Please Find below the summary of the main points in the 27 minute video, Ja I know it is a long video to watch, but if you like I-Robot and AI movies you will certainly like this one
It is time to arrange the stuff on the table 
Hopfield network is a combination between NeuroScience which is mainly biology and computer science, so our robot will start not just recognizing as HUMAN, but also REMEMBER as HUMANS do.
Hopfield neural algorithm consist of two main phases, the first one the storage phase which is mainly how the teacher arranged his table during a certain period of time, WHILE the second phase is retrieving a memory that is mainly represent a "stable memory" of arranging the tools and it is always the lowest memory that consume energy from the brain or in our case the AI brain
Here you find a flow chart of today's blog & video.
A blink from the past, so I have managed to minimize the recognition time to only 29 Seconds instead of 6 minutes, and also a remarkable training accuracy by using principle component analysis "minimizing images features" and maximizing my hidden network 
The first question occurred to your mind is why is it called Principle Component Analysis ?! Well because there are principles
Principle 1.Self-amplification
Principle 2.Competition
Principle 3.Cooperation
Principle 4.Structural Information
the above stated principles are called the self organization principles of neurobiology, which can be represented to mathematical theories that motivate neural networks to fit unlabeled input data, so the network starts to learn without a teacher and compress images on its own. That's cool
This is just an example of how the input ip web cam would like to the network, while the graph on the left represent our robot neuron brain movement while remembering, there are similar graphs for humans
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