The morph network can be the Frankenstein of the next generation.

The morph networks can mean many things. One of them is the network that can use in many roles. The thing is that the same network can operate in many situations. From everyday actions. To extreme situations like sudden accidents or war. 

The system will recognize the things like an accident by comparing the information that cameras give with the data matrix. That data matrix is stored inside the memory of the system. 

When the system sees the accident the data trigger activates the database where is introductions how the network must operate in that kind of situation. When the network or its sensors sees armed persons. It connects itself with a database where are its introductions to that situation. 

The network can morph itself when it faces certain triggers. When the image recognition system sees that the image matches with some condition. 

The system connects itself with a database. That involves the movements and all instructions. On how to operate in a certain situation or space? 

The new morph materials and networks are making machine learning more effective than ever before. The morph networks can be real or they can be virtual. 

The morph means the ability to make and remove connections between databases. That means the system can select sensors that are inputting data in its computers. The system can have two camera sets. Another camera set is infrared. For night vision. And another could be for day use. 

The morph network changes the camera system by using visibility as the trigger. When the visibility is low the system turns to use the infrared system. And when the visibility is good. The system turns to use daylight cameras. Because the system can cut the connection with a camera that is not in use saves the system that camera would not overload the system without purpose. 

That is an example of the morph system that changes camera sets by following light conditions. Those cameras can be CCD systems that are acting like insect net eyes. That kind of eyes can install on aircraft or drones. 

But the nanomachines are making it possible to create artificial physical morph networks. The nanomachines can be like small worms that are making connections between physical hard drives. When the robot will face the thing like city area. It will just turn the nanomachines to make connections with the hard disk that has the programs on how to behave and act in the city area. 

The morph network can act like artificial reflexes. When the system faces an emergency. It can remove unnecessary connections. And then it can use only the databases that involve responsibility for the risk. The system turns limited but it acts faster. Because it must not search for the right reaction so a long time as usual. 

The idea in this type of morph network is that they can make and remove connections. That thing makes them more effective than solid networks. When the system operates in a normal situation, there is time to ask which hard disk or database has a certain action. 

A large number of databases and connections are making the system very versatile. But otherwise, that kind of thing is making the system slower. If the system can remove unnecessary connections that make it more limited. But also faster. So that kind of thing can call as "artificial reflexes".

When the system can change the number of connections that makes it faster. In the cases where the system faces emergencies, it can start to use emergency mode. In that mode, the number of connections between databases. And the number of databases in use. Will turn as small as possible. That thing makes the system faster to react. 

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What mean to AI to understand?

 What mean to AI to understand?

What mean to understand anyway? We can do many things. And we might not understand them. When we are trying to think about the question of what means understanding? We are facing an ultimate question do we still understand anything? The fact is that I can put seven years old child to read the text about quantum physics and that child might read those texts pretty well. But does that child understand those words? The fact is that I can also use the Google text-to-speech application to make that thing. And this application will make its job well. 

The AI can make many things and if that application doesn't know about things like the mark of the sum or something like that. The programmer needs to store the characters of those things on the computer. The mark of the sum will store in the database, and then there will make the text that is connected to that mark. So the sigma-mark will trigger the words that are connected to that mark. 

1) To know means: That a character knows what to do in certain situations

2) Understanding means: To realize why actors must do things in a certain way.

When we are making the robot do something it makes things what we programmed in it. If we want to make a tennis robot that plays tennis with us, we might make a robot that hits the ball. The robot might have gesture control.

If the ball is coming to the robot it will hit it. Then robot must have some algorithms for how it aims at that ball. If the referee will give the pass to the robot, it must know the gesture and then make the pass. Or movement series what makes it pass. The robot must calculate many things like the right hit point and power. But then it might have one problem does it understand anything? 

The pseudo-understanding is that the AI can give pre-programmed answers to certain questions. 

It knows how to react to the ball. And simple gestures that are making the person who sits on the chair.  But could that robot play tennis in a real match? Does it separate the referee from the audience that might show similar gestures? The robot must "know" that it should not follow any other than the referee's marks. So the robot knows how to punch a ball. 

The ball acts as a trigger that activates a certain series of movements. The robot might have orders where the punch must and where the ball should not go. The robot would not strike outside the field, because it's programmed in there. And if somebody asks about why the robot doesn't hit the ball outside the field area, it can answer: "that's dangerous". 

If the programmer is put that answer to robot's computer. Or it might have an answer "that's prohibited" whenever the person asks it to make something that is not programmed in its memory. If somebody asks a robot to punch a ball to humans or vehicles robot might say "it's prohibited" and make a report to its operators. 

The reflex robot recognizes that some action is filling the notes that are stored in the database. After that, the action triggers the database. And then that database begins the response to that action. 

Those actions are programmed in the program of the robot's programs by programmers. The robot does all the time same things. There is a series of triggers that are activated by certain actions. So that robot has a reflex. A certain action activates certain types of reactions. 

The reflex automation is simple to make. When somebody says "good morning" to the computer, it might answer by saying "good morning". And then that computer might have a voice or image scanner that connects a certain workspace to it. If the computer uses an infrared camera or ultrasound-based system. It can also recognize a person. Even if that user has a beard or is in flu. The idea of those deeper-than-surface systems is to benefit the static components of the human body. 

Of course, the system can ask the person to identify self. The command that the operator gives for access to the workspaces. Might be "I'm Eric, open my workspace". In the place of that name is the operator's name. That means the system can also recognize if somebody tries to play as that operator. The system recognizes the face but asks the name of the operator. That uncovers if the person tries to use some other user's accounts. This is one version of artificial intelligence called "reflex automation". 

The thing is that the machine has some kind of model in its memory. When some action fits some models. That thing activates certain actions in the system. This type of system is effective. Artificial intelligence-controlled robots might make many things like activating traffic lights or bringing tea or coffee to certain persons. They know how to respond to some kind of command or action. But those computer programs don't know why that response is given. 

A robot or computer program has a series of reactions to how to react to something. And if something that is outside its databases is asked robot might say "I cannot do that thing". Or it can say that it transmits the problem to the system supervisor who is making an algorithm for that thing. And the time for machine learning starts to dawn.