1. Artificial Intelligence: Human vs. Machine
Adapted from slides provided by Professor Marie desJardins
CMSC 100
November 8, 2011

2. 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 computer’s)
...but our memories look very different!
The first magnetic core memory [
thebrain.mcgill.ca

3. What is AI? (and why is it so cool?) AI: Past and Present
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

4. What is AI?

5. AI: A Vision
Could an intelligent agent living on your home computer manage your , coordinate your work and social activities, help plan your vacations…… even watch your house while you take those well planned vacations?

6. 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

7. AI Foundations of AI Biology Mathematics Philosophy Economics
Computer
Science &
Engineering
Mathematics
Philosophy AI
Economics
Biology
Psychology
Linguistics
Cognitive
Science

8. What does this say about human beings?
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?

9. History of AI

10. History

11. Early AI: Eliza
ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test
Implemented at MIT during 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!!

12. 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.

13. 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 PARRY’s ramblings from those of real paranoids

14. 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?
...

15. 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?

16. 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

17. The Loebner Contest
A modern version of the Turing Test, held annually, with a $100,000 cash prize
Hugh Loebner was once director of UMBC’s Academic Computing Services (née UCS, lately OIT)
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
Go to the German area, copy the URL for the manifesto, and use Google Translate to translate into (sorta) English.

18. What’s Easy and What’s Hard?
It’s 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
It’s 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?

19. AI Today

20. 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, AT&T, SRI, ISI, MERL, ...
Lots of companies!

21. 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)

22. 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

23. Robotics SRI: Shakey / planning sri-shakey.ram
SRI: Flakey / planning & control sri-Flakey
UMass: Thing / learning & control umass_thing_irreg.mpeg umass_thing_quest.mpeg umass-can-roll.mpeg
MIT: Cog / reactive behavior mit-cog-saw-30.mov mit-cog-drum-close-15.mov
MIT: Kismet / affect & interaction mit-kismet.mov mit-kismet-expressions-dl.mov
CMU: RoboCup Soccer / teamwork & coordination cmu_vs_gatech.mpeg

24. 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, traffic circles, busy intersections...
Six finishers (best time: 2.8 miles in 4+ hours)

25. Art: NEvAr
Use genetic algorithms to evolve aesthetically interesting pictures
See

26. ALife: Evolutionary Optimization
MERL: evolving ‘bots

27. Human-Computer Interaction: Sketching
Step 1: Typing
Step 2: Constrained handwriting
Step 3: Handwriting recognition
Step 4: Sketch recognition (doodling)!
MIT sketch tablet

28. 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!

29. AxonX
Smoke and fire monitoring system

30. Rocket Review
Automated SAT essay grading system

31. What Can’t 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 √
Construct plans in dynamic real-time domains
Refocus attention in complex environments
Perform life-long learning
Exhibit true autonomy and intelligence!

32. Computational vs. Biological Memory

33. 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

34. 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
Size
Speed

35. 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)
bytes = one yottabyte (1Y?)
* Note that external storage is usually measured in decimal rather than binary (1000 bytes = 1K, and so on)

36. Moore’s Law
Computer memory (and processing speed, resolution, and just about everything else) increases exponentially

37. ???? Showdown Computer capacity: Computer retrieval speed:
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: 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

38. It’s 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 we’re just getting started
Electronic computers were only invented 60 years ago!
Homo sapiens has had a few hundred thousand years to evolve...

39. The Skeptics Speak

40. 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? ? !

41. What Searle Argues People have beliefs; computers and machines don’t.
People have “intentionality”; computers and machines don’t.
Brains have “causal properties”; computers and machines don’t.
Brains have a particular biological and chemical structure; computers and machines don’t.
(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 don’t.”)

42. Let’s Introspect For a Moment...
Have you ever learned something by rote that you didn’t really understand?
Were you able to get a good grade on an essay where you didn’t really know what you were talking about?
Have you ever convinced somebody you know a lot about something you really don’t?
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 don’t? (Or do they?)

43. Just You Wait...
Give us another 10 years!
or 20...
or 30...
or 50...