Objects
Objects can be anything. It can be sound, it can be vision, it can be touch, and so forth. A visual word can be an object, a sound of a word can be an object, or the visual meaning of the word can be an object. For different medias the objects can be represented differently. There is also the consideration of combinations of objects together such as a visual object in conjunction with a sound object. A car zooming by is a combination of a visual object and the zoom sound is the sound object. Or dropping a pencil on the ground is a combination of visual and sound objects.

Another factor is that objects can be encapsulated. For example, a hand is an object that is encapsulated in another object, a human being. Another example is a foot is an object encapsulated in another object, a leg.

The way the program learns these objects is by repetition and patterns. Each object is represented by strength and if it ever repeats itself the strength gets stronger. If the object don’t repeat itself then it will forget and memory won’t have a trace. 1-d, 2-d, 3-d, 4-d, and N-d objects can be created by repetition and patterns.

Object association is the key to the conscious
For each object the AI has to find other objects in memory that have association. “The more times two objects are trained together” and “the closer the timing of the two objects are” the more association the two objects have with one another. The object that will be used to find associations is called the target object and the objects that have associations are called element objects.

When the AI recognizes the target object from the environment it will activate closest element objects that have association to the target object. There are three types of element objects:

1. equals (same meaning)
2. stereotypes
3. trees

Equals
Objects that are very close to each other are considered “equal”. When any element object past the assign threshold the element object and the target object are considered equal – they have the same meaning. One example of this is the sound “hat”, if the sound “hat” is the target object and the element object that passes the assign threshold is a visual image of a hat then both the sound “hat” and the visual image of hat is considered the same.

Stereotypes
Stereotypes are facts about the target object. Objects that are associated with the target object but are not consistent are stereotypes. These objects are also farther away from the target object. We look at the fixed object as a part of the overall object. If the target object is “cat” and “cat” is a part of “cats don’t like dogs”, then we can safely say that “cats don’t like dogs” is a stereotype of “cat”.

Trees
Trees are objects that are usually farther away from the target object. Sometimes trees have relations to the target object. A tree is just instructions that people teach you at certain situations. Timing of the object is the key difference between stereotypes and trees. This is the most important trait in my program to convey intelligence. One example of trees is when you cross the street, the tree “look left, look right and check to make sure there are no cars before crossing the street” pops up in your mind.

Activation of objects produces the conscious
I mentioned before that the HLAI inherits everything from the UAI. If I apply the above lessons along with the UAI program then you have a machine that can “think”. As the AI experience things in our environment it recognizes objects from memory and activates its element objects. The object the robot recognizes has associations to other objects such as meaning and stereotypes. When the AI see a cat the meaning of the cat pops up. This could be a sound “cat” or the visual word cat. All the meaning of the object cat will compete with one another to be activated (pop up in the mind). Next, the stereotypes of cat will start to pop up in the robots mind such as “cats don’t like dogs”. Element objects that are closest to the target object gets activated first before the element objects that are farther away gets activated. It also depends on how long the AI focuses on the target object.

In the case the AI is on the cross walk, getting ready to cross the street, the tree “look left, look right and check to make sure there are no cars before crossing the street” pops up in its mind. This tree is learned previously by teachers many times and the association between crossing the street and the tree becomes stronger. The timing of when to train the tree to pop-up is important.

The importance of language
Language is very important to producing machines that can do complex thinking. The words in the language encapsulate things around us in a “fuzzy” way. For example, the word cat can mean a lot of things. Cats can come in different variety of shapes and sizes and colors. All the different objects in our environment that is classified as a cat is actually encapsulated in the word “cat”. Another word that is encapsulated is “jump”. There are infinite ways that the word can be expressed in the environment. You can be watching a person from the back and seeing them jump, you can be watching a person from the front, side, bottom, top, 50 yards away, 1000 yards away, etc. The person doing the jumping can also be wearing different clothes or they could be different species. All these various ways of interpreting jump is all encapsulated in the word “jump”. The point I’m trying to make is that our world is infinite and in order to understand the world around us we use language.

A more complex form of language is entire sentences. The computer will find meaning to sentences by studying the patterns that surround the sentence. Specifically, analyze certain scenes related to the sentence. If the sentence is “the rat jumped over the dog”, then the scene of a rat jumping over the dog is used to find patterns to the sentence. The AI will take training data (diagram below) and other similar training data in memory and average out what the meaning of the “the rat jumped over the dog” is.

Understanding sentences is important because it encapsulates entire scenes and instructions. The sentence is a fixed thing in our environment but the meaning behind the sentence can be changed based on society. Language is considered fuzzy logic because there is no real meaning behind a word (or sentence). One example is the word bad. We know from the dictionary that the word “bad” means something that is negative. However, the meaning can change based on society. In modern times the sentence: “those dance moves are bad”, uses the word bad not in the context of what we know as bad, but the word means the opposite of negative. The letters that make up the word bad is always going to be the same but the meaning of it from one time period to the next will change. (of course, there can be multiple meaning to a word and that is also decided by society)

Patterns
The AI program can do a lot of things such as change data, forget data (there is no such thing as deleting data), insert data, look for data, take sections of data out and so forth. The AI relies on patterns from the environment to do all these things. The one thing that usually manipulates these functions is language. When we say something like: “that was the wrong answer, the right answer is packets”. This one sentence is actually instructing the AI to modify data in memory. When we say something like: “remember to buy cheese at the store”. This one sentence is actually instructing the AI to put a remember instruction when it gets to the store. When the AI goes to the store the instructions “remember to buy cheese at the store” pops up and this tells the AI to go to the cheese section and buy cheese.

Since the pattern subject matter is so long I’m just going to give one example. The answering of questions relies on patterns in order to be understood. We are able to find the patterns and universalize the pathways so that when someone ask us a question we can give them the appropriate answer.

8 = 8 is an equal object or Dave = Dave is an equal object. They are equal is the relationship between the two objects. Whenever the computer finds two objects equal it will establish a relationship between the two objects and find patterns that revolve around these two objects. From the example below we have taken all the equal objects and we have tried to find patterns between those equal objects. Answering questions is a pattern that relies on equality to find the answers. This may not be very clear when you look at the first example, but after looking at the second example and comparing that with the first example there is clearly a pattern there.

By establishing a relationship between equal objects the computer will be able to find patterns between different training data and forge a universal pattern that can answer a universal question. The examples above have a pattern.

The pattern found above can answer any question that has that kind of configuration. Examples of this would be:

what is 8+8? 8+8 is 16.
what is the 21st state in the USA? The 21st state in the USA is Illinois.
what is the first letter in the alphabets? The first letter in the alphabets is ‘A’
what is the last letter in the alphabets? The last letter in the alphabets is ‘Z’

Averaging the meaning of sentences
When teachers say:

“look left, right, and make sure there are no cars before crossing the street” (R1)
“remember to see if there are no cars from the left and right before you cross the street” (R2)
“don’t forget to look at all corners to make sure there are no cars before crossing the street” (R3)

All the sentences are saying the same thing. This is why language is so important, we can interpret language infinite ways and they are all talking about the same things. The computer will recognize all of these things and it will average out what the meaning of the sentence is

After many training of the pathway the AI has universalized the groups of pathways. R1, R2, and R3 disappear and what you have left is the average of all the training data located in that area.

The AI not only averages out trees in pathways but entire pathways. The purpose is to universalize similar pathways into one pathway. This one pathway will contain the fuzziness of infinite possibilities. We can also take this universalized pathway and encapsulate that to make even more complex pathways.

Applying my AI program to real world situations
Language encapsulates the instructions to do something. We can keep encapsulating sequences in itself to form a very complex problem solving technique. In the problem below you will get an understanding of how encapsulation works.

ABC block
In this problem we want to use a basic intelligent problem that kids can solve. The ABC block is just 3 square blocks and the robot has to find a way to stack the blocks in an A B C format.

We accomplish this problem with the English language. We simple tell the machine: “I want you to stack the blocks up starting with C then B and finally A”. From this one sentence the robot should be able to finish the task. It doesn’t matter what order the blocks are put in. It doesn’t matter where the blocks are. If the robot understands the sentence it will carry out the command. Of course we have to train it to understand the steps to accomplishing this easy task. Let’s say that we had the blocks in this order and we wanted the robot to stack the blocks up from ABC:

We learn from teachers that in order to solve this problem we: “locate the C block”, “Take the C block and put it on the ground”, “then find the B block and put it on the C block”, “finally find the A block and put it on the B block”. These sentences are trees that tell you what to do in order to solve this problem. These trees were trained by a teacher many many times before you can attempt to solve this problem. By the way, these trees are your conscious.

These trees encapsulate the instructions to accomplish a goal. We train them by teaching the robot that this sentence is followed by these instructions. The robot will create pathways in memory that will store the instructions step by step. This may not sound impressive but let’s say you wanted to solve something like lining up the entire alphabet letters in a certain order. If you preprogram the solutions there will be couple trillion possibilities you have to manually preprogram. With trees we can encapsulate instructions in the form of sentences. And these sentences can be encapsulated into even more complex problems. Thus making a complex problem into a simple problem.

HLAI Pseudo codes
Chess game:
Player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, player recalculates the future steps, player makes a move, the opponent makes a move, and so forth until the game is over.

Human robots:
There are two players in the game of life: the robot and life. The robot makes a move, life makes a move, the robot recalculates its future steps, the robot makes a move, life makes a move, then the robot recalculates its future steps, the robot makes a move, life makes a move, then the robot recalculates its future steps, the robot makes a move, life makes a move, and so forth until the robot is shut down.

In addition to this the AI robot activates objects that it recognizes from the environment. Anything that has association with the object it recognizes will pop up in its mind. You can call this type of thinking the conscious. The robot gets all its training data straight from the environment. The more sophisticated the society is the more intelligent the robot becomes. Without a society this robot won’t have the ability to use language. Without language then complex thoughts can’t be created. This will eventually put the robot in the animal intelligence category.

The computer codes look exactly like the Universal Artificial Intelligence program. There are differences in the way the pseudo codes are built. One is the fact that the HLAI has 5 senses in its pathways instead of two senses like in the videogame program. The storage part has another addition. The UAI works by grouping data into two categories

1. primitive learning group
2. commonality group

The HLAI stores information by a more defined learning group. It too also has two categories:

1. learning group
2. commonality group

The learning group and the commonality group must co-exist in the same storage area. This means that similar learned groups will be put in an area that might not be similar because the learned groups may not be similar. For example, a tiger and a giraffe are animals and we classify them as animals but in their commonality groups they look totally different.

[In the notes webpage I will outline the stages of how the robot will learn information]