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Artificial Intelligence. What is AI? Issues in AI. An Overview - AI is a science of making intelligent machines - Intelligence is a type of computation : What is a computation?  Turing Machines - How do we know if a machine is intelligent or not ?  Turing Test.

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artificial intelligence

Artificial Intelligence

What is AI?

Issues in AI

An Overview

- AI is a science of making intelligent machines

- Intelligence is a type of computation:

What is a computation?

 Turing Machines

- How do we know if a machine is intelligent or not?

 Turing Test


1. What is AI?

  • Artificial intelligence is the science and engineering of making computer programs that exhibit characteristics of human intelligence.
  • Scientific aim: To understand the requirements for and mechanisms of human, animal, machine, robotic intelligence
  • Engineering aim: To apply such knowledge in building useful artifacts (machines & robots) capable to do things done by humans or animals
What is intelligence?

- Intelligence is the computational part of the ability to solve problems and achieve goals in the world in an efficientmanner (McCarthy)

- ‘Computational part .. to do … efficiently’  Algorithm


Tower of Hanoi Problem:



No. of all possible board states: 10120!!

- Combinatorial explosion problem

- Blind search – intractable

Branches of AI

Knowledge representation

  • Processing information about and representing facts about the world in some abstract way

Pattern recognition

  • Extracting knowledge from images (e.g., letters, face, X-ray data, satellite photos)

Reasoning and inference

  • Deriving conclusions from premises or incomplete observations (e.g., logical deduction, math theorem proving, medical diagnosis, stock market/weather forecasting)

Machine learning

  • Improving performance from experience (e.g., rule induction & adaptive modification)


  • Planning a complex sequence of actions (e.g., playing chess)

Natural language processing

  • Production and interpretation of spoken and written language

Pattern recognition

  • Knowledge representation
  • Reasoning & inference
  • Machine learning
Applications of AI

Computer vision

- IRIS (biometric identification device), detection of forgeries, chip inspection

Expert systems

  • MYCIN (medical diagnosis), HYPO (legal reasoning), auto pilot, intelligent tutoring system

Game playing

  • IBMS’ Deep Blue (search 2m positions per sec)

Speech recognition

- Dragon Naturally Speaking


- robot moles in Mars exploration

Dartmouth Workshop (1956)

- Summer workshop that officially launched the field known as ‘Artificial Intelligence’ (named by McCarthy)

- Participants included: McCarthy (Stanford), Minsky (MIT), Shannon (Lucent), Newell (CMU), Simon (CMU)

General Problem Solver (GPS)

(Newell & Simon, 1960’s)

- Landmark computer program that solves simple problems/puzzles (e.g., Tower of Hanoi) and even comes up with proofs for mathematical theorems

- Based on a general problem solving strategy called the ‘mean-ends analysis’ (work backward from the goal to decide on what action(s) will help you achieve in which goals are decomposed into subgoals in a recursive fashion)

Weak AI vs Strong AI

in the Study of Mind

(Searl 1980)

Weak AI:

- “The principal value of the computer in the study of mind is that it gives us a very powerful tool.”

Strong AI:

- “An appropriately programmed computer literally has cognitive states and therefore explains how the human mind works.”


2. Issues in AI

Issue #1:

What is a computation?

Turing Machines (Turing, 1937)

- A Turing Machine, an idealized, mathematical abstraction of a digital computer, consists of

(1) 1-dim tape of cells of unlimited length

(written on each cell is a symbol from finite alphabet)

(2) read/write head

(3) control (action) table or program

Control program:

- State of head: {S1, S2, S3}

- Binary alphabet on tape: {0,1}

- Movement of head: {Left, Right}

(Turing’s) Definition of computation

- “A function is said to be computable if it can be implemented on a Turing Machine.”

- Such functions are called Turing computable functions

(e.g., f(x) = 0; natural log e; +/x; if-then)

  • Roughly speaking, a function or task is computable if its solution can be found in “finite” time (or polynomial time).
  • A problem in which the time required to solve grows exponentially as the problem size grows said to be uncomputable (i.e., unsolvable), thereby requiring “infinite” time to solve  NP-hard problem

(e.g., Traveling Salesman Problem)


Traveling Salesman Problem

16-city problem

A candidate solution

Universal (Turing) Machine

- Turing also showed that it is possible to design a single Turing machine that can simulate any Turing machine. Such a machine is called a Universal Turing Machine

Church-Turing Thesis:

In essence, “A Universal Turing Machine can compute any non-NP-hard problem.”


- Programmable computers (PC, MaC)

von Neumann Machine

- program – control/action table unit

- CPU – read/write head unit

- RAM - tape

- DNA (biological computation device)

Issue #2:

How do we know if a machine is intelligent or not?

Turing Test (Turing, 1951)

- First attempt to define an operational definition of intelligence

- Turing defined intelligent behavior as the ability to exhibit human-likeperformance, sufficient to fool an interrogator in an “imitation game”

Can the Turing test be a definition of intelligence?!%

1. A computer may pass the test but without ‘real’ understanding of the conversation that took place (e.g., Searl’s Chinese Room)

2. Many ‘real’ human beings might fail the test.

3. A computer often exhibits intelligence without being a conversational partner (e.g., autopilot)

Chinese Room (Searle, 1980)

- Thought experiment developed as an attack on the Turing Test (againt Strong AI)

- Showed that in theory, it is possible to create a system that exhibits intelligent output without understanding (i.e., in the absence of mind), thus passing the Turing test

- Would it be practically possible to build such a system? Why or why not?