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" I have taken the human brain, reversed engineered it, and turned it into a software program. According to the patent office, I am the scientist who filed the first copyrights, first patent applications, and first youtube videos on this technology (2006 onward). I am not trying to patent portions of the human brain like deep learning applied to visual images. Instead, I'm trying to patent the whole human brain. All documents I have sent to the United States government describe every single aspect of a digital human brain and how it works. The most important thing is showing the public how my robot thinks -- what does my digital brain software output. I made 200 videos on youtube showing how my robot thinks while doing various human tasks, like cooking, playing videogames, flying an airplane or solving complex problems.

In 2015, Google tried to build software to play videogames. Fortunately for me, their software can only play simple games like Atari, using reinforcement and deep learning. Their software can't play goal driven games like pac-man, super mario or more complex games like Zelda. Google made an annoucement in late 2015 proposing a plan to solve this problem:

Deep learning + reinforcement planning + human-level AI

If you look carefully at my invention and compare it to Google's proposed idea in 2015, they look strikingly similiar. I have sent this idea to the US copyright office and patent office way back in 2006. The big craze in 2015 is deep learning. However, deep learning is a very small part of my invention (about 10 percent). There is a whole lot more components to a human brain than simply deep learning.

Human-level Artificial Intelligence is the official term used to describe a robot that can think and act like a human with college level intelligence. There is another recently coined term called AGI, which stands for artificial general intelligence.

When you apply human-level AI to videogames, the robot is thinking like a human. He is idenifying objects in the game, making decisions, planning strategies to beat the game, generating common sense knowledge, doing simultaneous tasks, doing recursive tasks, brainstorming new strategies, correcting itself, setting recursive goals and so on. By using Human-level AI, the robot is able to play any videogame because he is using human intelligence to play and not pre-defined instructions by a programmer. The robot can play new games that it has never played before.

In fact, this robot can do any human task. If he doesn't know how to drive a car, the robot can spend time reading books or attending driving school to learn this skill. He will need to practice driving in order to refine his skills. All knowledge in the robot's brain comes from attending school. No human programmers are needed to predefine language skills, or problem solving skills, or rules of life, etc. This robot simply goes to school to learn grammar or problem solving or decision making skills. "

NEW!!!!       Videos on Human Level Artificial Intelligence



    Human Artificial Intelligence:  Book4

    by Mitchell Kwok

     entitled -- Building super intelligent robots



The current artificial intelligence theories donít work (2006)
Some people think that building a robot to open a door is a very easy thing to do.  Reality is that it is very difficult to program a robot that can open a door (under any circumstances).  MIT and Stanford University have been emphasizing this problem for a very long time.  They use hierarchical recursive planning programs, such as the strip program to solve the problem.  The robot needs to fulfill preconditions, such as possession of a key,  in order to open a door. What if the robot can't find the key? or the key chain has 20 different keys and only one key can unlock the door?  Even more difficult is the fact that the robot might not find the key and uses alternative methods to get into the room (such as breaking a window or calling a locksmith).  Expert programmers have to program in all the possibilities of opening a door and the possibilities run infinitely.

If scientists can't build a robot to do a simple task like open a door, then what makes you think they can build a robot to replace a janitor or nurse.  A human janitor can open a door under any circumstances.  In addition, the human janitor can do many other tasks like clean a room, mop the floors, answer questions, wash dishes, pick up a package from the post office, report incidents, and drive a car.  Robots have to be pre-programmed with specific instructions, both knowledge and procedures, for every single human task.  This is the main reason why software companies have not commercially sold robots to replace basic human jobs yet (janitors, nurses, teachers, construction workers, factory workers, farm workers, etc).

When I enter a library and go to the Artificial Intelligence section there are literally hundreds and hundreds of books on the subject matter.  Most of the books come from the same old categories such as Neural Networks, NLP, Bayesian's probability theories, predicate calculus, language parsers, grammar rules, rule-based systems, forward and backward logic, recursive strip programs, recursive planning programs (for 2 player games), machine learning, semantic networks, heuristic searches, and genetic programming.  The special books are those that donít conform within the existing theories of AI and uses totally different methods.

I have concluded that none of the pre-existing AI methods can help programmers build a robot with human level intelligence.  At this point I started to brainstorm alternative ideas and attempt to solve the hardest problem facing artificial intelligence:  to build a robot that can do human tasks. 

My quest for human level artificial intelligence actually started back in 1999 when I was a student at the University of Hawaii.  One day, I was walking on campus and I was riddled with a question:  How come there are no humanoid robots walking on campus?  I thought to myself:  "surely, there should be robots that are commercially sold on the market that can at least replace janitors or bus drivers".    

After doing my own investigation and talking to professors, I realize that no one has designed a human robot that can do human tasks yet.  My professor was adamant that building a robot with human level artificial intelligence is an unreachable goal for the human race.  He showed me the latest books (2001-2002) from colleges specialized in AI, such as MIT and Stanford University.  In one of these books, there was "one page" at the ending that talked about human level artificial intelligence.  The author stated that HLAI is difficult (or impossible) to reach and this is a technology that is "yet to be discovered".

The main objective scientists are trying to achieve is something called ďStrong AIĒ.  This is a form of learning machine that can take past knowledge and use it to learn new knowledge.  For example, human beings use this technique to learn language.  We have to learn the ABCís first before we can learn how to read and write.  Our brain's take past knowledge on grammar and use it to learn more advance forms of grammar.

In terms of NLP (natural language processing), I reject AI programs that includes the following in their data structure:  computer text, language parsers, grammar rules, semantic networks, and manual insertion of knowledge into a computer system.  Human beings learn grammar in terms of a bootstrapping process.  They have to learn grammar in elementary school, then intermediate school, then high school, and finally college.  Same goes with learning math.  Before anyone can learn Calculus they have to first learn trigonometry.  Before they can learn trigonometry, they have to first learn algebra.  Before they can learn algebra, they have to first learn basic addition and subtraction.  Knowledge in a human brain builds on top of each other to form complex thinking.    

After studying Artificial Intelligence for over 7 years I can tell you that none of the existing theories or combination of theories can lead to a machine that has human level artificial intelligence (strong AI).  This is why there are no humanoid robots working in restaurants or businesses.  To sum things up, neural networks, NLP, Bayesian's probability theories, predicate calculus, language parsers, grammar rules, rule-based systems, forward and backward logic, recursive strip programs, recursive planning programs (for 2 player games),  machine learning, semantic networks, heuristic searches, and genetic programming can't be used to build a human robot.

The only way to build a machine with human intelligence is to build a machine very similar to a human being (two hands, two feet, one head, 5 senses, etc.).  There are no shortcuts or special AI techniques that can allow a machine to be intelligent at a human level.  The chatbots online or talking software are expert systems -- they are illusions to trick people into believing the machine has human intelligence. 

I have spent 6 years designing my own kind of learning machine.  It started out as a college project and it evolved from there.  I have written and published four books on the technology:

Human Level Artificial Intelligence book1
Universal Artificial Intelligence book2
Universal Artificial Intelligence book3
Human Artificial Intelligence -- building super intelligent robots:  book4

The books itself took me about 1 year to complete - from the first day that I wrote the pages to the day they were published.  However, the design of the Human Level Artificial Intelligence program and the Universal Artificial Intelligence program took me 6 years to complete.  A lot of hard work was put into this project and a lot of sacrifices were made.  If you want an idea on the magnitude of work I put into this project, just imagine brainstorming and writing 40-45 notebooks.  Each notebook has 400 pages, including front and back.  "My greatest regret was not publishing my work earlier".  

It took me this long because I needed to present one full software program to represent a human robot.  Most AI books give incomplete or theoretical information about their human robot.  For example, Stanford university talks about various topics in AI, such as planning programs, heuristic searches, semantic models, robot vision, machine learning, predicate calculus, etc, but they don't combine everything and explain how all parts interact with each other.  Most AI books use theoretical information and no implementable codes are presented.    

This explains why AI books try to solve basic 2-year old problems.  The most popular problems MIT and Stanford university try to solve are:  the ABC block problem, 2-player board games, and building a robot to open a door.  Other books neglect to even give examples because they have no idea how to implement their theories. 

In my books I focus on extremely complex human tasks, such as playing RPG videogames, writing a book, writing software programs, or making a comic book.  Writing software programs require tremendous amounts of intelligence, especially if you're writing software programs for a big technology company.  There is a reason why those people are being paid 6 figures each year.  Also, playing a game like Zelda (RPG) is a very difficult task.  I chose that game because when I was 15 years old I played that game and it took me 2 weeks to past.  If I wasn't intelligent at a human level, there was no way I could of past the game.     

Universal Artificial Intelligence
Expert programs are AI programs that were designed to solve a specific problem. An AI chess program is one classic example of an expert program. The purpose and data structure of the AI chess program is to play chess; it can't play checkers or do any other human task.

In terms of UAI, I'm not trying to build an expert program.  IBM's deep blue project can only play chess, MIT's autonomous car can only drive a car, and an AI checkers program can only play checkers.  The deep blue project can't drive a car or play tic-tac-toe, it can only play chess.  If human programmers want to change their AI program from playing chess to driving a car, they have to manually change the environment of the AI program -- this includes changing the variables, future prediction functions, probability theories, knowledge, etc.  They also have to modify their machine learning algorithms so that the AI program can learn and adapt.  A universal artificial intelligence program is one cohesive technology that can drive a car, fly an airplane, play chess, play basketball, or do any human task -- no human programmers are required to modify the environment of the AI program or change the machine learning algorithm. 

What exactly is the Universal Artificial Intelligence?  The UAI is actually a software that can play ďanyĒ videogame and past all the levels in the most optimal way possible.  Any game that exists for the Xbox 360, Playstation 2, Playstation 3, SuperNES, Gamecube, Atari, P2P, Gameboy or any videogame console can be used in the UAI program.

I feel videogames is a good example to use in my books and patent applications because I want to show people that my UAI program can do any human task. It can play sports games, first-person shooting games, role playing games (such as Zelda), action games, puzzle games, and fighting games. Instead of 2-player board games, or solving the ABC block problem or solving baby problems, I decided to focus my books on artificial intelligence to play videogames.

The goal is to build one software program that can do "any" human work. If the UAI can play a first-person driving game, then it can drive a real car. If the UAI can play a first-person flying game, then it can fly a real plane. Yet in another example, if the UAI can play a first-person cooking game, then you can build a robot that can cook in a restaurant.

This technology basically replaces human workers and allow companies and factories to run autonomously. For example, GM has over 50,000 human workers in their car factories. Using the UAI, all 50,000 employees will be fired and replaced with robots. The idea is to fully automate a car factory and to mass produce cars without human workers. This UAI is universal and can be applied to all fields, such as restaurants, post offices, factories, super markets, farming companies, and fishing companies etc.

This universal artificial intelligence doesn't exist yet based on current technologies.  We do not have a fully autonomous Mcdonalds or car factory yet.  I'm trying to patent and copyright this technology.  


Human Level Artificial Intelligence

The medical community has tried to define what a human conscious is for over hundreds of years.  No psychologist will tell you the meaning to a conscious because they donít know what it is.  My theory on the human conscious is based on a rudimentary analysis of human behavior.  I basically use myself as the guinea pig to find out what the conscious is.  I had to brainstorm and ask myself many questions in order to come up with a conclusion.

"The term, human conscious (n), used on this website means: a human being who is self-awared, alive, and thinking". 

The conscious is something that took a lifetime to accumulate.  The conscious you have right now started the moment you were born.  As you grew older the knowledge you accumulated from all your teachers are stored in memory and ďaveraged outĒ.  The conscious for you is the result of the average lessons you learned from life.  All the teachers you had, all the lessons taught by them are stored in memory (when I say teachers I mean parents, friends, strangers, teachers, tv lectures, etc).  For example, if you were crossing the street, the first thing your conscious will tell you is:  ďstop, look left, look right, and make sure there are no cars before crossing the streetĒ.  This lesson in crossing the street isnít a specific lesson taught to you by one teacher.  This lesson is the average lessons taught by numerous teachers.  The conscious is a construction of intelligence from a lifetime worth of learning.  If you recall all the lessons taught to you about crossing the street, the average of these lessons gives you the knowledge to cross the street.  There are more things that make up a conscious and I will be outlining them below.  

There are three main functions to the human conscious:

(1)     Finding the meaning of any given object (equals)
(2)     Finding information about an object (stereotypes)
(3)     Reminding the human to do things under certain situations (trees)

Finding the meaning of any given object
The most important aspect about human beings is our ability to understand natural language.  This distinguishes us from any living organism and is the main reason we are intelligent at a human level.  Sentences and words (which are objects) have meaning.  An entire sentence like:  ďthe cat jumped over the boxĒ, have meaning.  The meaning is we visualize a cat jumping over a box.  The power of language is very important because it brings order to chaos.  Especially when we live in a world where there are infinite possibilities we want to group the objects around us in a fixed way so that when we see it the next time we can identify it as the same thing.

Finding information about an object (stereotypes)
This one reminds us facts about an object.  Our brains' want to gather the most important and recent information about any given object.  Letís say that you have been friends with dave for a long time and you know a lot about this person.  Last week you saw dave get hit by a car.  Today, you go to work and dave was absent.  The first thing that you should be reminded of dave is that he got into a car accident last week.  That fact is the closest association to dave at the moment.  Then other secondary facts about dave pops up like he takes risks, is a trouble maker, doesnít do his homework etc.  This form of consciousness reminds us of important facts of an object/s.

Reminding the human to do things under certain situations (trees)
This one is very important because it guides us to do things under a given situation.  It's like an ultimate teacher that tells us to do things at certain times.  Not only does it tell us to do things, but it encapsulates all the ambiguous instructions.  One example of this consciousness is when you decide to go to a party.  When you are at home and getting ready to go to this party, instructions start to pop up in your head.  Things like ďwhat should I wear?Ē, ďwho is going to the partyĒ, ďWhat kind of party is this and what is the appropriate clothes I should wear?Ē.  These are reminders of instructions that you have to do in order to go to a party and "fit in".  The answering of these questions are the instructions. 

In some cases, the human conscious is similar to several people in a group discussing an event, object, or action.  the conscious can propose questions, answer questions, generate ideas, make comments, think, give advice, layout linear steps to solve a problem, debate with a member of the group, give encouragement, plan for the future, look up information in the brain, and so forth.  The voice in your head is like a debate that is going on and the purpose of the conscious is to plan and make intelligent decisions based on the current environment. 


Examples of the robot's conscious

The three pictures below show different types of robots: a robot driver, a robot soldier, and a robot cook.  The robot's conscious is the imaginary voice in his head that: provide information, think, give instructions, recall information, make decisions, solve problems, predict the future, generate future steps to reach a goal and so forth. 




Referring to PictureE, every frame of the robot's 5 senses are stored in memory.  These pathways store the linear data that the robot goes through to do a certain task.  There are certain goals and rules that are automatically done by the robot without given the instructions.  For example, the robot knows it has to stay a certain distance from the stove or the current task is to cook spagetti.  These tasks are implied in the pathways and doesn't require the robot's conscious to remind the robot all the different rules and goals it needs to do.  Only the most important facts/rules are activated by the conscious.  The human brain is very primitive and it can only focus on several things at any given moment in time.  The pathways guide the robot to do things automatically, while the robot's conscious gives the most important facts/rules during specific times.   

Videos on how the robot thinks


Details of the robotís conscious

The robotís pathways (also called the robotĎs experiences) store 5 sense data, activated thoughts, hidden data, and pattern objects.  Most AI researchers separate motor functions and perception.  In my robot design both motor functions and perception are stored in the robot pathways, frame-by-frame.  Also, AI researchers seperate the brain into parts: language processing/understanding, planning, reflexes, logic, and knowledge.  In my design, all brain parts are fused together into one function (this design is totally different from the physical structure of the human brain). 

FIG. 3 is one example of an intelligent pathway to shoot a basketball.  The pathway will repeat itself until a condition is met.  You can think of this as a state machine (computer program) that can loop itself to a previous state.  As you can see, these pathways can construct any state machine or computer program, static functions or linear functions.  Extremely complex intelligent pathways like playing videogames takes about 10-15 years to construct and it has the capabilities of playing "any" videogame.  . 

The robot selects intelligent pathways in memory based on the current environment.  These intelligent pathways construct one or more computer programs to take action.  These computer programs represents the robotĎs conscious.  Also, the robot's conscious is the imaginary voice that provides instructions to take action; it can:  do human tasks, do multiple simultaneous tasks, solve interruptions of tasks, solve conflicts of tasks, provide knowledge about an object/s, give meaning to language, generate common sense knowledge, solve problems, learn information, answer questions, follow commands given, search memory for information, or take any human action.      

These computer programs in the robotís conscious was created by lessons learned by teachers in school.  This method is called unsupervised learning in the real world (for more information about how the robotís conscious works refer to my books). 

Within these computer programs are functions and procedures to process information.  If we go deeper into the conscious, there are systems that uses information in containers and process them to output intelligence.  These containers include: the task container, the rules container, the identity container, the planning container and others (FIG. 6).  Furthermore, there is an empty space where work is done, images are extracted from memory, lines are drawn on the image to produce meaningful information, images are manipulated, words are written down, logical sequences are generated, etc (FIG. 5B).  The entire computer program inside the conscious was designed so that the robot is able to learn all information from teachers; and no programmers were needed to manually input information into the robot's brain. 

No programmers designed the elemental parts in the computer programs inside the conscious.  Notice I didn't include a semantic network in my robot's data structure for language understanding.  The reason is because the robot creates a self-defining semantic network, and a self-defining semantic network can evolve its capabilities.  There are some complex words and sentences that even modern semantic networks can't represent.  Other things like functions, procedures, frames/slots, data compatability, search functions, image processors etc, are all discovered on its own and no programmers were required to design them.  Even the task and rules container in the conscious are not designed by a programmer, they are discovered and created on their own. 


How the robot learns language

My robot does not use:  language parsers, grammar rules, lexicon systems, semantic networks, grammar classification, etc.  In terms of current NLP, words read by a software is parsed into a tree and elements in the tree are classified -- this is a noun, a verb, a declarative sentence, etc.   I believe that the robot has to learn grammar on its own from childhood to adulthood.  All knowledge of grammar has to be learned and programmers are not required to input grammar knowledge into the robot's brain.  Grammar knowledge, such as classifying sentences (declarative, exclamation, question, etc) or classifying word types (noun, verbs, adjective, etc) should be knowledge learned in terms of a bootstrapping process.

In Kindergarten, the robot learns the physical images of alphabets.  The data structure of the robotís brain doesnít include computer text of alphabets or numbers.  The sound of alphabets is also important because we need to understand what the alphabets sound like verbally. 

Next, teachers will string alphabets together to form words and sentences.  Words and sentences can describe objects, events, actions, and time.  This is how the robot learns knowledge in our environment.  Things like storing definition of an object, the images of an object in 3-d or 2-d space, the physical properties of an object, object interactions, entire events, object actions, and abstract situations can be classified using words or sentences.  And the learning of knowledge should be learned by itself and not through semantic networks, Bayesians network, predicate calculus, etc.  Also, the meaning of words and sentences should not be represented by a semantic network, but through human 5 sense data (sight, sound, taste, touch, and smell).   

After completing 1st grade, the robot's brain contains a wide variety of knowledge about grammar.  When the robot is confronted with a grammar problem, wither that be answering a question from a book or checking for sentence errors, the robot's conscious constructs a computer program, during runtime, to solve the grammar problem.  If a teacher ask the robot to answer a question from a book previously read, the conscious of the robot will construct a computer program to answer the question.  If the robot was asked to determine if a sentence is a declarative sentence or question sentence, the robot's conscious will construct an optimal computer program to answer that question. 

These computer programs in the robotís conscious was created by lessons learned from teachers in school.  The robot selects intelligent pathways in memory based on the current environment.  These intelligent pathways construct one or more computer programs to take action (for more information about how the robotís conscious works refer to my books). 

After completing 4th grade, the robotís brain has vast knowledge about grammar.  It can do any grammar problem from Kindergarten to 4th grade.  The simple problems from 1st grade are easy, while advance forms of grammar just learned are harder.  As stated before, the robotís conscious constructs an optimal computer program during runtime to solve any given grammar problem.  Different grammar problems construct different computer programs.  If a 1st grade grammar problem is presented to the robot, his conscious will quickly construct a simple type of computer program to solve the problem.  If the grammar problem was at a 4th grade level, the robotís conscious will take some time to construct a more complex computer program, whereby massive amounts of knowledge and rules are used.       

In high school, the robot has the knowledge to write essays and books and give speeches.  Every grammar task the robot has to do, a computer program is created in his brain to accomplish that task.  All knowledge builds on top of each other.  writing a book --> writing a letter --> writing a paragraph --> writing a sentence --> understanding words --> understanding alphabets.  In order to write a book, the robot has to learn the hierarchical grammar knowledge, starting with the understanding of the ABCs. 

Each year in school, the robotís conscious constructs ever-more complex computer programs to solve problems.  Knowledge on grammar is very complex when youíre at a college level.  The robot has learned grammar from kindergarten to college and over the years intelligent pathways are formed in the robotís brain to solve ďanyĒ grammar problem. 


The robotís conscious applied to math, science, art, sports and communication

The same method to learn grammar is used to learn ďanyĒ subject matter.  It doesnít matter if itís sports, math, science, communications, playing games, etc.  The robot learns knowledge in a bootstrapping manner, whereby knowledge is built on top of each other.  When youíre learning grammar, youíre actually using knowledge from other fields.  For example, some grammar problems require basic math skills or problem solving skills.  Thus, all knowledge is interconnected in some fashion. 

In terms of learning computer science, the robot has to know math, science, art, problem solving, grammar, and computer knowledge, etc.  Thus, we canít separate subject matters and learn them independently. 

This is why using language parsers, grammar rules, semantic networks and lexicon systems is a bad idea to process natural language.  

If the robot has to drive a car, itís conscious will select intelligent pathways from memory to construct an optimal computer program to drive a car.  If the robot has to play chess, itís conscious will select intelligent pathways from memory to construct an optimal computer program to play chess.  If the robot has to write a software program, itís conscious will select intelligent pathways from memory to construct an optimal computer program to write software programs. 

On the other hand, if the robot has to do multiple tasks at once, itís conscious will select intelligent pathways from memory to construct an optimal computer program to do multiple tasks.  Imagine the robot driving a car, texting a friend and playing chess at the same time.  The robotís focus is on one task at any given time, but it is devoting itís focus on three tasks.  This is accomplished by following these instructions:  play chess, stop, text a friend, stop, drive the car, stop, play chess, stop, text a friend, stop, drive the car.  The robot does this over and over again to joggle all three tasks.         

How exactly does the robot learn to do multiple tasks?  The answer is through school.  Teachers teach the robot how to do multiple tasks simultaneously.  The more you learn, the smarter you become at multi-tasking. 


Knowledge application (universal)

Letís say that the robot learned how to play games from a teacher.  The teacher teaches the robot that in games there are rules to follow and procedures.  A very rudimentary framework is given to the robot about games.  Next, the teacher will apply the game knowledge to different games.  The teacher teaches the robot about basketball and soccer and baseball.  In each game, the knowledge about games are the same, but the details are slightly different. 

With this knowledge about games, the robot can also play board games like chess, checkers and monopoly.  So, essentially, when the teacher teaches the robot about games, she is teaching the robot about all games in general; and the knowledge to play games can be applied to all fields and media.  The robot can play real-life sports games and board games; or the robot can play games that exist in a TV, such as videogames. 

Thus, the teacher is teaching the robot how to play any game.  The robot, on the other hand, uses common sense to play a given game by identifying the rules, procedures and penalties of that game.  Because the robot has a universal pathway in memory to play games, the robot has the ability to play any game, even new games or unknown games.

I use the term universal artificial intelligence because the robot can play any game.  The IBM's deep blue project can only play chess.  This robot can play chess, checkers, tic-tac-toe, monopoly, etc.  It can play any sports game in any media.  It can play any videogame for any game console.  And expert programmers donít have to change the robotís brain for every game. 

This technique is also used to solve any problem.  Teachers teach the robot the general way of solving a problem and the robot, through trial and error, can apply that knowledge to solving any problem.  The more the robot learns about solving problems the better it becomes in solving problems.   



Technically, this robot that Iím proposing can learn math, science, grammar, or any subject matter.  It can do any human task or multiple tasks.  This robot is what researchers have been trying to design for the past 70 years.  Human level artificial intelligence is the term I use to describe this robot.   

The only problem with this technology is that its "alive".  It needs to be alive in order for it to do anything human.  Human beings are self-awared because they have the ability to control their actions.  They make decisions based on pain and pleasure and the primary function of a human being is to pursue pathways that lead to pleasure and to stay away from pathways that lead to pain.  How exactly are software companies going to sell a robot or service that is self-awared?  One lawsuit filed on behalf of the 13th amendment and that's good enough to stop a multi-billion dollar software company from selling these robots. 


Inventions by Mitchell Kwok (patents pending)

email: amember1234@yahoo.com



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