Artificial Intelligence:Artificial Intelligence:

Human vs. MachineHuman vs. Machine

Professor Marie desJardins

CMSC 100

November 5, 2009

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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 computer’s)

...but our memories look very different!

The first magnetic core memory [www.columbia.edu/acis/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

What is AI?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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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

Main Goals of AI

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ComputerScience &

Engineering

AI

Mathematics

CognitiveScience

Philosophy

Psychology Linguistics

BiologyEconomics

Foundations of AI

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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?

History of AIHistory of AI

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History

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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!!

Early AI: Eliza

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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 PARRY’s 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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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 UMBC’s 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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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?

AI TodayAI 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, AT&T, SRI, ISI, MERL, ...

Lots of companies!

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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)

Applications

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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

What Can AI Systems Do Now?

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Robotics

SRI: Shakey / planning sri-shakey.ram SRI: Flakey / planning & control sri-Flakey UMass: Thing / learning & control

umass_thing_irreg.mpegumass_thing_quest.mpegumass-can-roll.mpeg

MIT: Cog / reactive behaviormit-cog-saw-30.movmit-cog-drum-close-15.mov

MIT: Kismet / affect & interactionmit-kismet.movmit-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, traffic

circles, 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 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

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

Size Speed

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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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Moore’s 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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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...

The Skeptics SpeakThe 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 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.”)

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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?)

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Just You Wait...