Chapter 10
Global Village
“… is the shrinking of the world society
because of the ability to communicate.”
Positive: The best from diverse cultures
will emerge and be shared by all.
Negative: Even on the other side of the
world, Britney Spears is playing on a radio
somewhere.
Telecommunication Models
Tree-and-branch model: a centralized
provider sends out information through
many channels to consumers
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Radio
Cable TV
Switched-network model: people on the
system are consumers and possibly
providers
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Internet
Artificial Intelligence (AI)
AI is a group of related technologies used
for developing systems (hardware or
software) that behave intelligently or
emulate human qualities such as learning,
reasoning, language, vision, and
recognition
It’s also my field of research.
VR
VR stands for Virtual Reality
VR by itself is not really an area of AI, but
it is used by some AI systems
Here at IU we have a VR room called the
CAVE (CAVE Automated Virtual
Environment)
Robotics
Not all robotics is properly classified as AI,
but much of it is
Industrial robots that build cars, robotic
lawnmowers and vacuum cleaners and
robots that emulate the way we move use
“simple” AI programs
Robotic guides or waiters use more
sophisticated AI systems
NLP
Natural Language Processing is an
extremely difficult area of AI where we try
to create a system that can understand
and/or produce human natural languages
with some accuracy
NLP is especially hard because of all the
things that make human language
interesting – words with double meanings,
symbolism, sarcasm, puns, etc.
Fuzzy Logic
Fuzzy Logic systems deal with imprecise
data and uncertainty, and problems that
often have multiple solutions
They are designed to find good solutions
quickly with imperfect data, instead of
perfect solutions slowly
Expert Systems
Expert Systems are computer programs used to
solve problems in a very specific domain
One expert system for diagnosing and
classifying bacterial infections (MYCIN) was
more accurate on the whole than the doctors
that trained it
Once, they were considered to be the
culmination of AI research
Problems: It takes a real expert to train one, and
they are useless outside of their field. Unlike real
humans, they have no direct way of learning
new material.
Neural Networks
Neural Networks (NN) are computer
simulations of the way neurons are
connected in our brains.
Neural Networks are exceedingly good at
learning patterns. You present them with a
set of training cases, which they use to
learn general association patterns, then
you test the system on a set of test cases
and evaluate it.
Genetic Algorithms
Some problems are very hard to solve
directly, or may have many “ok” solutions
but only a few “good” ones.
Genetic Algorithms (GAs) are commonly
used to “evolve” good solutions
GAs simulate organic reproduction,
mutation, and natural selection (i.e.
Evolution)
The Turing Test
How do we tell when an AI system is
“truly” intelligent?
The main problem is we cannot explain
intelligence yet.
We regard each other as intelligent
because we’re human, and we tend to act
reasonably rationally.
We conclude that someone is intelligent if
they “act” intelligent.
The Turing Test
Alan Turing, the founding father of much modern
CS, addressed this question in 1950.
His proposal, not entirely serious, is that a
human judge talk with 2 “people” over some sort
of remote connection. If at the end of the
conversation, he could not be sure which
“person” was human and which was an AI, the AI
was intelligent for all practical purposes.
The Turing Test
The Turing Test is a nice idea, but it’s not
taken very seriously by many scientists
right now for several reasons:
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It’s extremely difficult to build a system with
enough reasoning and NLP to be able to
handle the TT for more than one area
ELIZA, an early AI program, fooled many
people into thinking it was a real human
psychologist. But ELIZA was very clearly not
“intelligent”.