1. Artificial Intelligence
Module 11

2. 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

3. Can computers be intelligent?
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??

4. Intelligence in a computer
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

5. Brain versus 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

6. 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

7. 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

8. Brain versus computer Adaptability and learning Time to learn
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!

9. Defining Artificial Intelligence
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 computers
do things at which, for the moment, people are better.
Artificial intelligence is the study of the computations
that 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.

10. a computer is intelligent?
How do we measure if
a computer is intelligent?
A machine may be deemed intelligent when it can pass for a human being in a blind test.
—Alan Turing
The Turing Test

11. Alan Turing’s “Turing Test”
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!)

12. How the Turing Test works
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

13. Falsifying the turing test
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

14. 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

15. 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?

16. Making an intelligent computer
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

17. Game playing
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:

18. Lookahead search 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

19. Tic Tac Toe

20. Searching in other domains
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

21. Natural Language Communication
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

22. Language translation
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:
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?

23. Conversation without communication
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

24. Eliza goes to work.
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’s
I 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?

25. Pattern recognition
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

26. Image analysis and vision
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.

27. Optical Character recognition
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…

28. Speech 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?

29. 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

30. Expert reasoning systems
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

31. Expert 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

32. 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

33. Machine Learning 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

34. Robots
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

35. 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??

36. Robots and planning 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

37. Intelligent Agents
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?

38. Are humans a multi-agent system?
Environment
Rules and constraints
Some interference from outside (God)
The ant farm analogy

39. Implications of AI
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

40. 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?

41. 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

42. Philosophical and Social 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”?

43. Asimov’s 3 laws of robotics
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?

44. Resources to investigate
expert system demo
Nouse perceptual vision interface
fledermaus
Aibo
Aibo robot soccer play
robot car: desert challenge
robot car: city challenge
robomower

45. To know 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