And then the dawn of machine learning.

 And then the dawn of machine learning.

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Machine learning or autonomously learning machines are the newest and the most effective versions of artificial intelligence. Machine learning means that the machine can autonomously increase the data mass, sort the data and make connections between databases. That ability is making machine learning someway unpredictable. And that kind of thing makes the robot multi-use systems that can do the same things as humans. 

The reflex robot is a very fast-reacting machine. The limited operational field guarantees. that there is not needed a very large number of databases. And that means the system must not search the right database very often. That makes it very fast. But if it goes out from its field it will be helpless. 

When we are thinking of robots that can make only one thing like playing tennis they can react very fast in every situation. That is connected with tennis. There is a limited number of databases. And that means the robot is acting very fast. 

When a robot or AI makes the decision it systematically searches every single database. And if there are matching details to observed action. That activates the database or command series that is stored in the database. But the thing that makes this type of computer program very complicated is that when the number of stored actions is increased the system will slow.  

If we want to make a robot that can make multiple actions. That thing requires multiple databases. And searching for the match for the situation in every database takes a certain time. So complicated actions require complicated database structures. Compiling complex databases takes time because there are limits in every computer. And in the case of a street operating robot, the system compiles data that its sensors are transmitting to its computers. 

So the conditions that this kind of system must handle might involve unexpected variables like fog or rain. And for those cases, the system needs fuzzy logic for solving problems. In that case, only the frames of the cases are stored in databases by the system creators. And that system is compiling those frames with the data sent from the sensors. 

The waiter robot can be used, as an example of machine learning.

A good example of a learning machine is the waiter robot that is learning the customer's wishes. The robot will store the face of the customer to its memory. When it asks does the customer wants coffee or tea? Then the robot will ask "anything else". And in that case, the robot can introduce the menu. 

And then the customer can make an order. There are certain parameters in the algorithm. Those are stored in the waiter-robots memory. The robot is of course storing that data in the database. The reason for that is simple. The crew requires that information that they can make the right things for the customer. But that data can use to calculate also how many items the average customer makes after a question "anything else"? 

The robot can also store the face in the database that it can calculate how often that person visits the cafeteria. Then that robot can simply store the orders below the customer's face. And it learns how often a person orders something. If some customer is ordering some certain products always. The robot can send the pre-order to the kitchen. That they can get a certain type of order. When some customers will visit often and order all the time same thing, the robot can start to say "do you want the same as usual? For that thing the system requires parameters how often in a certain time is "often"? That was an example of the learning system.