Download Chapter 6: Looking Glass World

Chapter 6: Looking Glass World
Simulations in computers exist as
• Binary code
• Logic operations
Approaches to coding
• Outside-in (math based)
• Inside-out (physics based)
Outside-in
• “it starts with how things should look and
then tries to bolt on code for how it should
behave.”
• Special code must be added to account for
imperfect behavior
• Example: Bouncing Ball
y=abs(sinx)
y=abs[(sinax)]/bx
y=abs[(sinaxc)]/bx
parabolas
decaying parabolas
friction
Inside-out
• “For this we must start from the inside and work out;
structure must generate function.”
• Properties will emerge naturally
• Example: Bouncing Ball
X=0, Y=1000
; Start at top left of world
DX=10, DY=0
; Ball is rolling right but not yet falling
LOOP:
; Every tick, do the following…
X=X+DX
; Add speed to last X, Y position
Y=Y+DY
; to calculate ball’s new position
DY=DY-1 ; Vertical speed changes due to gravity
IF Y<=0 THEN
; If we have hit the floor…
Y=0
; Don’t bounce right through it!
DX=DX*0.7
; Include some friction to the ball in
DY=DY*0.7
; both the horizontal and vertical directions
; by multiplying the speed by a fraction
DY=0-DY
; Reverse the ball’s vertical direction
END IF
; That’s the bounce dealt with
Orders of simulation:
• 1st order = procedural space
• 2nd order = parallel universe
1st Order Structure
• Strives to be sufficiently realistic
• Brittle system: attempts to simulate
appearance; error in 1st order magnifies in
consecutive orders
• Robust system: simulates global
behavior despite differences in internal
structure
2nd Order Structure
• Arises from communication amongst 1st
order building blocks
• Procedural space becomes a parallel
universe
Chapter 7: They Call Me Legion;
For I am Many
Human Simulation
Simulation as result of computer program:
– 1st order: atomic motion
– 2nd order: life, conciousness, intelligence
Data provided to the model determines the
function
Algorithms vs. Second-Order
Structures
Algorithm is single serial computation
Second-order virtual machines can be put
together to build organizations
• Machines do their own internal processing
• Machines communicate to produce parallel
computation
Parallel systems and time-slicing
• Time slicing is separation of “test” phase and
“change state” using buffered storage
• A computer using a time-slicing loop is able to
perform several functions and not be devoted to
a single one
Intelligence
“Intelligence is not the ability to follow rules –
it is the ability to develop rules in the first
place.”
“Intelligence is the result of billions of
unintelligent processes operating
concurrently.”
AI Research
• Programs are not intelligent because they
only contain portions of stored intelligence
• AI research needs to be focused on
pseudo-parallel algorithms
Simultaneity
• Simultaneity is the key to intelligence in
simulation
• Turning serial algorithms into parallel
systems allows things to happen
simultaneously
“I do have a strong hunch that intelligence is
necessarily parallel in nature and yet, as
long as a computer program runs quickly
enough in relation to the speed at which
the outside world is changing, it is
acceptable to simulate this parallelism by
time-slicing on a serial computer.”
Chapter 8
• -Creatures stay in their ideal place in the
environment through the “magic” of
feedback loops
• Cybernetics= the formal study of feedback
systems
• -Feedback is an essential component of
life and comes in two types
•
1. positive – tends to intensify a
change
•
2. negative – tends to oppose a
change, stabilizing the system
For example:
• 1. A manager tells workers they are performing well on
the job and the workers respond by performing even
better.
– (Good work + praise= better work)
• 2. A manager reprimands workers for performing poorly
and they respond, in frustration, by performing even
poorer.
– (Bad work + criticism = worse work)
• Both are examples of positive feed back! The thing
being fed back, the manager’s opinion, amplified a
change in the workers’ behavior.
• Note: What determines + or – is completely independent
of the feedback itself
• In nature: As the climate cools, animals growth a thicker
fur coat
• Negative feedback means the manager’s
opinions counteract a change in the workers’
behavior
• 3. If the manager praises poor performance, and
reprimands good performance, the work will
stabilize in the middle
– (Bad work + praise = better work) and (Good work +
criticism = worse work)
• Negative feedback in nature: Animals are
growing thicker fur coats, while the climate
begins to warm
• 4. If a worker knows that he/she is working poorly and is
then praised, the worker will respond to the decreased
demands with less effort.
– (Bad work + praise = worse work)
• This is an example of adaptation of the amount of effort
by the worker.
• Adaptation is the first law of biology: it is a common
feature of biological systems that exploits feedback
• Looking at the system as a whole:
• There are two feedback loops at work here.
– 1. On a longtime scale, negative feedback (Adaptation)
– 2. On a short timescale, positive or negative feedback (the
manager’s opinion)
Feedback loops can be compared to a feedback landscape of hills and
valleys where hills are positive and valleys are negative feedback.
-Feedback landscapes are continually changing in two ways:
1. The population is evolving
2. The environment is changing
Life
-it is a journey along an uneven feedback landscape
-during the journey, there are limited amounts of resources and energy
available
-organisms must continually move forward along the landscape as it
crumbles behind them
-the challenge of life is to remain running on one of the ridges without
slipping beyond recovery
-intelligence is the ability to use past experience of cause and effect to
predict future events, intelligence helps insure survival
• Observer Effect
– Things in life have meaning relative to the observer
because of their utility
– A statue has something a lump of clay does not
– A computer has something a slab of silicon does not
• This difference can best be described as
“elegance”
• The environment, as an observer, selects
phenotypes with the most utility
• This utility is relative to the environment
– In a cool climate, thick fur coats have a greater utility
than thin coats. If the climate warms, thick coats will
no longer have the greatest utility
Chapter 9
The Building Blocks of Life
One way to categorize these blocks it by their anatomical
features
-cells to tissues to organs
-life can then be defined by its features
(compositional definition)
• An organism can also be thought of as a network of
feedback loops. Life can then be defined by its
processes rather than its features (functional definition)
• By looking at the larger picture of the function of an
organism, not simply the composition of its parts, we see
that it is more than the sum of its parts
• The building blocks of life are not the physical anatomy
but the informational layout.
• This type of building block can be implemented in a
computer as first-order components
Chapter 10
The Whole Iguana
Upon watching his friend dismember a spider as a child, Grand makes
two observations regarding life.
•
Only through an organism’s interactions with its own environment can
intelligence truly become evident. It is the fight for survival that creates this
basis and generates intelligent motives and actions.
•
Intelligence is only possible as a whole. An organism is either whole or not
an organism at all.
Observation 1:
Only through an organism’s interactions with its own environment can
intelligence truly become evident. It is the fight for survival that creates
this basis and generates intelligent motives and actions.
• Example:
– With its limbs removed, the spider continues
to try and use its removed appendages to
continue moving.
• An organism must receive feedback from its
environment in order to learn.
• Lack of interaction with its environment creates a
void where intelligence should reside.
“Intelligence without action is not even achievable.”
It is possible for organisms, most notably
human beings, to express intelligence by
using their imaginations.
An organism’s survival plays a key roll in demonstrating intelligence.
Example:
If you ask an individual an intellectually stimulating question, what is the
purpose or reasoning the person would give for answering?
This brings us to the concept of Artificial Intelligence.
• How do we construct an intellectual entity
on these guidelines of survival?
What does Grand suggest?
• Supply a reward or punishment to the
system artificially!
Example:
• Grand uses the example of people
associating a “stern” look with being
physically hit or spanked when we were
younger by a displeased adult.
Observation 2:
Intelligence is only possible as a whole. An organism is either whole or
not an organism at all.
• “As a general rule, if you take an organism to pieces you do not end
up with pieces of an organism. All you get is a sticky mess of
lifeless bits of meat or vegetable matter.”
Example
• How far much of a human’s brain can you remove until
they are no longer considered a human?
This example leads us directly to the critical thinking
question asked by Grand.
• “So why do so many who attempt to create thinking machines
expect to be able to implement just one specific aspect of
intelligence and get away with it?
Example
• An adult runs into the middle of a busy
road in order to prevent a child from
getting hit by a bus.
• Why should this be considered
intelligence?
In order to create a complete system we can correctly term “Artificial
Intelligence” Grand gives five required components.
• 1) A brain capable of learning and acting
as a result of prior interactions within a
“realistic environment.”
• 2) An emotional system which allows for
rewards and reprimands to motivate
action.
• 3) An ability to eat and continue living in
order to associate its intelligence with
survival.
• 4) An ability to speak and communicate
(mostly to communicate with its owner).
• 5) An ability to find a mate and reproduce
an offspring which shares the “genetic”
background of its parents.