Artificial Intelligence: Human vs. Machine

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Artificial IntelligenceHuman vs. Machine

• Professor Marie desJardins
• CMSC 100
• Fall 2008

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Memory is at the Core (Literally)

• Remember Hal?
• Open the pod bay door, Hal.
• My mind is going...
• Memory is at the core of our being (and a
  computers)
• ...but our memories look very different!

The first magnetic core memory www.columbia.edu/a
cis/history
thebrain.mcgill.ca
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Overview

• What is AI? (and why is it so cool?)
• AI Past and Present
• History of AI
• AI Today
• Computational vs. Biological Memory
• The Skeptics Speak

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What is AI?
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AI A Vision

• Could an intelligent agent living on your home
  computer manage your email, coordinate your work
  and social activities, help plan your vacations
  even watch your house while you take those well
  planned vacations?

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Main Goals of AI

• Represent and store knowledge
• Retrieve and reason about knowledge
• Behave intelligently in complex environments
• Develop interesting and useful applications
• Interact with people, agents, and the environment

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Foundations of AI
Computer Science Engineering
Mathematics
Philosophy
AI
Biology
Economics
Psychology
Linguistics
Cognitive Science
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Big Questions

• Can machines think?
• If so, how?
• If not, why not?
• What does this say about human beings?
• What does this say about the mind?
• And if we can make machines think, should we?

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Why AI?

• Engineering To get machines to do a wider
  variety of useful things
• e.g., understand spoken natural language,
  recognize individual people in visual scenes,
  find the best travel plan for your vacation, etc.
• Cognitive Science As a way to understand how
  natural minds and mental phenomena work
• e.g., memory, learning, language, visual
  perception, etc.
• Philosophy As a way to explore some basic and
  interesting (and important) philosophical
  questions
• e.g., the mind body problem, what is
  consciousness, etc.

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History of AI
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History
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Early AI Eliza

• ELIZA A program that simulated a psychotherapist
  interacting with a patient and successfully
  passed the Turing Test
• Implemented at MIT during 1964-1966 by Joel
  Weizenbaum
• First script was DOCTOR
• Simple pattern-matching
• Each pattern had an associated reply which might
  include bits of the input (after simple
  transformations (my ? your)
• Weizenbaum was shocked at reactions
• Psychiatrists thought it had potential
• People unequivocally anthropomorphized
• Let me tell you my problems...
• Many thought it solved the natural language
  problem!!

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Eliza Speaks

• I am the psychotherapist. Please, describe your
  problems.
• i'm having a hard time with my students
• Why do you say that?
• well, they keep asking me for money
• Maybe your plans have something to do with this.
• what plans?
• Is it because of your life that you say what
  plans?
• No, it's because I didn't know what you were
  talking about
• Is it because you didn't know what I was talking
  about that you came to me?
• no, it's because i wanted to see if you could
  pass the turing test
• Does it bother you that you wanted to see if I
  could really pass the turing test?
• ..
• How do you reconcile problems at school?
• This is a waste of time. Good bye
• My secretary will send you a bill.

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Early AI Parry

• Kenneth Colby modeled a paranoid using the same
  techniques as Eliza, around 1968
• PARRY has basic emotions. If it gets angry, its
  replies become more hostile
• In a Turing test, expert psychiatrists were
  unable to distinguish PARRYs ramblings from
  those of real paranoids

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PARRY Speaks

• Good afternoon. My name is Doctor Jones.
• Pleased to meet you.
• How are you?
• All right I guess.
• Could you tell me your name?
• Pat Smith.
• I though you might have some problems.
• I've been upset lately.
• By what?
• People get on my nerves sometimes.
• Tell me more.
• Do you know anything about bookies?
• ...

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Please tell me this isn't AI!

• P I don't understand your motives.
• E Why don't you understand my motives?
• P Shouldn't I be?
• E Why do you say that just now?
• P That's just the way it is.
• E Suppose that's the way it were not.
• P So?
• E I am not sure I understand you fully.
• P You harp on the same questions too much.
• E What does that similarity suggest to you?
• P I don't get you.
• E Do you wish that you did get I?

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Turing Test

• Three rooms contain a person, a computer, and an
  interrogator
• The interrogator can communicate with the other
  two by teleprinter (or, say, AIM)
• The interrogator tries to determine which is the
  person and which is the machine
• The machine tries to fool the interrogator into
  believing that it is the person
• If the machine succeeds, then we conclude that
  the machine can think

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The Loebner Contest

• A modern version of the Turing Test, held
  annually, with a 100,000 cash prize
• Hugh Loebner was once director of UMBCs Academic
  Computing Services (née UCS, lately OIT)
• http//www.loebner.net/Prizef/loebner-prize.html
• Participants include a set of humans, a set of
  computers, and a set of judges
• Scoring
• Rank from least human to most human
• Highest median rank wins 2000
• If better than a human, win 100,000 (Nobody
  yet)
• 2008 winner Elbot

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Whats Easy and Whats Hard?

• Its been easier to mechanize many of the
  high-level tasks we usually associate with
  intelligence in people
• e.g., symbolic integration, proving theorems,
  playing chess, medical diagnosis
• Its been very hard to mechanize tasks that lots
  of animals can do
• walking around without running into things
• catching prey and avoiding predators
• interpreting complex sensory information (e.g.,
  visual, aural, )
• modeling the internal states of other animals
  from their behavior
• working as a team (e.g., with pack animals)
• Is there a fundamental difference between the two
  categories?

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AI Today
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Who Does AI?

• Academic researchers (perhaps the most
  Ph.D.-generating area of computer science in
  recent years)
• Some of the top AI schools CMU, Stanford,
  Berkeley, MIT, UIUC, UMd, U Alberta, UT Austin,
  ... (and, of course, UMBC!)
• Government and private research labs
• NASA, NRL, NIST, IBM, ATT, SRI, ISI, MERL, ...
• Lots of companies!

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Applications

• A sample from the 2008 International Conference
  on Innovative Applications of AI
• Event management (for Olympic equestrian
  competition)
• Language and culture instruction
• Public school choice (for parents)
• Turbulence prediction (for air traffic safety)
• Heart wall abnormality diagnosis
• Epilepsy treatment planning
• Personalization of telecommunications services
• Earth observation flight planning (for science
  data)
• Crop selection (for optimal soil planning)

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What Can AI Systems Do Now?

• Here are some example applications
• Computer vision face recognition from a large
  set
• Robotics autonomous (mostly) automobile
• Natural language processing simple machine
  translation
• Expert systems medical diagnosis in a narrow
  domain
• Spoken language systems 2000 word continuous
  speech
• Planning and scheduling Hubble Telescope
  experiments
• Learning text categorization into 1000 topics
• User modeling Bayesian reasoning in Windows help
  (the infamous paper clip)
• Games Grand Master level in chess (world
  champion), checkers, backgammon, etc.
  Breaking news (8/7/08) - MoGo beats
  professional Go player

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Robotics

• SRI Shakey / planning sri-shakey.ram
• SRI Flakey / planning control sri-Flakey
• UMass Thing / learning controlumass_thing_irre
  g.mpegumass_thing_quest.mpegumass-can-roll.mpeg
• MIT Cog / reactive behaviormit-cog-saw-30.movmi
  t-cog-drum-close-15.mov
• MIT Kismet / affect interactionmit-kismet.mov
  mit-kismet-expressions-dl.mov
• CMU RoboCup Soccer / teamwork
  coordinationcmu_vs_gatech.mpeg

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DARPA Grand Challenge

• Completely autonomous vehicles (no human
  guidance)
• Several hundred miles over varied terrain
• First challenge (2004) 142 miles
• winner traveled seven(!) miles
• Second challenge (2005) 131 miles
• Winning team (Stanford) completed the course in
  under 7 hours
• Three other teams completed the course in just
  over 7 hours
• Onwards and upwards (2007)
• Urban Challenge
• Traffic laws, merging, trafficcircles, busy
  intersections...
• Six finishers (best time 2.8 miles in 4 hours)

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Art NEvAr

• Use genetic algorithms to evolve aesthetically
  interesting pictures
• See http//eden.dei.uc.pt/machado/NEvAr

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ALife Evolutionary Optimization

• MERL evolving bots

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Human-Computer Interaction Sketching

• Step 1 Typing
• Step 2 Constrained handwriting
• Step 3 Handwriting
  recognition
• Step 4 Sketch recognition (doodling)!
• MIT sketch tablet

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Driving Adaptive Cruise Control

• Adaptive cruise control and pre-crash safety
  system (ACC/PCS)
• Offered by dozens of makers, mostly as an option
  (1500) on high-end models
• Determines appropriate speed for traffic
  conditions
• Senses impending collisions and reacts (brakes,
  seatbelts)
• Latest AI technology automatic parallel parking!

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AxonX

• Smoke and fire monitoring system

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Rocket Review

• Automated SAT essay grading system

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What Cant AI Systems Do (Yet)?

• Understand natural language robustly (e.g., read
  and understand articles in a newspaper)
• Surf the web (or a wave)
• Interpret an arbitrary visual scene
• Learn a natural language
• Play Go well v
• Construct plans in dynamic real-time domains
• Refocus attention in complex environments
• Perform life-long learning

Exhibit true autonomy and intelligence!
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Computational vs. Biological Memory
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How Does It Work? (Humans)

• Basic idea
• Chemical traces in the neurons of the brain
• Types of memory
• Primary (short-term)
• Secondary (long-term)
• Factors in memory quality
• Distractions
• Emotional cues
• Repetition

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How Does It Work? (Computers)

• Basic idea
• Store information as bits using physical
  processes (stable electronic states, capacitors,
  magnetic polarity, ...)
• One bit yes or no
• Types of computer storage
• Primary storage (RAM or just memory)
• Secondary storage (hard disks)
• Tertiary storage (optical jukeboxes)
• Off-line storage (flash drives)
• Factors in memory quality
• Power source (for RAM)
• Avoiding extreme temperatures

Speed
Size
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Measuring Memory

• Remember that one yes/no bit is the basic unit
• Eight (23) bits one byte
• 1,024 (210) bytes one kilobyte (1K)
• 1,024K (220 bytes) one megabyte (1M)
• 1,024K (230 bytes) one gigabyte (1G)
• 1,024 (240 bytes) one terabyte (1T)
• 1,024 (250 bytes) one petabyte (1P)
• ... 280 bytes one yottabyte (1Y?)

Note that external storage is usually measured
in decimal rather than binary (1000 bytes 1K,
and so on)
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What Was It Like Then?

• The PDP-11/70s we used in college had 64K of RAM,
  with hard disks that held less than 1M of memory
• ... and we had to walk five miles, uphill, in the
  snow, every day! And we had to live in a
  cardboard box in the middle of the road!

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What Is It Like Now?

• The PDP-11/70s we used in college had 64K of RAM,
  with hard disks that held less than 1M of memory
• The cheapest Dell Inspiron laptop has 2G of RAM
  and up to 80G of hard drive storage....
• ...a factor of 1018 more RAM and 1012 more disk
  space
• ...and your iPod nano has 8G of blindingly fast
  storage
• ...so dont come whining to me about how slow
  your computer is!

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Moores Law

• Computer memory (and processing speed,
  resolution, and just about everything else)
  increases exponentially

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Showdown

• Computer capacity
• Primary storage 64GB
• Secondary storage 750GB (1012)
• Tertiary storage 1PB? (1015)
• Computer retrieval speed
• Primary 10-7 sec.
• Secondary 10-5 sec.
• Computing capacity 1 petaflop (1015
  floating-point instructions per second), very
  special purpose
• Digital
• Extremely reliable
• Not (usually) parallel

• Human capacity
• Primary storage 7 2 chunks
• Secondary storage 108432 bits?? (or maybe 109
  bits?)
• Human retrieval speed
• Primary 10-2 sec
• Secondary 10-2 sec
• Computing capacity possibly 100 petaflops, very
  general purpose
• Analog
• Moderately reliable
• Highly parallel

????
More at movementarian.com
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Its Not Just What You Know

• Storage
• Indexing
• Retrieval
• Inference
• Semantics
• Synthesis
• ...So far, computers are good at storage, OK at
  indexing and retrieval, and humans win on pretty
  much all of the other dimensions
• ...but were just getting started
• Electronic computers were only invented 60 years
  ago!
• Homo sapiens has had a few hundred thousand years
  to evolve...

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The Skeptics Speak
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Mind and Consciousness

• Many philosophers have wrestled with the
  question
• Is Artificial Intelligence possible?
• John Searle most famous AI skeptic
• Chinese Room argument
• Is this really intelligence?

?
!
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What Searle Argues

• People have beliefs computers and machines
  dont.
• People have intentionality computers and
  machines dont.
• Brains have causal properties computers and
  machines dont.
• Brains have a particular biological and chemical
  structure computers and machines dont.
• (Philosophers can make claims like People have
  intentionality without ever really saying what
  intentionality is, except (in effect) the
  stuff that people have and computers dont.)

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Lets Introspect For a Moment...

• Have you ever learned something by rote that you
  didnt really understand?
• Were you able to get a good grade on an essay
  where you didnt really know what you were
  talking about?
• Have you ever convinced somebody you know a lot
  about something you really dont?
• Are you a Chinese room??
• What does understanding really mean?
• What is intentionality? Are human beings the
  only entities that can ever have it?
• What is consciousness? Why do we have it and
  other animals and inanimate objects dont? (Or
  do they?)

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Just You Wait...
Give us another 10 years!
or 20...
or 30...
or 50...
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Thank You!
Any Questions?