Following instructions using Human Level Artificial Intelligence

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This video shows a robot reading and following technical instructions. There are no sound in the video because I wanted to show the viewers how the robot thinks while reading/following instructions.

Before the robot can read text, he has to understand how to read the text. There is a specific way to read text, based on several factors. These factors include: language, media type, length of text and so forth. Reading a Chinese book is totally different from reading an English book. Reading a technical manual is different from reading a comic book. There are rules and procedures that the robot must follow in order to successfully read text.

Next, the robot has to read the text and create a fabricated movie, which serves as the meaning to the text. This fabricated movie consists of images, words, flow charts, animation, diagrams to represent the meaning to the words read.

In addition to the meaning, the robot's conscious activates facts and logic from the text read. It uses logic to identify what tasks it has to do and to generate more facts on the instructions. These facts generated are used to truly understand the text read.

While the robot is reading the instructions, knowledge pours into primarily 4 containers: task container, rules container, planning container, and identity container. For example, if it identifies a task it will insert the task into the task container. If it identifies a rule it will insert the rule into the rules container.

The process of reading and following instructions is very complex. Some of the instructions in the robot's pathways are formed by repeated learning. Other instructions in the robot's pathways are discovered by trial and error. The knowledge that is poured into the containers are based on patterns and these patterns are discovered by repeated learning. No programmer is needed to define what data to insert into the containers.

Lastly, this form of language understanding doesn't use: machine learning, semantic networks, bayesian's network, planning programs, expert programs, language parsers, grammar rules, induction/deductions, predicate calculus, decision trees, genetic programming, rule based systems, and common sense systems, etc. Every knowledge the robot has in his brain is learned from teachers in school.

writing the software program

part1: http://www.youtube.com/watch?v=JtTeyXI-vv4

part2: http://www.youtube.com/watch?v=6BGIXIqUTLQ