WHY I AM OPTIMISTIC

Patrick H. Winston

MIT Artificial Intelligence Laboratory

The salients

Applications side:

We have already won

Science side:

We are bound to win

The Applications

We have expanded our frontiers

Lots of people

Lots of good

A little good for a lot of people

A little good for a lot of people

A lot of good for a few people

A lot of good for a lot of people

We have exemplars of all kinds

Large software companies

Large entertainment companies

Companies with huge IPOs

Multidimensional multinationals

A multitude of small companies

We were a one-horse field

Rule chaining

Inheritance

Now we ride many horses

Rule chaining

Generate and test

Search

Tree building

Agents

Neural nets

Constraint propagation

Inheritance

Genetic algorithms

Bayes netsLearning

And not just reasoning horses

Vision Language and speech Infrastructure

We had a Pyrrhic victory

IO

MemoryCables Power

Tapes

Disk

Network

We learned negative lessons

Nobody cares about saving money

Using cutting edge technology To replace expensive experts

We learned positive lessons

Everybody cares about

New revenues Saving a mountain of money Increasing competitiveness

We changed the business model

ReplacesExpensivePeople

SavesMountainsOf Money

CreatesNewRevenue

Ferrets

Blunder stoppers

Novices

Experts

The critic and the billionaire

What’s next: connections

People

Global Net

Physical WorldComputers

EnhancedReality

Intelligent StructuresUseful robots

HumanComputerInteraction

InformationAccess

The click-in phenomenon

The fax machine The world wide web

The Science

Shrobe’s point

Applications drive science Unless they all look alike

Atkeson’s point

We could move to the center But, we might be kidding ourselves

My point

AI is applied computer science Much energy wonderfully used But consequently diverted

A 100 year enterprise

Molecular Biology

Artificial Intelligence

1950 2000 20501900

Why we are the way we are

Powerful Ideas

Models of Thinking

Reflection…Biology…Psychology

TuringMinsky

The IntelligentReasoner

LanguageVision

Input/Output Channels

The standard paradigm

The dawn age

x

(1 - x )dx

4

2 5/2

What went wrong?

We think with our eyes We think with our mouths We think with our hands Each faculty helps the others

What is the evidence?

Armchair psychology

Clues from the brain

Armchair psychology

Hillis’s observation on the value of talking to yourself

Everyone’s observation on the value of drawing a sketch.

From brain scanning

Intelligence is in the I/O

The Explanation

MotorReasoner

LinguisticReasoner

VisualReasoner

Is it time to start over?

An I/O oriented paradigm Essentially free computation Important, inspiring allies

From brain rewiring

From watching infants

Is it time to start over?

An I/O oriented paradigm Essentially free computation Important, inspiring allies Accumulation of powerful ideas

Six powerful ideas

Recreated condition One-shot learning Memory is cheap Change matters Survival of the smallest Bi-directional search

Recreated condition: Minsky

P

/ \

P P

/ \

P P

/ \ / \

P P P P

/ \ / \ / \ / \

P P P P P P P P

/ \ / \ / \ / \ / \ / \ / \ / \

P P P P P P P P P P P P P P P P

*----------------------------------

|

| K-line

|

*-------------->

|

*--------->

One-shot: Yip and Sussman

ae p l

Time

WordMemory

RuleMemory

One-shot: Yip and Sussman

ae p l z

Time

RuleMemory

WordMemory

One-shot: Yip and Sussman

100%

Trials 500

Accuracy

Memory is cheap: Atkeson

Atkeson’s practice tables

Atkeson’s practice results

One stored trajectory

Feedback only

Three stored trajectories

Change matters: Borchardt

A

D

Appear

Disappear

Change

Decrease

Increase

A

D

Appear

Disappear

Change

Decrease

Increase

Borchardt’s ladder diagrams

D A

D A

A A A

Distance

Speed

Contact

T1 T2 T4T3

Survival of the smallest: Kirby

Kirby’s phase transitions

Time

Coverage

Bi-directional search: Ullman

Model

Image

Joyous inferences

Powerful ideas Marvelous engineering Essential alliances

What about …

Bayes and Markov Neural nets and connectionism Logic

WHAT WE MUST NOT DO

Loose our faith

It will take 300 years All the low hanging fruit is gone We shouldn’t make predictions

Waste time arguing

Is it possible? Is it successful? Is it really AI?

Squander our capital

One thousand people 10% interested in the science side 10% actually working on it 10% of the time

WHAT WESHOULD DO

Human Intelligence Enterprise

Vision, language, motor Free hardware Clues from the brain Powerful ideas Conceive and test models

The Human Intelligence Enterprise

VisualReasoner

LinguisticReasoner

MotorReasoner

Why we should do it

It can only be done once Revolutionary applications

The Human Intelligence Enterprise

VisualReasoner

LinguisticReasoner

MotorReasoner

What we should ask

Why do we have discrete words? What do our inner agents say? How do they learn what to say? Do we see what chimps see? How did our faculties evolve? Why can’t we all play the piano?

The Human Intelligence Enterprise

VisualReasoner

LinguisticReasoner

MotorReasoner

So, here is why I’m optimistic

Nothing could possibly be morefun, exciting, rewarding, and glorious, than …

Applications that really matter Figuring out our own intelligence

The Human Intelligence Enterprise

VisualReasoner

LinguisticReasoner

MotorReasoner