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Referring to FIG. 1, the present invention is a method of creating human artificial intelligence in machines and computer based software applications, the method comprising:

an artificial intelligent computer program repeats itself in a single for-loop to

receive information, calculate an optimal pathway from memory, and taking action; a 3-D storage area to store all data received by said artificial intelligent program; and a long-term memory used by said artificial intelligent program.

Said an AI program repeats itself in a single for-loop to receive information from the environment, calculating an optimal pathway from memory, and taking action.  The steps in the for-loop comprises: 

1.  Receive input from the environment based on the 5 senses called the current pathway (block 2). 

2.  Use the image processor to dissect the current pathway into sections called partial data (also known as normalized visual objects).  For visual objects, dissect data using 6 dissection functions:  dissect image layers using previous optimal encapsulated trees, dissect image layers that are moving, dissect image layers that are partially moving, dissect image layers by calculating the 3-dimensional shape of all image layers in the movie sequence, dissect image layers using recursive color regions, and dissect image layers based on associated rules (block 4). 

3.  Generate an initial encapsulated tree for the current pathway and prepare visual object variations to be searched (block 6).

Average all data in initial encapsulated tree for the current pathway and determine the existence state of visual objects from sequential frames (block 8). 

4.  Execute two search functions to look for best pathway matches (block 14).

The first search function uses search points to match a visual object to a memory object.  It uses breadth-first search because it searches for visual objects from the top-down and searches for all child visual objects before moving on to the next level. 

The second search function uses guess points to match a memory object to a visual object.  It uses depth-first search to find matches.  From a visual object match in memory the search function will travel on the strongest-closest memory encapsulated connections to find possible memory objects.  These memory objects will be used to match with possible visual objects in the initial encapsulated tree.  This search function works backwards from the first search function.

The first search function will output general search areas for the second search function to search in. If the second search function deviates too far from the general search areas, the second search function will stop, backtrack and wait for more general search areas from the first search function.

The main purpose of the search function is to search for normalized visual objects separately and slowly converge on the current pathway (the current pathway is the root node in the initial encapsulated tree).  All visual objects in the initial encapsulated tree must be matched.  Search points and guess points call each other recursively so that top-levels of normalized visual objects will eventually be searched as well as bottom-levels of normalized visual objects.  

5.  Generate encapsulated trees for each new object created during runtime and include it in the initial encapsulated tree.

If visual object/s create a hidden object then generate encapsulated tree for said hidden object.  Allocate search points in memory closest to the visual objects that created the hidden object (block 22).

If visual object/s activates element objects (or learned object) then generate encapsulated tree for said activated element objects.  Search in memory closest to the visual object/s that activated the element object (block 24).

If pathways in memory contain patterns determine the desirability of pathway (block 12). 

6.  If matches are successful or within a success threshold, modify initial encapsulated tree by increasing the powerpoints and priority percent of visual object/s involved in successful search (block 10). 

If matches are not found or difficult to find, try a new alternative visual object search and modify initial encapsulated tree by decreasing the powerpoints and priority percent of visual object/s involved in unsuccessful search.  If alternative visual object search is a better match than the original visual object match modify initial encapsulated tree by deleting the original visual object/s and replacing it with said alternative visual object (block 16 and 20).  

7.  Objects recognized by the AI program are called target objects and element objects are objects in memory that have strong association to the target object.  The AI program will collect all element objects from all target objects and determine which element objects to activate.  All element objects will compete with one another to be activated and the strongest element object/s will be activated.  These activated element objects will be in the form of words, sentences, images, or instructions to guide the AI program to do one of the following:  provide meaning to language, solve problems, plan tasks, solve interruption of tasks, predict the future, think, or analyze a situation.  The activated element object/s is also known as the robot’s conscious (block 18 and pointer 40).

8.  Rank all best pathway matches in memory and determine their best future pathways. A decreasing factorial is multiplied to each frame closest to the current state (block 26 and block 28).    

9.  Based on best pathway matches and best future pathways calculate an optimal pathway and generate an optimal encapsulated tree for the current pathway.  All 5 sense objects, hidden objects, and activated element objects (learned objects) will construct new encapsulated trees based on the strongest permutation and combination groupings leading to the optimal pathway (block 34). 

If the optimal pathway contains a pattern object, copy said pattern object to the current pathway and generate said pattern object’s encapsulated tree and include it in the optimal encapsulated tree (block 30).  

10. Store the current pathway and the optimal encapsulated tree (which contains 4 data types) in the optimal pathway (block 32).

Rank all objects and all of their encapsulated trees from the current pathway based on priority and locate their respective masternode to change and modify multiple copies of each object in memory (block 36). 

11.  Follow the future pathway of the optimal pathway (block 38).

12.  Universalize data and find patterns in and around the optimal pathway.  Bring data closer to one another and form object floaters.  Find and compare similar pathways for any patterns.  Group similar pathways together if patterns are found (block 44).   

13.  Repeat for-loop from the beginning (pointer 42)

The basic idea behind the AI program is to predict the future based on pathways in memory.  The AI program will receive input from the environment based on 5 sense data called the current pathway.  The image processor will break up the current pathway into pieces called partial data.  The image processor also generates an initial encapsulated tree for the current pathway.  Each partial data will be searched individually and all search points will communicate with each other on search results.  Each search point will find better and better matches and converge on the current pathway until an exact pathway match is found or the entire network is searched.  During the search process, visual objects will activate element objects (learned objects) or create hidden objects.  Each new object created by the visual object/s will generate their respective encapsulated tree and included in the initial encapsulated tree.  The optimal pathway is based on two criteria:  the best pathway match and the best future pathway.  After the search function is over and the AI program found the optimal pathway, the AI program will generate an optimal encapsulated tree for the current pathway.  All 5 sense objects, all hidden objects, all activated element objects (or learned objects) and all pattern objects will recreate (or modify) encapsulated trees based on the strongest encapsulated permutation and combination groupings leading up to the optimal pathway.  Next, the current pathway and its’ optimal encapsulated tree will be stored near the optimal pathway.  Then, the AI program follows the future instructions of the optimal pathway.  Next, it will self-organize all data in and around the optimal pathway, compare similar pathways for any patterns and universal data around that area.  Finally, the AI program repeats the function from the beginning.

The next couple of sections will emphasize on the robot’s conscious and how the conscious is used to solve problems, plan tasks, predict the future and so forth.  These sections are mentioned in detail in parent applications, but I will give a summary explanation so the readers can have a better understanding of how human intelligence is produced in a machine. 

The human conscious works by the following steps:

  1. The AI program receives 5 sense data from the environment.
  2. Objects recognized by the AI program are called target objects and element objects are objects in memory that have strong association to the target object. 
  3. The AI program will collect all element objects from all target objects and determine which element objects to activate.  Each target object might have multiple copies in memory so each target object will gather element objects from all or most same copies in memory.   
  4. All element objects will compete with one another to be activated and the strongest element object/s will be activated.
  5. These activated element objects will be in the form of words, sentences, images, or instructions to guide the AI program to do one of the following:  provide meaning to language, solve problems, plan tasks, solve interruption of tasks, predict the future, think, or analyze a situation. 
  6. The activated element object/s is also known as the robot’s conscious.

Referring to FIG. 2A, when the AI program locates the three visual objects: A, B, C in memory it will run electricity through these nodes and all of its connections. 

The mind has a fixed timeline.  Only one element object can be activated at a given time in this timeline.  This is how we prevent too much information from being processed and allow the AI to focus on the things that it senses from the 5 senses (FIG. 2B).     

FIG.2A

                

 

Intelligent pathways in memory

The main purpose of pathways in memory is to create any form of intelligence.  The pathways should contain patterns that will control the AI program to take action in an intelligent way.  Patterns in pathways control the robot’s body functions such as moving its arms and legs or searching/modifying/storing information in memory or thinking consciously of intelligent ways to solve problems. 

The pathways in memory can form for-loops, while-loops, if-then statements, and-statements, or-statements, assignment statements, sequence data, patterned data, classes, functions, random data, static data and all the different combinations.  The pathways are also able to form any type of computer program, including:  databases, expert systems, genetic programs, and AI programs.  Simple computer programs like a word processor or complex computer programs like the internet can form in pathways.  The pathways can even form self-learning and self-accomplishing behavior to solve arbitrary problems. 

The intelligent pathways can do anything that a state machine can do.  Self-organization between similar intelligent pathways is the tool that defines the state machines. 

As always, patterns and meaning to sentences is the key to forming different types of intelligence.  The robot gets its data from the environment (most notably from teachers).  Teachers will teach the robot how to form these intelligent pathways.  The robot will simply compare similar examples and average out pathways to find “patterns”. 

This method will replace the old idea of manual insertion of rules and data into an artificial intelligence program.  Most of the artificial intelligence subject matters are not used in the present invention, including:  neural networks, language parsers, expert AI programs, rule-based systems, semantic models, Bayesians probability theories, genetic programming, agent programs, and recursive planning programs.  The HAI program is a self-learning, self-awared, self-teaching, and self-modifying computer program that can play any videogame for any game console, drive a car, fly an airplane, build a house, write a book, start a business, cook, clean the floors or mow the lawn.             

 

Details of the conscious

The robot’s conscious comprises a computer program that is created by the intelligent pathways in memory.  As the robot’s brain selects an optimal pathway, this intelligent pathway will generate a computer program to do things (insert, delete or modify information that goes into the conscious).  As the robot’s brain selects optimal pathways over a period of time, the computer program is generated and functions to do one or multiple tasks.  

If the current situation is to drive a car, the conscious will have a computer program to drive a car, if the current situation is to fly an airplane, the conscious will have a computer program to fly an airplane, if the current situation is to do math equations, the conscious will have a computer program to do math equations.  While the robot is selecting intelligent pathways for a given situation, the intelligent pathways are creating the computer program inside the conscious to take action.   

The computer inside the conscious serves as an operating system and it can manage multiple simultaneous tasks.  For example, in terms of driving a car, the robot has to search and identify traffic lights, road signals and pedestrians.  At the same time, he has to drive between the two white lines on the road and avoid other cars/pedestrians.  In other cases, the robot has to plan routes in his head so that he knows what streets to drive in.  The rules and tasks of driving a car are all managed by the computer program inside the conscious.  Later on, I will give detailed examples of how the conscious works. 

FIG. 3 is a diagram illustrating the robot’s conscious.  The conscious is like an empty room and you can do anything in it.  You can extract a map of the city and draw lines/arrow on it.  Planning routes require that the robot extract a map image from memory and id the current location, draw lines to the destination location, and list the streets it has to travel in linear order.  The robot will drive the car based on the list of streets it has created in its mind. 

There are basically two containers in the conscious:  tasks and rules/facts.  Tasks are things that the robot has to physically or mentally do.  Picking up a book is a physical task and searching for relevant data in memory is a mental task.  The rules are data that is extracted from memory that are needed for a task.  Solving a problem require facts about a situation.  Rules are needed to be followed while doing a task.  For example, when driving on the road, there are rules that you have to follow.  When playing baseball, there are rules to follow for each player.

 FIG. 3

The intelligent pathways selected by the robot during a situation will define what tasks to do at certain times and what probable rules to follow at certain times.  To tell you the truth, the rules are mostly made up of if-then statements.  If the robot recognizes this object then it will take this action.  However, there is no definite law that says rules have to be if-then statements.  They can be any discrete math function, like when-statements, for-loops, complex recursions and so forth.           

Another feature of the conscious is the identity container.  The current pathway (the current encountered situation) contains 4 types of data types:  5 sense data, hidden objects, activated element objects and pattern objects.  These 4 data types will define objects, events, actions, object interactions, object relationships and so forth.  Basically, they create a self-defining and self-adapting semantic network based on the data in the current pathway.  There are some object interactions that are so complex that even semantic networks can’t define.  Things like object property and complex object interactions are things semantic networks can’t represent. 

A self-defining semantic network is a network that doesn’t need computer scientists to write the functions.  The network is created by itself from data learned from the environment (the current pathway). 

In the identity container are very important things the robot believes, such as its primary goals in life, what are the rules that it must follow and what does it like and dislike.  This container is important because the robot is “self-aware” and constantly knows certain things and doesn’t need to search for these things in memory.  When the robot encounters objects/events/actions from the environment it extracts the strongest facts in memory related to these objects.  Knowledge is like a water valve.  When the robot’s task is to play baseball, the most important knowledge about baseball pours in from memory into the conscious.  When the robot encounters a friend, the most important knowledge about this friend pours into the conscious.  In other cases, the robot can think of a bird and the bird’s knowledge will pour into the conscious. 

Each object/event/action in memory is like a forest of network that is neatly organized.  

The identity container is created because the robot is constantly thinking about itself.  When it decides on items, the robot might say:  “I select the blue box”, the word “I” automatically extracts info on the robot.  When the robot does a task, he knows that he is doing the task.  If someone else in the environment is talking to or about the robot, then the robot will extract info about itself. 

The identifying of objects/events/actions and their interactions is based on the activated element objects from the robot.  The conscious thoughts of the robot identify objects/events/actions.  Conscious thoughts will be in the form of sentences and finger pointing to assign words to 5 sense data.  Other peoples’ spoken sentences can also define objects/events and actions in the environment.           

By identifying objects/events/actions in the environment and establishing relational links, object properties and object interactions, the data is configured in an optimal way to be stored in memory. 

The identity container is there because it is easier for the robot to make decisions and to do tasks based on what the robot wants.  Things the robot likes and dislikes are there so that he can make decisions quickly.  Instead of searching for the relevant data in memory during runtime, the knowledge is already there for the taking.  Of course, the knowledge within the identity container is based on the current situation.  When the robot has to make a decision, the knowledge will be based on what that decision is about.  For example, if the robot was in the supermarket and he has to decide on groceries, the identity container might contain likes and dislikes in terms of shopping.  In the task container, there might be intelligent pathways for decision making.  In the rules container, the rules of selection from the task of decision making might be included.   

There are two types of factors that change data in the robot’s conscious:  internal commands and external commands.  For example, the intelligent pathways selected by the robot’s brain can change the tasks in the task container.  The robot wanted to go to Mcdonalds to eat lunch, but changes his mind when he saw the taco picture from Taco bell.  The intelligent pathway decided to eat at Taco bell instead of Mcdonalds.  This example is an internal command.  The external command is based on people or things from the environment.  If the robot was playing baseball, the coach will tell the robot what position it has to play.  The coach will tell the signal codes of the game and the batting lineup.

The robot will have to first identify a command from the coach.  The coach might give a 10 minute speech and commands might be vague.  The robot might have to use logic to id the commands.  Once the command is found, the robot will change all the data in the containers in the conscious in terms of its task, rules to follow and knowledge needed.      

 

Using intelligent pathways to determine what knowledge to learn

When the robot is in school, he has the task of learning knowledge from teachers.  He also has the task of following the teacher’s every command.  Unless that task is dangerous or unusual, the robot might ask why.

In some cases, the robot has the power to control wither to learn knowledge or not.  If the robot doesn’t want to learn, he can stop paying attention to the teacher or to pretend like he is paying attention. 

He can pay attention to the lesson or he can ignore the lesson.  The controlling of the degree of learning is done via intelligent pathways from memory.  If the robot knows that he is in trouble with his grades, he can consciously decide to pay strong attention to knowledge from the teacher.  Paying more attention means learning more, which leads to a better grade for the class. 

The intelligent pathways in memory also determine fact from fiction.  If the robot was taught that the world is flat and one day he read a magazine and the scientist says the Earth is actually round, will he believe the magazine or what society think is true at that time?  Intelligent pathways of determining fact from fiction will be used and if the robot determines that the magazine is true, then he will put a reminder in the location of the false fact in memory and establish a pointer to the true fact. 

On the other hand, if the robot determines that the scientist is lying and his facts are flawed, then he will ignore the information in the magazine and only store a temporary encounter of the magazine.  The data from the magazine will be marked with a false fact and will not change the true fact that is already stored in memory. 

This intelligent pathway to determine fact from fiction is based on teachers teaching the robot how to find out if the material read is true or false.  The teachers have to teach steps to identify, analyze and determine if certain reading material is false or not.  Teachers have to train the robot to determine truth from different medias.  The robot might have to read a short essay and determine if the content is true or not.  Or the robot has to watch a movie and determine if the content is true or not.  Maybe there are some truths and some false information in the movie and the robot has to id as much true and false scenes.  In other cases, the robot has to read an entire website and determine where the true and false statements are. 

 

Learning to activate element objects based on a given situation (target object)

In the overall idea of the conscious described above, the brain recognizes target objects from the environment and activates element objects (conscious thoughts) from memory.  There is a way to teach the robot to activate specific element objects in its conscious based on a target object/s. 

A classroom is a perfect place to learn how to activate element objects based on a target object/s.  A teacher will teach the class how to analyze a situation or object.  After the situation is given the teacher will ask the students to comment on the situation and to share insights with other students. 

The teacher and students will comment on the situation and their comments can be facts, logical facts, common knowledge, question-answers, point of views and so forth.  Once the robot hears all the comments from students and the teacher about the situation, he knows what are important data.  The situation is the target object and the comments and facts are the element objects.  In the future, when the robot encounters the situation he will activate the comments from the students and teacher.

Grade school worksheets can be another method to learn to activate element objects based on a given target object/s.  In the worksheet, there might be a picture and next to the picture is the instruction:  “write down important facts about the picture”.  If the picture is a man, then important facts can be:  his name, telephone number, occupation and age.  If the picture is a snake, then important facts can be:  a reptile, poisonous, dangerous creature, has venom in teeth, don’t go near it and so forth.  The next time that the robot encounters a snake, the facts from the worksheet activates.  If the robot encounters a male, facts about his name, telephone number, occupation and age activates. 

In some instances, the worksheet can be associated with recognizing a target object and activating element objects.       

       

Learning to create a database system in memory 

In current database systems there exist a frame for each customer.  In the frames are slots such as name, age, gender, occupation and height.  The human robot learns to create a database system in memory.  The frames and functions of the database system are learned.  In memory, data is stored in a hierarchical-associated manner.  The intelligent pathways provide the search function for the data base system. 

What about the frames/slots and relational links to other customers?  How does the robot form these frame/slots?  The answer is right in front of your eyes.  When the robot reads a form about someone, the form lists important facts about the person.  The form will list the name, address, phone number, occupation and so forth about a person.  The form is optimal in that important facts, such as a person’s name are located at the top and minor facts, such as their pet are located at the bottom.  This numbering list determines the priority of which facts are important and which aren’t. 

If the robot encounters many forms and these forms are all similar to each other, whereby the name is at the top and minor facts are at the bottom, then the robot can create a template form in memory.  Since a form is associated with a person and the robot encounters this association numerous times, then a template form can be created for all people the robot has met and will meet.

If the robot meets a girl, called Jessica, at a party that he has never seen before, he will store the visual images of Jessica in memory and create a template form for this person.  When Jessica tells the robot her name, the name will be stored in the template form at the name slot.  When Jessica tells the robot information about herself, the information will be stored in their respective slots in the template form. 

The template form is associated with Jessica at this point.  If the robot encounters Jessica in the future, this template form will activate and further facts will be stored in this template form.  Let’s say that Jessica told the robot her occupation in the second meeting.  The fact “Jessica is a retailer” is identified by the robot; and the robot logically assume her occupation is a retailer.  The sentence “her occupation is a retailer” will be stored in the template form under the occupation slot.  Why?  Because the occupation in the template form has association with the sentence.  The sentence:  “her name is Jessica” will be stored in the name slot.  The sentence:  “her phone number is 555-5555” will be stored in the phone number slot.  It’s all about association.  The different data types are compatible because of this type of network.  The sentences are sound sentences and the template form is a visual diagram.  These are two different data types, but the sound occupation is associated with the visual text occupation.

The search function for this database system is based on patterns.  The teachers teach the robot using supervised learning.  Here is the question and here is the answer.  The robot, through self-organization, will find the patterns between the question and the answer.  The pattern is the search function.  If the question and the answer example is very complex, the teacher can teach the robot steps of logic in order to get from the question to the answer.  The robot can then find out the patterns by comparing similar examples (FIG. 4). 

The question is the start and the answer is the end.  It doesn’t have to be a question-answer format, it can be a command-accomplish command format.  It can also be an input and desired output format.   

 FIG. 4

The steps are guides to make it easier for the robot to find the answer and to ultimately discover the search pattern.  The diagram above shows that there are three steps that have to be done in order to find the answer.  During the self-organization phase, when the robot’s brain compares similar examples, it will find patterns between each step. 

The search pattern in intelligent pathways is a combination of internal functions (searching in long term memory, searching in memory, determining distance, etc) and intelligent pathways.  In other intelligent pathways, there might be search patterns already pre-existing and that can be transported to the current search pattern.  For example, step1 might be an intelligent pathway that already exists in memory.  Instead of finding the pattern to step1 the robot’s brain can copy the search pattern from the intelligent pathway found into step1.    

 

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