Can Machines Think? Rob Kremer Department of Computer Science University of Calgary.

32
Can Machines Think? Rob Kremer Department of Computer Science University of Calgary
  • date post

    18-Dec-2015
  • Category

    Documents

  • view

    213
  • download

    0

Transcript of Can Machines Think? Rob Kremer Department of Computer Science University of Calgary.

Can Machines Think?

Rob Kremer

Department of Computer Science

University of Calgary

22

IntelligenceIntelligence

What is intelligence???

g

33

IntelligenceIntelligence

Turing Test: A human communicates with a computer via a teletype. If the human can’t tell he is talking to a computer or another human, it passes.

?

g

44

IntelligenceIntelligence

Add vision and robotics to get the total Turing test.

g

55

Weak and Strong AI ClaimsWeak and Strong AI Claims

Weak AI:– Machines can be made

to act as if they were intelligent

Weak AI:– Machines can be made

to act as if they were intelligent

Strong AI:–Machines that act intelligently have real, conscious minds.

Strong AI:–Machines that act intelligently have real, conscious minds.

g

66

What is Intelligence?What is Intelligence?

The Chinese Room

g

77

What is Intelligence?What is Intelligence?

Replacing the brain

g

88

What is AI?What is AI?

g

99

How do we classify research as AI?How do we classify research as AI?

g

1010

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

1111

LearningLearning

Explanation– Discovery – Data Mining

No Explanation– Neural Nets– Case Based Reasoning

g

1212

Learning: ExplanationLearning: Explanation

Cases to rules

g

1313

Learning: No ExplanationLearning: No Explanation

Neural nets

Doctor, doctor, I have a runny nose!

You’re going to die.

*ULP*! Why do you say

that??? No idea. The Neural Net

said so.g

1414

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

1515

Rule-Based SystemsRule-Based Systems

Logic Languages– Prolog, Lisp

Knowledge bases Inference engines

g

1616

Rule-Based Languages: PrologRule-Based Languages: Prolog

Father(abraham, isaac). Male(isaac).Father(haran, lot). Male(lot).Father(haran, milcah). Female(milcah).Father(haran, yiscah). Female(yiscah).

Son(X,Y) ← Father(Y,X), Male(X).Daughter(X,Y) ← Father(Y,X), Female(X).

Son(lot, haran)?

X

g

1818

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

2020

Search: GamesSearch: Games

9!+1 = 362,880 nodes

g

2121

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

2222

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

2323

Ability-Based AreasAbility-Based Areas

Computer vision Natural language recognition Natural language generation Speech recognition Speech generation Robotics

g

2424

Natural Language: TranslationNatural Language: Translation

“The spirit is strong, but the flesh is weak.”

→ Translate to Russian

“Дух сильн, но плоть слаба.”

← Translate back to English

“The vodka is great, but the meat is rancid!”

g

2525

Natural Language RecognitionNatural Language Recognition

g

2626

Natural Language RepresentationNatural Language Representation

“Tom believes Mary wants to marry a sailor.”

g

2727

Approaches to AIApproaches to AI

Learning Rule-Based

Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based

Systems Search Planning Ability-Based Areas Robotics Agents

RoboCup Challenge RoboCup Game

g

2828

Approaches to AIApproaches to AI

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

Learning Rule-Based Systems Search Planning Ability-Based Areas Robotics Agents

g

2929

Agent CommunicationAgent Communication

AliceAlice BobBob

(performative: request, content: attend(Bob,x))Can you

attend this meeting?

(performative: agree, content: attend(Bob,x))Sure...

(performative: inform, content: attend(Bob,x))

I’m here

(performative: confirm, content: attend(Bob,x))Thanks for coming.

(performative: ack, content: attend(Bob,x))(nod)

(performative: ack, content: attend(Bob,x))

(nod)(performative: ack, content: attend(Bob,x))

(nod)

gg

3030

Agent CommunicationAgent Communication

reply-propose-discharge(Alice,Bob,x)

act(Bob,Alice,x)

propose-discharge(Bob,Alice,x)

Alice Bob

request

inform

reply

agree

inform ack

propose-discharge

done

reply

inform ack

reply-propose-discharge

confirm

reply

informack

reply(Bob,Alice,x)

ack(Bob,Alice,x)

ack

ack(Bob,Alice,x)

ack

ack(Alice,Bob,x)

ack

ack(Alice,Bob,x)

ack

g

3131

How far have we got?How far have we got?

Our best systems have the intelligence of a frog

Mind you, how many frogs spend all their intelligence controlling a nuclear power plant? g

3232

Happy Birthday,Shelby!

3333

QuickTime™ and a decompressor

are needed to see this picture.

g

3434

QuickTime™ and a decompressor

are needed to see this picture.

g