Intelligent pathways

Learning from childhood to adulthood and how the robot’s pathways become more complex

The robot learns information in terms of a bootstrapping process.  What this means is that it has to learn information through stages in life.  First, the robot has to learn things in terms of a baby because there are no knowledge predefined in the robot’s brain.  Next, we send it off to school, starting with elementary school.  Then, it has to acquire higher level knowledge by attending high school.  And finally, the robot has to attend college, where its highest knowledge is acquired.  As it learns, information in the robot’s brain builds on top of each other to form complex intelligence.   

Just like how we humans have to learn to walk, to talk, to move, to eat, these robots have to go through life the same way.  Let’s illustrate the gradual forming of simple pathways into complex pathways by outlining a series of stages.  There are basically 7 stages of learning that the robot has to master before having the ability to do mundane human tasks.  And these stages are learned sequentially and they are not learned individually. 

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

1. innate reflexes

In this stage the robot will learn all the different objects that are in the environment from the 5 senses:  sight, sound, taste, touch and smell.  The robot was born into this world without knowing that we live in a 3-d world.  It has to learn and understand what objects look like in 3-d.  Things like cat, dog, table, chair, red, blue, car, house, I, her, him, loud, soft etc. are learned and stored in memory.  

Next, the robot will start to move its arms and legs from innate built-in reflexes.  Movement of the arms, legs, mouth; and controlling the vocal cords are things that the robot must learn first (motor functions).  These experiences are stored in memory in an organized way.  Curiosity will be the factor that steers the robot into doing things that it never did before.  Things like new objects it never learned before will have top priority compared to old objects it learned.  New sensations will be more focused on then old sensations.  By the time the robot learns most of the objects around him, its memory banks will be filled with data and things around the robot will be more familiar.  The meaning of objects will also be established.

2. Trained to do things

This part is where a teacher will guide the robot to do things that are appropriate and to force the robot to learn things that a baby supposed to know (FIG. 28A).  Things like walking, and grabbing objects, and throwing things around must be learned.  The guide is used so that the robot will learn important things that it can use to control the environment.  A thing like walking is important because it wants to get from one destination to another.  Writing using a pencil is important because the robot must learn to communicate with people by written words.  Things like walking and writing and speaking must be learned by a guide because we can’t pre-program the robot to learn these things.

Although the guide isn’t something we want to store in memory, the point is that the more we guide the robot, the stronger the desired created pathways will be.  When it is strong enough it can be used by itself and the guide pathway will be forgotten.  The robot will find a way to use the desired created pathway to accomplish a goal.  Walking for example, if the robot knows that walking will get it from one destination to the next, then when it sees food, it will use the walking path to go from its current location to the food.  Reward is also playing a part in this learning process.

Also, during this process simple sequential consequences will be understood.  Things like what is the consequence of dropping a ball, where should the ball be when you drop it, and solid objects and soft objects have different properties.

3. Sequential events

In this stage the robot begins to learn how objects interact with each other.  When two objects hit one another both objects suffer, when the robot fall down it’s painful, when a solid object is grabbed it has the same shape, but if a soft object is grabbed the object has a bent shape. So, sequential events will be learned. The consequences of the robots actions in comparison to the environment will also be learned. 

By learning all these things, the individual pathways in memory will turn more complex and long. The robot will be able to piece together the outcome of an event just by looking at its past. Another thing to remember is that curiosity is the key to new pathways. The more unique the event is, the more the robot wants to learn it. The old events learned many times before will be ignored because the robot learned it already, but the new sensations will guide it to learn new things. Think of curiosity as a form of pleasure and old sensation as pain. Since this robot does things in terms of pleasure it will look for new data from the environment.

At this stage, things like lying and magic can’t be distinguished yet. The robot will not be able to lie yet and if it sees a man flying in the sky or walking on water, the robot will think it’s real.

4. Sentence commands

This part will require the robot to know basic grammar like the names of popular objects found in the environment.  These basic grammar must be taught to the robot and understood. The rules program will do the rest by assigning the meaning (words) to a given object.  Even hidden objects must be understood like jump, run, walk, loud, soft, etc.  Once a basic language is established we can combine sequential events with grammar and force the robot to do things by using words as the tool.  An example would be if you say,  “sit down”, the robot will sit.  When you say: “pick up the book”, the robot will pick up the book.  When you say: “read the first paragraph”, the robot will read the first paragraph of the book.  These are commands that you give to the robot to indicate what you want it to do.  There is no deception, or lying involved in the command process.  It’s simply someone giving a command and the robot taking the action.  The robot might not understand a command and make mistakes, but having a voice in the head that tells the robot to do things hasn’t been created yet.

5. Giving the robot option commands

This part is an extension of the last stage.  Instead of saying a word and letting the robot do things, we can add words/sentences intto the pathways and let the robot decide what it wants to do.  This is very affective because using words to give commands will control the pathway and to ultimately form state machines.  In this example, the pathway formed an if-then statement. 

So, this command pathway represents what the robot will do.  If a teacher gives the command then the robot will listen; if it’s a friend that gives the command the robot won’t listen.  There are also innate likes and dislikes the robot will have and there are commands out there that tap into that kind of thing.  For example, if the robot was given this command: “pick the food you like to eat”.  Within the robot’s memory there are powerpoints that determine an objects worth.  Pattern finding will tap into that and pick the one with the highest powerpoints.  Commands like: “pick the color you like”, “eat the food you like”, “play with the toy you like”, “buy the present you want”, “wear the clothes you love”, and so forth will all depend on the robot.  These likes and dislikes can also be a learned thing.

6. Practice makes perfect

Now, let’s get on with a more complex way the pathways can be formed.  When we practice something like riding a bike, we are actually creating new pathways to ride the bike.  Practicing will help the robot to decide the best newly created pathway to pick to accomplish a goal.  We can build a pathway in memory that will treat practicing something as a command.

Referring to the diagram below, this example shows that by using English we can guide the robot to do infinite amounts of tasks.  This example is a practice pathway.  It uses a command that will tell the robot to do something until a desired outcome is present.  If it doesn’t accomplish the goal then it will repeat itself until the task is completed. At the same time this is happening more options can be added to this practice pathway like, if you practiced 7 times and you still didn’t accomplish a goal, then quite or when you are hungry and you don’t have the strength to shoot, then stop practicing.  The existing pathways will add, strengthen, or minus "trees" from it as the robot learns more (a tree comprises one or more interconnected pathway).  So, an intelligent pathway can grow as it learns more and knowledge can grow like a tree, whereby weak branches fall off, new branches are formed, and existing branches can weaken or strengthen. 

Another point worth mentioning is that language, in terms of words and sentences, can represent entire trees.  The words or sentences stored in pathways mark the beginning of a pathway (or trees).  Because words/sentences can represent pathways, we can use words/sentences as reference pointers to pathways.  Let's say pathwayA contains this sentence, "pick up the paper" and this sentence references pathway4, instead of copying pathway4 into pathwayA, the robot's brain can use the sentence as a reference pointer so a copy of pathway4 is not stored into pathwayA.  This method minimizes repeated storage of pathways/data in memory. 

Instead of following commands given by external people, there are other factors to consider before the robot takes action.  The robot will do the things that a society will consider appropriate at the time.  If a society says it should lie in order to not do a task, then that’s what the robot will do.  If a society says the command isn’t appropriate in this type of situation, then the robot will not follow it.  If the robot finds the command dangerous and it can really damage itself, then it will not carry out the command. This is where the inner voice that is the core of the consciousness is built.  The consciousness is the average of the things taught to the robot by society. 

7. Copy other peoples’ behavior

This part is a very powerful tool used to learn things.  We can go ahead and train a pathway that will allow the robot to copy certain things from what it sees.  Things that it sees on TV will be learned and copied by the robot.  Copying will allow the robot to learn the most appropriate things to do in a society.  When it is in a situation, it will do things in terms of what society, as a whole, should do.  The way it dresses, the way it behaves in school, the things that it likes/dislikes, how to dress, how to take care of itself, how to get money, how to get food to survive, what to say to certain people, how to make friends, how to get good grades in school, and finding answers to questions.  All these things are learned by copying other people in our environment.

Adaptive intelligent pathways

FIG. 34 depicts an adaptive pathway to beat a boss in the videogame, Contra. The strategy that I use on all bosses and on all videogames is to first observe what the boss is capable of doing. Next, I identify any weak spots on the boss and determine the sequence of actions I have to take in order to hurt the boss. Once a strategy is found and I identify the weakness of the boss, I repeat this strategy over and over again until the boss is defeated. I have been playing videogames for a long time, starting with Atari and onward, I have never fought with a boss that didn't have a pattern. All bosses in all games I ever played had predictable patterns.

FIG. 34 

According to FIG. 34, the player (which is the robot) is trying to find a weak spot on a boss. He tries one strategy, "hit the boss on the head" and that didn't work. The strategies the player uses and didn't work are remembered so these strategies are not repeated. Next, the player brainstorms other ideas and decides to, "hit the boss on the back of the leg when he lands to the left", this strategy works. This tells the player that one of the strategies to hurt the boss is to wait for the boss to land on the left of the screen and hit the boss on the leg. This adaptive pathway was designed to try strategies on a boss and to keep strategies that work and delete strategies that don't work.

After playing with the boss using 10-15 tries, the player has multiple strategies that it can use to hurt the boss. The next step is to string these strategies together and determine the most optimal way, the fastest way, to defeat this boss. The player keeps doing this until it discovers the pattern, the best strategies, to defeat the boss. The more it plays the easier it is to beat the boss. This adaptive pathway is universal and can be used to play any boss for any videogame.

Basically, what this adaptive pathway does is it generates a computer program inside the robot's conscious to find a strategy to beat a given boss. It generates new strategies it can use to hurt the boss, it rememembers all failed strategies, it focuses on success strategies, it discovers the linear strategies to use in order to maximize the hurt for the boss, and finally, it decides on the best linear strategies to destroy the boss. Furthermore, this computer program inside the robot's conscious can apply to any boss for any videogame console.

All intelligent pathways are structured hierarchically, and the pathways go from general to specific. For example this adaptive pathway to beat a boss in a videogame, at the higher levels, can be used to design a poster. A graphic designer might select a pathway to try new ideas and to use them in a poster. The good ideas that work will be used and the bad ideas will be discarded. Another example is doing a science experiment, this adaptive pathway can be used to try a strategy until the experiment is a success. It can also be used to solve math problems, or to shop for the cheapest groceries.

Complex intelligent pathways

Pathways from the robot’s brain builds on itself as it learns more from teachers.  Even something as complex as representing a hierarchy system can be generated by an intelligent pathway.  Things like parent-child relationships, who is the grandfather of the family, or what does having a brother really mean, can be represented by intelligent pathways.  When people say “that’s your father”, there are lots of complex things we need to know before we can understand what the sentence really means.  These complex things, include: “where do humans come from?”, or “parents are supposed to take care of their kids” or “everyone has one female parent and a male parent” or ”the male parent is the father and the female parent is the mother”. It is a very complicated intelligent system when it comes to representing a family tree.

The whole idea is to use these intelligent pathways to form static or linear state machines (computer programs). We need the pathways to form:  conditional loops, discrete math statements, classes, methods, static variables, recursions, various data structures, operator functions, graph searches, etc.

Intelligent pathways construct a computer program for a given situation; it should have the ability to modify the computer program as it learns more.  All aspects of the computer program formed by intelligent pathways can be changed.  These aspects include: variables, frames/slots, classes, functions, conditional-loops, recursions, search functions, data compatibility, and so forth.  For example, if the government changes the laws for the game of basketball, the robot has to learn the new laws and modify existing laws in memory regarding basketball.  In other words, the robot has to modify intelligent pathways in memory that represent linear procedures to play basketball.  Also, it has to modify knowledge stored in the brain on basketball such as, the rules, facts, procedures, and playing positions.  

Driving a car is one example of a human task.  When the robot needs to drive a car, its conscious will extract the necessary knowledge in order to drive.  The conscious will construct one or more interconnected computer programs to drive a car.

The intelligent pathway to drive a car was created from all knowledge learned about driving.  Knowledge would include lessons from teachers, books read on driving, trial and error experiences driving a car, driving examinations, and TV lectures on driving.  The totality of information learned on driving constructs intelligent pathways in the robot’s brain to drive on the road and highways. 

The brain of the robot is so well developed that it can learn to do any human task via reading books or through trial and error.  If the robot wants to be a doctor he has to go to medical school, if the robot wants to be a computer scientists he has to go to a 4-year college, if the robot wants to be a graphic designer, he has to go to art school, etc.  The knowledge learned on each subject matter will be stored in memory and intelligent pathways will be formed.  Both knowledge about the subject matter and the intelligent pathways to extract and manipulate data are stored in the robot’s brain.    

Even the knowledge learned through books or teacher lectures are selectively extracted and stored in the brain in an organize way.  For example, if the robot reads a controversial article, he will select only the facts from the article that he believes is true and he will try not to store false information. 

There might be ambiguous books and the author does a poor job in explaining things.  The robot can read vague information and translate it into a form that is understandable to the robot and store that information in memory.  Sometimes the robot can summarize a long book into short notes, so he doesn't have to store every detail of the book.  These summary notes can also organize details of the book by grouping information into manageable topics. 

Storing minimal amounts of data to do human tasks 

The intelligent pathways in the robot’s brain should be structured so that only important information is stored and minor or repeated information are eliminated.  For example, the for-loop in a pathway is used so that the robot’s brain can jump back to a previous state.  If the for-loop wasn’t used, the intelligent pathway will contain multiple copies of the same procedure.  The for-loop in pathways minimizes repeated data storage to save disk space. 

Just like creating integrated circuits, the pathways in the robot’s brain has to minimize repeated information.  These intelligent pathways have to be constructed in an efficient manner. 

In building computer chips, a programmer has to determine what kind of algorithm to use to minimize an integrated circuit.  In the case of intelligent pathways, pattern objects contained in these intelligent pathways should "self-minimize" intelligent pathways.  The data in pathways should organize itself so that data is void of repeated copies, procedures are stored in optimal areas, and data is stored in optimal areas so that retrieval functions can access the data efficiently.    

As stated numerous times before, the creation, modification, and deletion of intelligent pathways in the robot’s brain are done through lessons from school.  The lessons by teachers and books, teach the robot’s brain to form and modify intelligent pathways. 

Developing search functions in intelligent pathways

In some ways, the robot’s brain is no different from a human brain, despite dissimilarities.  Although I didn’t design the robot’s brain with the same parts as a human brain, the data in the robot’s brain self-organizes to form parts that are similar to the human brain.  For example, the robot doesn’t have a predefined area for language understanding.  In a human brain, there is one area that stores information on language understanding and grammar.  My robot’s brain wasn’t predefined where the language part is located, instead, as it learns grammar from school, the language part develops somewhere in the robot’s brain. 

The same goes with other parts of the human brain.  I didn’t define storage areas for each of the 5 senses into the robot.  However, as the robot learns more from the environment, these 5 sense data are stored in their respective senses.  For example, the smell of an orange is stored near the smell of an apple because they have common traits, like smell data.  So, eventually, the robot will form different parts in memory very similar to human brain parts.  In the robot brain, there will be a part for logic, language understanding, visual cortex, sound, taste, touch, smell, emotion, problem solving, and knowledge. 

All information in the robot’s brain is global.  What this means is that data in one part will intersect with data on other parts.  For example, the object apple might be scattered throughout the entire brain.  There might me an apple stored in the visual cortex, the smell area, the logic area or even the touch area.  The primary place to store an apple object is called its masternode and the masternode has reference pointers to other copies of the apple object located in different areas of the brain.  During the self-organization phase, repeated data will be eliminated or, at the least, minimized.  Search functions to locate the apple object, will first locate the masternode.  If the masternode is found, its reference points will point to other copies of the apple object, and each copy will be searched based on priority.              

The intelligent pathways has to form search functions to search for information in the robot‘s brain.  These information includes, searching for visual data, searching for rules, searching for answers to questions, searching for relevant information about one or more objects, searching for ideas to solve a problem, searching for memory experiences, searching for solutions to problems, searching for steps to accomplish a task, searching for future outcomes of a situation, and so forth.

If you look at google’s search engine, they spent 15 years developing their algorithms.  The robot’s brain depend on intelligent pathways to search for information for a given situation.  These intelligent pathways has to form search functions automatically, without programmers inputting search algorithms.  Furthermore, the search functions in intelligent pathways has to modify itself if the environment changes.

If you think about searching data in a massive database, this problem is very complex.  In database systems there might be different topologies, media types, and data types.  My goal was to design the intelligent pathways so that regardless of what data are stored in the robot’s brain and how these data are organized, the intelligent pathways will still be able to search for information using the “best search methods“.  Not only does the intelligent pathways develop optimal search methods, they will also develop the fastest search methods.     

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