Generating common sense knowledge (pt 6) using Human Level Artificial Intelligence

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This video shows a robot observing a movie using common sense. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence.

My robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, genetic programming, or MACHINE LEARNING.

When the robot watches a movie (observation mode), a lot of knowledge is generated on characters and scenes. These knowledge include: identifying characters, recalling facts about a character or groups of characters, generating common sense knowledge, producing logical facts, determining what body movements or facial features imply, identifying inner thoughts of characters, predicting the future of what might happen in the movie, keeping track of the plot, generalizing plots and complex scenes, and so on.

These extra knowledge generated on the movie gives the robot addition data about the movie. He is able to better understand what is really going on and to see the hidden plots behind the movie. Also, background information about each character is vital to understanding the plot.

In the video, the robot is observing blurry images and movie clips. Because the robot is intelligent at a human level, he can use logic and common sense to identify what objects are in blurry images or movie clips. Let's say the robot needs contacts and one day he loses his contacts, that doesn't make him shut down because he is unable to understand the world around him, visually. He is still able to adapt and understand that this blurry object is this object. He basically takes an object from what he sees from the environment and he extracts the closest match from memory. His brain compares the 2 images (fuzzy image from environment and clear image from memory) and determine if they are the same things.

Despite images or movie clips being blurry or unrecognizable, the robot's brain is still able to understand what that object is. The most important thing is that the robot's mind is able to identify objects, actions, and things regardless of the quality media. The media type can be blurry, pixelized, up-side down, distorted, stretched or partly missing. The robot's intelligent pathways are able to use common sense to identify the contents in any media type.

In other words, the intelligent pathways in the robot's brain can form computer programs in the robot's mind, to process images. No human programmers are needed to write codes to compare blurry images to clear images or identify if an image is up-side down. The robot's mind has a "self-defining image processor" to process any image function.