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4. Giving speeches and having conversations
Giving a speech inherits most of the intelligent
pathways for writing a book. Intelligent pathways in memory are structured
hierarchically. Writing sentences on paper is similar to speaking sentences
to a public. They both share commonalities.
FIG. 53 is a diagram depicting the hierarchical tree that shows the
relationships between writing a book and giving a speech. The common
intelligent pathway used is translating thought to sentences (FIG. 54). The
intelligent pathway to write a book is used to write a book and the
intelligent pathway to give a speech is used to give a speech. Both
intelligent pathways inherit the root node, which is translating thoughts
into sentences. For each task, there are slight differences for T4 and the
robot’s conscious have different rules that it must follow.
FIG. 54
FIG. 55
In intelligent pathway T4, the last instruction will
point to a different pathway to do things according to the task. If the
robot was giving a speech, the pathway will be to open its mouth to speak
the remembered sentence. If the robot was writing a book, the pathway will
be to move his hand and write the remembered sentence on a paper.
Tasks and rules are very important to the robot when
giving a speech. The robot has to know the people in the audience. A
speech for an audience comprising children will be different from a speech
for an audience comprising politicians. With the children audience, the
robot will have rules that it must follow, such as: “use elementary words”,
“don’t use bad language”, “speak slowly”, “make the audience laugh” and so
forth. The job of the intelligent pathways changes as the robot understands
the current situation. The intelligent pathways modify the tasks and rules
and knowledge of the computer program inside the robot’s conscious.
This is why the robot has to learn speeches in a
variety of environments. He should learn to give a speech in front of
politicians, school teachers, the public, in a classroom, in a family
gathering, in a concert and so forth. At least, learn the knowledge of how
to give a speech under various audiences. College classes on speeches are a
good place to learn how to give a speech. Thus, the robot will be able to
give speeches under any situation. He can give a speech in a news
conference, or in a family party or in a debate class.
In the hierarchical tree, under give speech, the robot
will be able to select the appropriate intelligent pathways for that given
situation. All speeches will comprise very similar general instructions.
The specific intelligent pathways will have instructions to do specific
types of speeches.
5. Writing computer software programs
The writing of computer software is very similar to
writing a book. Although, the rules of grammar and the rules of software
engineering are different, they both have rules that you have to follow
according to specific tasks.
The robot has to have knowledge about object-oriented
programming, which includes discrete math functions like if-then statements,
for-loops, and statements, or statements and so forth. Learning classes,
methods and recursions are also a must to write meaningful software.
Once the knowledge and the rules of software
engineering are stored in the robot’s brain, he has to know the steps to
write software. These steps can be read from basic programming books or
learned through trial and error. When the robot takes computer science
classes, the professor will give assignments to the robot, ranging from
basic to complex. A simple assignment like writing a java applet that will
output the text: “hello word”, might be the first assignment. As the robot
learns more about computer science and actively write software, he will get
better and better. By the time he graduates from college, the robot will
have the knowledge to write complex software.
By the time, the robot gets into college, his brain
already has the capacity to learn anything. Learning how to write software
or learning how to build bridges or learning how to draw the blueprints to a
city is possible.
It might of taken the robot 20 years to develop the
skills to write a book. However, when it gets to college, it will take the
robot only several months to learn how to write a decent software program.
The reason why is because the intelligent pathways are so well developed
that it can teach itself how to learn. The way the robot store and retrieve
information has been optimized.
FIG. 55 is a diagram showing what knowledge the robot
should know about computer science. These various knowledge will be needed
to translate an outline of a problem into a software program.
The knowledge of object oriented programming will be configured in memory
according to how knowledge in science books are configured (in previous
chapters). Teacher lectures of chalkboard demonstrations will help to
organize the data in memory. Also, visual diagrams in the computer text
books will help to organize the data in memory. When the teacher draws
diagrams on the chalkboard and finger points to numbers and shapes, he is
providing a very strong method for the robot’s brain to configure data in an
organized way.
FIG. 56
Referring to FIG. 56, classes and functions can be
represented by a square diagram with inner shapes representing certain
elemental parts. For example, the square at the top is the declaration,
where the objects and functions are defined. The circle at the bottom is
the main body and that is where objects are used and functions are called.
In the second diagram is a visual diagram of
recursion. The rootnode is the parent, which is made up of a class and the
child node is an inner copy of the parent. Other data and facts about
binary trees can be included in this diagram. During the learning phase,
the teacher is drawing this diagram and he is pointing here and there and
writing down things on the diagram. The robot will store static as well as
linear data from the lecture and configure data from the lecture in an
organized manner for memory storage.
As far as accessing data from the knowledge learned
about object oriented programming, the data is stored in memory as 5 sense
data (either as static data or linear data or both). The intelligent
pathways to analyze is the key to finding out how things work. For example,
before writing a software program, the robot will activate the class diagram
and analyze the diagram. Information extracted from the intelligent
pathway, via search patterns, will determine the fruit of the search. The
robot might think: “oh, that is where the declaration goes or that is where
the body of the problem should be or that is where the functions are
called”. Another example is the human hand. If the teacher asks this
question: “what does a human hand look like”, the robot will extract a
visual image of the human hand and using intelligent pathways in memory to
output what the visual hand looks like. There is no data in memory that
explains the detailed aspects of a human hand. The intelligent pathways
analyze the image to extract the information of what the human hand look
like, during runtime.
FIG. 57 is a diagram depicting the steps the robot will take to write
software. The robot will be given a problem by the professor. He will take
the problem and write an outline for a software program that will solve the
problem. Next, the robot will translate the outline into software
functions. Finally, he will test out the software program so that it is
working correctly; and solves the problem given by the professor.
FIG. 57
The outline of the software program is very important.
At this stage, the robot needs to know how the software program will work
and use computer science knowledge to translate the outline into computer
functions. For example, if the robot is writing a computer program on a
binary tree, he has to use recursive classes. The outline of the binary
tree will probably be a diagram of a hierarchical tree. If the computer
software is a chat network, the outline might be multiple interconnected
nodes with a hub in the middle.
For me personally, I would draw out the outline of the
software program I am planning to write. I do this by drawing circles,
squares and connected lines. I also write down the functions of each node
and what data is contained in each node.
Referring to FIG. 58, when the robot decides to write a
software program, knowledge will start to pour into its conscious. At
first, a general computer program will be used to manage tasks and rules.
As the robot follows intelligent pathways in memory to write software, the
computer program inside the robot’s conscious will get more specific. It
can write any software program regardless of how complex it might be.
The intelligent pathways contain the step-by-step instructions to manage
multiple layered tasks and to be aware of possible rules that the robot
needs in order to accomplish tasks in the task container. Task
interruptions will be solved by the robot’s conscious as well. There are
limits to how certain computer statements work. The robot has to be aware
of these limits. There might be number limits to a for-loop because the
program might run out of disk space. Or certain variables can only use
certain data types. The robot has to know these rules when writing the
software.
FIG. 58
Learning how to write software and understanding the
knowledge of writing software is one thing, actually doing the assignments
is another thing. The robot has to write software starting from simple
software programs to complex software programs. The robot’s brain learns
knowledge through trial and error and the pathways have to go through a
bootstrapping process, whereby intelligent pathways build on itself.
Here is a list of simple to complex software programs:
1. write a program to output “hello world”.
2. write a program to convert Fahrenheit to Celsius.
3. write a database program to store customers using a
binary tree.
4. work in a group to write software to an operating
system.
When the robot learns to write a simple program like
program1, he can use that knowledge to write program2. In program2 you have
to know how to output string text to the monitor so that users can see the
number conversions. Program2’s knowledge can be used in program3 because
program2 has the ability to manage classes and calling functions from the
main body. Finally, knowledge from program3 can be used to write codes in
program4.
Thus, by learning and writing software programs from
simple to complex, the robot can form smarter intelligent pathways to cater
to complex tasks.
Hierarchical intelligent pathways are also considered
when learning how to write software programs. Intelligent pathway (S1),
which is a universal problem-solving pathway, can be used to write
software. If the robot fails in completing the assignment, he will loop
itself to try again. He will modify his goals, change his strategies and
take another action. This loop will repeat itself, until the robot
completes the assignment, which is to write a specific software program.
To make the task of writing software more complex,
imagine that the professor wanted the robot to use Unix to write the codes.
The robot’s brain has two tasks to juggle, one task is to operate the Unix
system and the second task is to write the software program. Let’s say the
robot knows how to write software programs, but doesn’t know how to use the
Unix system. The professor will give a primer on the Unix system and the
robot has to read the instructions and understand how to operate his
account.
FIG. 59 is an intelligent pathway, called W1, to open
and use the Unix system. W2 is another intelligent pathway to write a
specific software program. The robot’s brain manages the two tasks (W1 and
W2) and merges them together to physically allow the robot to write the
software program. The robot will first login to the Unix system using his
user name and password. Then he will open a writing program called pine.
Next he will write the software program according to the assignment. He can
test the software program by executing commands in the Unix system. The
robot will apply the trial and error method to refine the software program.
Finally, after the tests are over the task of writing a software program is
done. However, in order to accomplish writing the software program (W2),
the robot had to use the Unix system (W1). The robot’s conscious managed
performing two of the tasks in order to finish writing the software
program.
Learning to make decisions
Making decisions is based on a lifetime of learning
from school. Teachers teach the robot how to make decisions. Assignments
are given by teachers to instruct the robot to do tasks on decision making.
These assignments include: worksheets, homework assignments, and real life
examples. The robot will also learn to make decisions based on trial and
error. The knowledge learned in school forms the foundation for intelligent
pathways to make decisions. The robot will modify these decision making
pathways through the process of trial and error.
When the robot is at its early stages of life, it will
have to build its pathways from simple data then as it gets older and there
are more data in memory it will organize the pathways into complex
intelligence. Just like how we humans have to learn to walk, to talk, to
move, to eat, these machines have to go through life the same way. Let’s
illustrate the gradual forming of simple data into intelligent data by
outlining a series of stages.
1. innate reflexes
2. trained to do things
3. sequential events
4. sentence commands
5. give robot option commands
6. practice makes perfect
7. copy other peoples behavior
Decision making comes from many factors and the 7
stages of human intelligence form the intelligent pathways to make
decisions.
Teachers will teach lessons on decision making, ranging
from easy to complex. An easy decision making is selecting an item from a
group of items. A complex decision making is answering a question for a
physics exam.
Decision making is just one form of a task. The robot
has the option of aborting the task or continuing a task or postponing a
task. Once a decision has been made that decision can be modified. A human
being can decide to go to Mcdonalds for lunch. However, when he gets to
Mcdonalds, he might change his mind and go to Pizza hut instead.
Examples
Imagine a teacher wanting the robot to select one movie
from 100 different movies. The teacher will help guide the robot by giving
him criteria steps (they don’t have to be in linear order) that would help
narrow down the decision making process. The teacher might say things
like:
1. “what kind of movie category do you like to watch?”
-- “action and comedy movies”
2. “here are the choices. What actor/actress would
you like to see?” – “Arnold”.
3. “based on his movies, which one would you like to
select?” – “terminator 2”.
This is just one example of selecting a movie from a
group of different movies. There might be a situation where the robot has
to select from 5 movies or 2 movies. The robot can use the example above to
select from an arbitrary number of movies. This example will self-organize
with other similar examples and a universal intelligent pathway will be
created in memory to make decisions related to movie selections.
What about buying items in a supermarket? The decision
making process will include similar steps to the example above. In other
words, it inherits some instructions from the universal intelligent pathway
to select movies. However, buying items is a different situation and
requires different instructions. Teachers will teach the robot how to buy
items in the supermarket in a rational way. The teacher will make up
assignment worksheets that will ask the robot to select items in a
supermarket. In the worksheet, there might be two items. One item is very
expensive and the other item is very cheap and both items look and taste the
same. The worksheet will ask which item should the robot select. The
correct answer is the cheaper item. If both items are the same but their
prices are different, then it’s obvious that the robot should buy the
cheaper item. Let’s call this intelligent pathway (D2).
The last example is simple. To make the decision
making process more complex, new facts should be included such as: the
cheaper item is a generic brand and is known to have product complaints, but
the expensive item is a name brand and has a good reputation.
Another factor is how much money does the robot
currently have? If the robot decides to select the expensive item, but
doesn’t have the money, then by default, he has to select the cheaper
item.
Complex decision making builds on itself through a bootstrapping
process. In a complex decision making process, the robot has to use many
encapsulated linear decision making pathways. FIG. 60 is an illustration.
Intelligent pathway D2 is to select 1 out of 2 items. D2 is encapsulated in
D1. Next, D2 might trigger a new decision pathway such as generating 10
categories based on D2’s decision and selecting one out of the 10 (D3).
Next, to narrow down the choices, a D4 is used to come up with new facts
about D3’s decision. Finally, with all the selections made from D2-D4, the
robot will use multiple if-then statements to come up with one decision
(D5). Intelligent pathways D2-D5 are all previously learned in the past.
Through steps (via sentences and visual examples) by teachers, the robot is
able to create intelligent pathway D1 to make a very complex decision.
FIG. 60
Decision making can also be learned and copied
The lessons from teachers should have relations with
observation of decision making. This way, the robot can form intelligent
pathways to make decisions based on observation and not by teachers teaching
step-by-step examples.
FIG. 61 is a diagram depicting D2. The pathway shows
the steps of the robot that will lead to a decision. To the right of
pathway D2, is the robot observing someone making a decision similar to D2.
Instead of seeing the hidden steps of making the decision, the robot only
sees the problem and the decision. In D2, there is: the problem, the
steps, and the decision. In the observation pathway the robot only see the
problem and the decision.
Through relational links between D2 and the observation, the robot will
assume that the person is using the missing steps to make the decision.
FIG. 61
With this said, the decision in D2 can also change if
the decision making in the observation is different. Based on many
examples, the intelligent pathway D2 will be modified based on the
observation of decision making. The robot will try to make decisions based
on what the average of society will do. The teachings establish the
foundation for decision making. However, the observation of decision making
and trial and error will decide how intelligent pathway D2 will evolve.
Many factors are needed for decision making. The
intelligent pathways store the probability of selection. This probability
could be a learned thing or it could be a hidden object that is not known to
the robot. A learned probability is if the robot learned to use a
calculator to determine the probability of something. The robot might be
taught to calculate the probability of something in its mind. What are the
chances an event will happen? When making a decision based on flipping a
coin, the robot knows that there is a 50 percent chance, it could be heads
or tails. This form of probability is learned. The math results of the
probability will determine what the robot will select.
In some cases, the robot can do calculations in the
mind and to use that info. to select items. In other cases, the robot has
to work out the math problem on paper and to use the answer for the decision
making process. Yet, in other cases, the robot is not aware of the
probability and the probability is based on hidden data generated by the
patterns stored in intelligent pathways.
Complex if-then statements
The intelligent pathways to make decisions are usually
based on complex if-then statements (or common discrete math functions).
Many similar pathways are learned in memory and these similar pathways
self-organize to form patterns. These patterns establish computer programs
in intelligent pathways to activate the linear steps to generate a
decision. Intelligent pathway D1 is one example. D1 can have many
variations and if the robot follows D1 in multiple same situations, the
linear steps will be slightly different.
The reason why is because the intelligent pathways
store a computer program that will select different steps, at given times,
to accomplish a goal. The goal in this case is to make a decision or to
select items. Following intelligent pathway D1 will not always generate the
same steps to make a decision. However, the computer program in D1 will
most likely come out with the correct decision at the end.
Generating complex if-then statements are done through
patterns. The robot will use intelligent pathways to come up with
meaningful logic to accomplish a task. The intelligent pathways are
universal and it will generate complex if-then statements (or any
combination of discrete math functions) to generate a decision. Universal
means that the intelligent pathway was trained with many similar examples of
solving a problem and it can solve the problem under various circumstances.
Types of decision making
There are many different types of decision making.
Random selection is one type or logical decision making is another type. A
situation where the robot has to select from a variety of food is one
example of logical decision making. The robot has to decide on 3 food:
hamburger, spaghetti or hot dog. He understands he can only choose one food
to eat. The robot will look into each items’ powerpoints in memory. The
powerpoints is the desirability of that item. A function in selected
pathways in memory will determine which items’ powerpoints are highest and
output that information to the robot’s conscious. The robot will select the
item with the most powerpoints. For example, if the hamburger has 10
powerpoints, the spaghetti has 18 powerpoints and the hot dog has 14
powerpoints, then the robot will select the spaghetti.
Things can be more complex. The robot loves to eat
spaghetti and the powerpoints are high, but what if the serving of the item
is small? The factor about size is confronted next. Although the robot
selected the spaghetti, the size of the hot dog might over power the
decision to pick the spaghetti.
What if the hot dog had onions? The robot is allergic
to onions and can’t eat any food that has onions. The robot will
automatically turn down the hot dog and activate a new problem: select
between the hamburger and the spaghetti. The hamburger is larger in size
than the spaghetti, but the robot loves spaghetti more than the hamburger.
Either the robot will randomly pick or it will come to a compromise (via
friendly conversation). At the end, the robot picked the spaghetti.
You can see from the linear decision making problem
that it takes the robot time to resolve the many conflicts in the decision
making process. However, the more the robot learns how to make decisions,
the better it will be at the task. Also, the process of trial and error
will evolve the intelligent pathways learned by teachers. The steps to
making a decision should be based on using logic to narrow down the possible
choices -- breaking up the complexity into chunks and tackling each chunk
separately.
Other decision making, like random selection, is based
on hidden data in pathways that randomly pick an item. The robot learns to
pick randomly by observing decision making situations. For example, if the
robot watches a game show and he sees players select the 2nd door
more than the other doors, then when the robot is confronted with the
decision of selecting random doors, the robot will choose the 2nd
door.
I was asked by a friend once this question: “pick a
number between 1 and 10”. My answer was 7. My friend told me that 80
percent of all people answered 7. The reason why is because he told me to
pick a number between 1 and 10. My obvious thought was 5. I remember he
said it could be any number, so I didn’t pick 5, instead I picked a number
between 5 and 10 which is 7.
This type of example shows how predictable human beings
are. Even though they learned knowledge differently and live different life
styles, society made them into the average joe.
Other types of decision making include selecting
pictures and movie clips from webpages. When people go online they make
decisions in terms of what they are searching for online. The search
engines aren’t really good at finding exact things online. They give the
user an approximate search, but all the hard work is done by the user. When
I was writing my patent applications it took me hours just to find 30 images
that I needed for my drawings. All 30 images online needed some form of
modification, such as resizing or adjusting the contrast, in order to be
used in my patent application. The Patent office has rules that the
drawings must follow and I had to make sure the images follow these laws.
If the search engine was intelligent at a human level
then all I needed was one search and my 30 drawings would be found. In
fact, the search engine would help me modify the 30 drawings and download
them into my computer.
That’s not the case with modern day search engines.
More complex type of decision making is to compare images or compare movie
clips and select from a group of items. There can be a mixing of different
items. The robot might have to select from 3 websites, 2 movies, and 3
pictures. Only one item can be selected for purchase.
Comparing a movie to a website is difficult because
each item is completely different in many aspects. It’s because of years
and years of learning that the robot is able to compare different media
types. Teachers have taught the robot many different examples and he has
personally engaged in decision making situations in his life. This
knowledge and experience of comparing different media types is
self-generated by the robot actively making decisions. In order to actively
make a decision, teachers have to teach the robot how to compare and
choose.
One such method taught by teachers to compare different
media types is to identify aspects of each item and compare these aspects.
The comparison might reveal facts from each item that might benefit the
robot. If the robot was comparing a website to a movie, maybe content of
the two items can be compared instead of presentation of data. Maybe the
financial worth of the website can be compared to the financial worth of a
movie. Let’s say that a rich person had a choice to make between buying a
website or owning the rights to a movie, he will have to compare the two
items. He must compare different aspects of each item and ultimately reveal
how each item can benefit the rich person. After debating with itself, the
rich person will weigh the benefits and select one of the two items.
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