

IoT

MobileDevices

Sensors

SocialMedia

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Syllabus: Part 2

Machine Learning

Artificial Intelligence and its applications - Lecture 1: Introduction43

Supervised Learning

Correct / Wrong

UnsupervisedLearning

No ground truth

ReinforcementLearning

Learn from reward

Syllabus: Part 2

Supervised Learning

Ground truth (desired output) is provided

A sample (x, y)

x: a feature vector

y: a desired output (e.g. label, value, …)

Learn the mapping between x and y

Predict y for an unseen x

Error can be measured explicitly

Artificial Intelligence and its applications - Lecture 1: Introduction44

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Syllabus: Part 2

Supervised Learning

Classification y is a label of the sample

E.g. x = (Length, Weight)y = Seabass or Salmon

Artificial Intelligence and its applications - Lecture 1: Introduction45

Seabass Sample

Salmon Sample

Unseen Sample?

We

igh

t

Length

?

?

?

Syllabus: Part 2

Supervised Learning

Regression y is a real number

E.g. x = (Length)y = Price of a fish

Artificial Intelligence and its applications - Lecture 1: Introduction46

A sample

Length

Price

?

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Syllabus: Part 2

Supervised Learning

Example

Artificial Intelligence and its applications - Lecture 1: Introduction47

(Classification)

Class

A bounding box- Size and Coordination

- Class

(Regression)

(Classification)

For each bounding box- Size and Coordination

- Class

(Regression)

(Classification)

For each bounding box- Size and Coordination

- Class

- Which pixel is background?

(Regression)

(Classification)

(Classification)

Syllabus: Part 2

Unsupervised Learning

Only x is available

No desired output (y) is given

Find relation/structure/speciality of data

Never know how good your results are

Evaluation base on an assumption

Artificial Intelligence and its applications - Lecture 1: Introduction48

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Syllabus: Part 2

Unsupervised Learning

Clustering

Outlier Detection

Artificial Intelligence and its applications - Lecture 1: Introduction49

We

igh

t

Length

With labelled informationW

eig

ht

Length

Without labelled information

Outlier

Syllabus: Part 2

Unsupervised Learning

Example: Customer Segmentation

Artificial Intelligence and its applications - Lecture 1: Introduction50

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Syllabus: Part 2

Deep Learning

Artificial Intelligence and its applications - Lecture 1: Introductionhttp://www.erogol.com/brief-history-machine-learning/

51

Perceptron

Decision Tree

2006

Deep Learning

Syllabus: Part 2

Deep Learning

Deep Learning, means Artificial Neural Network with a deep structure

Artificial Intelligence and its applications - Lecture 1: Introduction52

Simple Neural Network

Deep Neural Network

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Syllabus: Part 2

Deep Learning

Features does not rely on experts anymore

Artificial Intelligence and its applications - Lecture 1: Introduction53

Person ExpertDesignedFeatures

. . .

Classification

Who?

Decision

Feature Extraction + ClassificationPerson

Who?

Decision

DeepLearning

TraditionalLearning

Future AI

Truly Conscious

Can we create a very complex and powerful computer which has consciousness?

Is it necessary?

Who is responsible for the decisions made by AI?

Is ethics part of our consideration when dealing with AI?

Artificial Intelligence and its applications - Lecture 1: Introduction54

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Is AI Irreversible Trend?

When and where is this education?

Artificial Intelligence and its applications - Lecture 1: Introduction55

Her parents too poor? No computer game? Where are the Laser guns?

Only pencil and paper? Boys doing sewing? How can they earn a living?

Is AI Irreversible Trend?

Waldorf School of the Peninsula in California Silicon Valley

75% of the school parents are managers or executives from Silicon Valley Hi Tech companies

Artificial Intelligence and its applications - Lecture 1: Introduction56

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Is AI Irreversible Trend?

Bill Gates didn’t let his children get a cell phone until they reached 14

Steve Jobs won’t let his kids use iPad

How about us?

Artificial Intelligence and its applications - Lecture 1: Introduction57

Humility is beginning of Wisdom

Revelation

Wisdom

Knowledge

Information

Data

Artificial Intelligence and its applications - Lecture 1: Introduction58

Artificial Intelligence

Human Intelligence