Prospects for the 21 Century

parathyroidsanchovyAI and Robotics

Nov 17, 2013 (3 years and 8 months ago)

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Artificial Intelligence:

Prospects for the 21
st

Century


Henry Kautz

Department of Computer Science

University of Rochester

What is Artificial Intelligence?


Study of principles for understanding
and building intelligent agents


Human, animal, or mechanical


How to perceive the world


How to reason and make decisions


How to learn


How to act (motion, speech)


How to cooperate with other agents

Can’t Win Definition of AI


AI = making a computer solve a
problem that requires human
intelligence


By definition, any problem solved by AI
no longer requires human intelligence


So, AI never succeeds!


Useful idea: study
tasks

people
perform in order to understand
intelligence

Outline


Approaches to AI


Task based (“Classical AI”)


Neural networks


Which Way Will Achieve AI?


Criticisms


Ray Kurzweil’s Perspective


A Middle Ground


Classical AI


The
principles

of intelligence are
separate from the hardware (or
“wetware”)


Look for these principles by studying
how to
perform individual tasks

that
require intelligence

Success Story: Medical Expert
Systems


1980: First expert level performance


diagnosis of blood infections


Today: 1,000’s of systems


Often outperform doctors

Success Story:

Chess

I could feel


I
could smell


a new
kind of intelligence
across the table

-

Garry Kasparov
(1997)



Examines 5 billion positions /
second


Intelligent behavior
emerges

from brute
-
force search

Success Story: Robotics (1)

Rendezvoused with an asteroid, 1998
-
2000

Capable of autonomous diagnosis & repair

Success Story: Robotics (2)


DARPA Grand Challenges, 2004
-
2007


Races in desert and urban environments by
fully autonomous vehicles


Succeeded with “off the shelf” AI
technology!

Success Story: Text to Speech


Kurzweil

Reading Machines, 1978
-
2006

Neural Networks


Develop computational models of the
brain at the neural level


McCulloch & Pitts model (1943): very
simple, but a pretty good approximation
of most real neurons

Success Story: Face
Recognition


Programming a
neural net
that learns to
recognize
faces can now
be done as
homework
problem!

Success Story: Brain
-
Computer
Interfaces

Miguel
Nicolelis

(2003),
Duke
University

Success Story: MRI Imaging of
Specific Thoughts


Tom Mitchell (CMU) 2006

Tools

Buildings

Food

Which Approach Will Achieve
AI?


Criticism of Classical AI:


Successes so far are in all narrow
domains


We can never explicitly program enough
“commonsense” into a AI system to make
it a true general intelligence


The human brain has a completely
different architecture than a modern
computer

Which Approach Will Achieve
AI?


Criticism of Neural Networks:


Successes so far are in all narrow
domains


Building an AI by studying neural
processes is like trying to reverse
-
engineer Windows Vista by watching bits


“Summation and threshold” is just
another kind of logic gate!

A Middle Ground


Most AI researchers (including me)
believe that AI will be accomplished by
a combination of ideas from both camps


Studying tasks tells us
what needs to be
computed


Studying brains tells us what
classes of
algorithms are possible


We can
implement

those algorithms in many
ways

A Middle Ground


Neural nets are not necessary the best
way to implement all the thing the brain
does!


Evolution rarely produces optimal solutions
!


Machine learning is compatible with
both

the classical and neural net approaches


Learning from text on the Internet will
solve the problem of getting enough
“commonsense” information

Pioneering an Emerging Area


Assisted Cognition


Computer systems that enhance the
abilities, independence, and safety of
persons with cognitive disabilities


Aging and age
-
related diseases


Brain injury


Developmental disabilities


Computer caregivers

Examples


Maintaining a daily schedule


Compensating for memory problems


Compensating for lowered self
-
initiative


Step
-
by
-
step task prompting


Navigation


Indoors and outdoors


Safety and health


Need for immediate help


Long term health trends

Activity of Daily
Living Monitoring


Goal: Accurate,
automated ADL logs


Changes in routine
often precursor to
illness, accidents


Human monitoring
intrusive &
inaccurate

Object
-
Based Activity Recognition


Activities of daily living involve the
manipulation of many physical objects


Kitchen: stove, pans, dishes, …


Bathroom: toothbrush, shampoo, towel, …


Bedroom: linen, dresser, clock, clothing, …


We can recognize activities from a
time
-
sequence of object touches

Sensing Object Manipulation


RFID: Radio
-
frequency
identification tags


Small


Semi
-
passive


Durable


Cheap


Near future: use
products’ own tags

Wearable RFID Reader


Bracelet reads tags near hand, transmits information
wirelessly to monitoring system


Soon will be built into a wristwatch

Interpreting the Sensor Data:
Machine Learning


Machine learning

algorithms automatically create
the recognition system from training examples


Can handle sensor noise and user errors

Using Commonsense Knowledge


Can further improve the system by
adding “commonsense knowledge”






Example: a travel mug is like a cup

Results: Detecting ADLs

Activity

Prior
Work

SHARP

Personal Appearance

92/92

Oral Hygiene

70/78

Toileting

73/73

Washing up

100/33

Appliance Use

100/75

Use of Heating

84/78

Care of clothes and linen

100/73

Making a snack

100/78

Making a drink

75/60

Use of phone

64/64

Leisure Activity

100/79

Infant Care

100/58

Medication Taking

100/93

Housework

100/82

Legend

Point solution

General solution

Inferring ADLs from
Interactions with
Objects

Philipose, Fishkin,
Perkowitz, Patterson,
Hähnel, Fox, and
Kautz

IEEE Pervasive
Computing
, 4(3), 2004

RFID

All Watched Over by Machines of Loving Grace,
by Richard Brautigan

I like to think

(it has to be!)

of a cybernetic ecology

where we are free of our labors

and joined back to nature,

returned to our mammal

brothers and sisters,

and all watched over

by machines of loving grace.