Artificial Intelligence

Module 11

After this module, you will be able to… Explain the details of the Turing Test Explain counter arguments to the Turing Test List and describe several AI techniques Understand the implications of AI

What is “intelligence”? from the dictionary: “The ability to learn from

experience, to reason, and to think.”

Can computers do this, or be made to?? and if they can, does that make them intelligent??

Can computers be intelligent?

The basic metaphor mind = software brain = computer

Mind vs. software human minds receive input, generate output, and

process and remember information so does software taking this metaphor seriously means building

software to emulate minds

Intelligence in a computer

Processing power and speed can only estimate the processing power of the

average human brain (at this time) best guess: nerve volume to be proportional to

processing power retina processes about ten one-million-point images per

second 1,500cc human brain is about 100,000 times as large as

the retina, therefore average brain about 100 million MIPS (Million computer Instructions Per Second ) while recent super-computers have only a few million MIPS worth of processor speed

Brain versus computer

Memory (have you ever ‘run out’ of memory?) best guess

calculating using the number of synapses connecting the neurons in the human brain and estimate each of them to be capable holding one megabyte worth of memory. Since the brain has 100-trillion-synapses, we can estimate that the average brain can hold about 100 million megabytes of memory (100 terabytes) Versus: computer: 2 GB RAM, 300 GB Harddrive 1 terabyte = 1024 Gigabytes (GB) Terabyte Harddrive: $600 US

Brain versus computer

Performance and ability a matter of programming?

The brain is made for general purposes, not specifically just for computational jobs in theory, the brain could be as quick as a

computer in computational and recording jobs but the average human is constantly distracted by their senses, emotions & thoughts

Brain versus computer

Adaptability and learning the human brain is able to learn by trial and error,

induce conclusions from past experience and create new methods to deal with the situations - highly adaptable!

Time to learn however, the human brain requires time to adapt

to the behaviors required when learning a new skill or quitting a old habit

The computer is getting better every year - don’t forget Moore’s Law!

Brain versus computer

Artificial intelligence is the study of ideas which enable computers to do the things that make people seem

intelligent.

—Patrick Henry Winston, in Artificial Intelligence

Artificial intelligence is the study of how to make computersdo things at which, for the moment, people are better.

Artificial intelligence is the study of the computationsthat make it possible to perceive, reason, and act.—Patrick Henry Winston, in Artificial Intelligence

Artificial intelligence is the study of those things we don’t yet know how to make the computer do.

Defining Artificial Intelligence

A machine may be deemed

intelligent when it can

pass for a human being in

a blind test.

—Alan Turing

The Turing Test

How do we measure if a computer is intelligent?

Turing proposes that If a computer acts intelligently then it is intelligent. If it walks like a duck, quacks like a duck and flies like a

duck, it has to be a duck

Removes all physical characteristics from consideration Based entirely on “intelligence”

Never been passed yet Turing thought it would be passed by 2000 but it hasn’t The Loebner contest: $100K prize for 1st program to pass

the Turing test (no winners so far!) http://www.loebner.net/Prizef/loebner-prize.html

Alan Turing’s “Turing Test”

Invented in the 1950’s

Purpose: to test if a computer is “intelligent”

Set-up: requires 2 humans and a computer one human is the “Interrogator” and kept in a separate room

The Interrogator has a typed conversation with both the human and the computer, without knowing which is which

The computer is considered intelligent if the interrogator fails to distinguish between the responses of the human and those of the computer

How the Turing Test works

Searle’s Chinese Room giant room in China with input and output slots messages written in Chinese are put into input slot answer, also in Chinese, arrives in output slot answer cannot be distinguished from answers a (Chinese

speaking) human would provide

What’s inside the room? if there is a human there, then we would say “the room”

exhibits intelligence if there is a computer there, we would say it has passed the

Turing Test

Falsifying the turing test

But, what’s inside the room is an army of unilingual English clerks and a huge number of filing

cabinets filled with cards with various rules on them mapping one Chinese symbol into other Chinese symbols (it’s a BIG room!)

When a question arrives in the input slot it is broken down into its constituent symbols by the clerks, each

symbol written on a piece of paper for each symbol, clerks look up the cards associated with the symbol,

copying new symbols onto new pieces of paper based on the rules on the cards

these new symbols lead to the look up of further cards and the application of rules on these cards creating still more pieces of paper with more symbols

this process iterates, mediated by the clerks, until it terminates with a final piece of paper that is pushed through the output slot

Falsifying the turing test

This is an exact analogy of the Turing Test The process inside the room is an exact analogy for

how a computer processes information the clerks are like processing units the filing cabinets are like computer memory the cards with the rules are like programs

So, where is the intelligence? in the clerks? - but they are just following orders without

intelligence (besides, they don’t speak Chinese) in the cabinets or the cards or the room? - but they are

inanimate where else?

Must, therefore, mean that the Turing Test is wrong Or does it?

Falsifying the turing test

Two common approaches to AI: one approach attempts to use computers to

simulate human mental processes. we don’t really know how brains work – hard to simulate! brain much more complex than most powerful computer two main types

cognitive psychology (simulating mind) neural networks (simulating brain)

the second, more common, approach to AI involves designing intelligent machines, independent of the way people think. acknowledge that computers are adept at solving problems in

other ways

Making an intelligent computer

One of the most successful areas of AI has been game playing Chess: Deep Blue has beaten

Gary Kasparov, the reigning world champion

Checkers: the world’s best checkers player is a computer program, Chinook (built at U. of Alberta)

Poker: now being explored Go: still too complex

Techniques work best for complete information games with no element of chance Rock Paper Scissors: http://chappie.stanford.

edu/cgi-bin/roshambot

Game playing

Main game playing technique is lookahead search extrapolate all possible moves from the current board position

“growing” a game tree then examine the bottom of the tree for (extrapolated) board

positions that look good (using an evaluation function) make a move that would lead towards the best of these positions repeat this process after the opponent has moved estimated game tree sizes, if generated to the various end games:

Tic-Tac-Toe: 9! = 362,880 nodes Checkers: 10**78 nodes Chess: 10**120 nodes Go: 10**720 nodes

estimated duration of the universe to date (in nano-seconds): 4.5*10**27 (this is less than 10**28)

game playing researchers have found lots of techniques for “pruning” game trees to make lookahead search more tractable

Humans use strategies that do relatively little lookahead

Lookahead search

http://www.codeproject.com/useritems/RSM_TicTacToe.asp

Tic Tac Toe

AI techniques developed by game playing researchers have led to general searching techniques in a variety of applications outside of game playing, eg. planning automated theorem proving constraint satisfaction

As in game playing such searches often combinatorially explode, and there thus needs to be some way to prune and guide searches heuristics: rules of thumb an evaluation function in game playing is a heuristic: allows evaluation

of a game position without further growing the search tree There is much mathematical work on proving the efficiency

(computational complexity) of search algorithms, looking for better search algorithms

Searching in other domains

Harder than it seems determining grammatical correctness (syntax)

rules are vague, ambiguous, and contradictory. Colorless green ideas sleep furiously

semantic analysis The dog lies on his blanket The boy lies on his test

determining meaning from dialog context (pragmatics) The chicken is ready to eat

the problem of ambiguity They are flying planes I saw Mt. Robson flying to Vancouver Time flies like an arrow

Natural Language Communication

The Russian-English project (late 50’s, early 60’s) millions of dollars, eventually abandoned

out of sight, out of mind – invisible idiot or blind and insane the spirit is willing, but the flesh is weak - the vodka is good, but the meat

is rotten first rate translation without understanding is impossible

But, there are situations where there can be limited successes translating scientific documents: unilingual Japanese physicist

can fix up a machine-translated English language physics paper weather forecasting Babelfish: http://babelfish.altavista.com is translating legal documents such a situation?

Saskatchewan’s own Gigatext disaster in the late 80’s: millions spent on translation software to translate Saskatchewan laws into French automatically - why might this not be such a good idea?

Language translation

Eliza – one of the first programs to converse in a limited form of natural language (developed by Joseph Weizenbaum in the mid-60’s) early language-processing used scripts and generic, open-ended, or neutral

responses http://www-ai.ijs.si/eliza/eliza.html http://www.simonlaven.com/

Conversation without communication

Dialogue between Eliza (upper case letters) and a simulation of a paranoid named Parry (lower case letters), developed by Kenneth Colby in the late 60’sI don't understand your motives.

WHY DON'T YOU UNDERSTAND MY MOTIVES?

Shouldn't I be?

WHY DID YOU SAY THAT JUST NOW?

That's just the way it is.

SUPPOSE THAT'S JUST THE WAY IT WERE NOT.

So?

I'M NOT SURE I UNDERSTAND YOU FULLY.

You harp on the same questions too much.

WHAT DOES THAT SIMILARITY SUGGEST TO YOU?

I don't get you.

DO YOU WISH THAT YOU DID GET I?

Eliza goes to work.

Pattern recognition involves identifying recurring patterns in input data with the goal of understanding or categorizing that input

Applications include image analysis optical character recognition automatic speech recognition talking computers neural networks

Pattern recognition

Identifying objects and shapes in a photo, drawing, video, or other visual image

Uses: colorizing classic motion pictures, piloting cruise missiles, identifying people, etc.

Difficult to do irrelevant surrounding data,

obstructions, indistinct edges, changes in light or angle, movement, etc.

Image analysis and vision

Locates and identifies printed characters embedded in images (it “reads” text)

About 99% accurate “at best” much better with typed text than handwriting

Uses: digitizing paper documents, “Palm Pilots”, translating foreign text characters…

Optical Character recognition

Have to be able to adapt to different pronunciations, accents, rate of speech, tone, voice inflection, etc.

Have to be able to isolate words from a continuous signal

Must be able to interpret and use all of the subtleties of speech in order to interact believably

Much easier for the computer to speak believably than to understand speech advancing quite fast, now possible to imitate a real voice…

ethic/legal issues – can a network use an actor’s voice to say things the actor never said?

Speech recognition

Automatic speech recognition systems use pattern recognition techniques similar to those used by vision and OCR systems, including these: segmentation of input sound patterns into individual

words and phonemes expert rules for interpreting sounds context “experts” for dealing with ambiguous sounds learning from a human trainer

Speech recognition

Expert systems are programs designed to replicate the decision-making process of humans has a knowledge base representing content from

a specific field Knowledge bases contain facts, and a

system of rules for determining and changing relationships among facts can be reorganized as new information arrives

Expert reasoning systems

Expert systems are difficult to build because it’s hard to capture “all” knowledge from an expert

If-then rules if the engine will not turn over and lights don’t work,

then check the battery Probabilistic reasoning

deals with uncertainties, conclusions stated as probabilities

there’s a 70% chance that this patient has a bacterial infection

Expert systems

Expert systems are used in many areas medical diagnosis, credit card authentication, process

insurance claims, predicting weather, grammar checker in word processors, medical diagnosis, even prospecting

They have many advantages can help to train new experts can provide expertise when no experts are available

physically can preserve knowledge of experts can help to reduce the number of human errors can augment human decision making when used

collaboratively by an expert

Expert systems

Machine learning attempts to learn patterns from data various machine learning algorithms: eg. ID3 data mining

text mining mining databases usage mining

neural networks Many applications now of machine learning

in information retrieval in mining corporate databases in modelling behaviour of users for personalized and adaptive

systems http://www-ai.cs.uni-dortmund.de/kdnet/auto?self=$81d91eaae317b2bebb

Machine Learning

A robot is a computer-controlled machine designed to perform specific manual tasks. a robot’s central processor might be a

microprocessor embedded in the robot’s shell, or it might be a supervisory computer that controls the robot from a distance

Robots

Robots are program-controlled machines designed to perform specific manual tasks remote controlled robots: intelligence is supplied by humans

which control the robot from a distance autonomous robots: the robot does its own reasoning,

planning, and acting They use input sensors to sense their current

environment and then are able to act appropriately to this environment: planning, acting, and reacting

They can do things that are impossible for humans see infrared lights, rotate arms 360°, enter areas

unreachable by humans, etc. They are limited by current state of technology

can’t tie shoelace, can’t consistently tell the difference between a cat and dog!

what is the algorithm for tying your shoelace??

Robots

Robots often need to be able to plan eg. a Mars robot decides how to go from one place to another eg. a household robot decides how to avoid obstacles while

vacuuming the floor A plan consists of a sequence of steps that the robot creates to

carry out one or more tasks Advantage of allowing the robot to plan: it can flexibly react to

local conditions eg. Mars is a long way away, with many minutes to communicate

back and forth; makes things very complex to control from earth eg. the dog keeps moving about during the vacuuming

It is useful to plan ahead of time but the planner must also be able to dynamically change a plan as it is executed when the unexpected happens

Planning is useful in many areas outside of robotics instructional planning in an e-learning system dialogue planning in a conversational system scheduling applications, eg. NASA has an intelligent scheduling

system that has reduced the time to re-configure the shuttle by one third

Robots and planning

Agents are autonomous software processes that can reason and act in some environment usually the environment is virtual (or else they would be called

robots) agents in computer games personal or companion agents in e-learning, eg. Steve system agents

Multi-agent systems more than one agent in the environment behaviour is emergent, based on agent interactions the environment and agents constitute an eco-system of a sort what if the agents could replicate? what if they could reason

and communicate as well as humans? what would they talk about with each other? what would they tell us?

Intelligent Agents

Are humans a multi-agent system?

Environment Rules and constraints Some interference from outside (God)

The ant farm analogy

Weak AI: computers can be made to replicate some intelligent functions, but not all

Strong AI: it will eventually be possible to create a computing system that equals human intelligence

Implications of AI

Weak AI: computers can be made to replicate some intelligent functions, but not all which ones not?

possibly emotions? Hollywood treats emotions as special: eg. insisting that emotions

are uniquely human and all other entities wish to have them (such as Data on Star Trek: Next Generation)

are emotions disconnected from cognition? others?

the expertise paradox: we can make computers that are relatively good at doing

things that require human expertise but we have a very hard time capturing commonsense and

basic abilities that children can perform at a young age why might this be?

Implications of AI

Strong AI: it will eventually be possible to create a computing system that equals human intelligence looking back: over history many things have been considered unique to

humans but no longer: tool use; doing mathematics (originally “computer” meant a human doing calculations)

why should any current human capabilities in principle be out of bounds for computers?

Church’s thesis (paraphrased): there can be no more powerful system that processes information than a computer

a prospect with staggering implications if we can build a system that equals human capabilities, why not one

that exceeds them? plenty of things that computers do better than humans now! probably more to come? are there any limits?

towards “The Singularity”: human-machine symbiosis http://singinst.org/

Implications of AI

How intelligent can computers get? Will they surpass humans? Should limits be imposed on what can be investigated? Who is responsible if the AI system makes a mistake? Are there tasks that computers should not even attempt? Tasks that only

humans should do? How much would you trust a computer compared to a human? Will intelligent computers be infallible? - consider HAL in the movie 2001 Can there be robot moral codes? - consider Asimov’s 3 laws of robotics Should an AI of human-level intelligence (or above) have the same rights as

humans? - consider David in the movie AI Can intelligent programs have emotions? Should they? Can it be avoided? Would an artificial intelligence be immortal? - consider that it could save a

copy of itself from time to time to be restored if anything happened to it. But, would a copy of an intelligent program be the same individual? What if the original copy was suddenly found again?

Does the metaphor mind=software and brain=computer illuminate the mind-body problem in philosophy? Does it shed light on the notion of “soul”?

Philosophical and Social Implications of AI

Isaac Asimov, science fiction writer, introduced in his 1942 short story "Runaround", the Three Laws of Robotics:1. A robot may not injure a human being or, through inaction, allow a

human being to come to harm.2. A robot must obey orders given it by human beings except where

such orders would conflict with the First Law.3. A robot must protect its own existence as long as such protection

does not conflict with the First or Second Law. How could these be enforced by the software running a

robot? consider the Japanese engineer killed by an industrial robot

pushing him into a grinding machine when it couldn’t sense him: how could this be prevented?

Asimov’s 3 laws of robotics

• http://www.aiinc.ca/demos/whale.html– expert system demo

• http://perceptual-vision.com/Nouse/– Nouse perceptual vision interface

• http://www.ivs3d.com/products/fledermaus/– fledermaus

• http://www.aiboworld.tv/– Aibo

• http://ca.youtube.com/watch?v=NXHioIzcvAk– Aibo robot soccer play– http://www.robocup.org/

• http://www.sciencedaily.com/releases/2005/10/051013083817.htm– robot car: desert challenge

• http://blog.wired.com/defense/2007/11/darpa-names-win.html– robot car: city challenge

• http://www.friendlymachines.com/– robomower

Resources to investigate

What is AI? What is the Turing Test? Two approaches to AI Natural language communication Pattern recognition

Image analysis OCR Speech recognition

Expert systems Robots Future of AI and ethical dilemmas

To know