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) parabolasy=abs[(sinax)]/bx decaying parabolasy=abs[(sinaxc)]/bx 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 worldDX=10, DY=0 ; Ball is rolling right but not yet fallingLOOP: ; Every tick, do the following…X=X+DX ; Add speed to last X, Y positionY=Y+DY ; to calculate ball’s new positionDY=DY-1 ; Vertical speed changes due to gravityIF 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 inDY=DY*0.7 ; both the horizontal and vertical directions; by multiplying the speed by a fractionDY=0-DY ; Reverse the ball’s vertical directionEND 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 evolving2. 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 Upon watching his friend dismember a spider as a child, Grand makes two observations regarding life.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.