Revolutionary AI system learns by using text, video, and audio.

The new AI system can connect information, that it gets from text, video, and audio. This new AI-based system can connect itself with multiple systems more effectively, than ever before. And that thing makes the system very effective. 

At this point, we must realize one thing. The AI itself is a platform-free application. So the system can use things like robot bodies and surveillance cameras as the physical tool that increases its operational environment. If learning AI works with the robot. Their AI can operate as a visible control system. 

Or it can operate backward and send information to the central system. That central system interconnects data that it collects from the network, robots, and other things. That allows the system can collect multipurpose data matrix. That it connects data from multiple data-handling tools. 

Machine learning means the ability to connect memory blocks. Same way, when the human brain connects neurons to virtual neurons, the learning machine connects databases to new entirety. And that is one of the biggest advances in data sciences. 

But without data is no databases. And without databases is no learning. The problem is how to input data into databases. The answer is that the system can use cameras and microphones to get information. Those cameras and microphones can be installed in the robot's body. 

If the AI uses robots as the medium it's even more flexible than pure network-based solutions. The idea is that AI can operate as a hybrid application. In those models, the AI solution is a combination of software- and hardware-based solutions. 

Complicated algorithms require powerful computers and effective internet connections. For learning machines information is the material. That is used for making new solutions. Learning machines can make virtual solutions or physical solutions. 

There is the possibility that the AI makes virtual models like CAD images of the work. And if the engineer accepts it, the system will transfer it to industrial robots and 3D printers that make a physical product. 

https://scitechdaily.com/revolutionary-ai-system-learns-concepts-shared-across-video-audio-and-text/

https://shorttextsofoldscholars.blogspot.com/

   

 The brain cells in a dish are learning faster than AI. 

The brain cells in a dish are learning faster than AI. But when we are thinking. That those brain cells at dish should learn only one thing. Those brain cells could learn a single thing faster than regular human brains because they need only one connection between them. So if we would minimize the data mass that is loaded to the neurons. We can make them react very fast. And also, they can learn that data faster than human brains.

In the cases, when data mass that loaded to the brain is minimal. There is a smaller number of connections between those neurons. So when neurons are handling data they must not search connections. 

The reason why brain cells in a dish are learning faster than brain cells in human brains. Is that the brain cells in the dish can use their entire time. for solving some problems. The brain cells in the human brain must sometimes concentrate on some other things. So there are cuts in the data handling process of human brains. And also there are lots more information that the human brain must handle than certain problems.

There is a reason why human brain cells in a dish are learning faster than human brains. The reason for that fast-learning process is that those brain cells in the dish must handle more limited information than normal brains. If the only thing what those brain cells must learn is some game like chess or video game those brain cells would learn the thing very well and fast. In the real world, what means places outside laboratories neurons must handle larger data masses than in laboratories. 

When people are walking on the streets, their brains must handle many types of signals. If those neurons would be in the dishes. The only thing that they must do is to learn some computer games. That thing is called sensorial adaptation or selective sensorial adaptation. The idea is that the neurons must be in the chamber where they are learning only one thing. 

And if that thing is the only stimulus that those neurons get. That thing will make them learn that only thing very well. Sometimes introduced a theory that "Kaspar Hauser" (1812-1833) the "boy who has grown in the barrel" or at least in total isolation was the victim of "selective adaptation" experiment. In that case, the only stimulus that this poor man got would be the military tactics. 

But the brain in the jar has brought one thing to my mind. Even those mini-brains would be small-size they are learning things. The memories of the person can transfer to those cells. And if there are enough brain cell cultures. That thing makes it possible to store memories in those cells. And then transmit them to another person. 

The thing is that the memories can transfer to the cell cultures. Makes it possible to talk with animals. If those memories can project to the screens of the computer that thing can give data. About how animals are living? The memories of the animals would download to the cell cultures and then those memories could transport to the screens of computers. Or of course, some extreme scientists would transfer the EEG of those brain cells to their brains. 

There is the possibility to transfer "trained neurons" to the nerve channel of the fetus. And that thing is opening new and very fascinating and same way frightening visions in my mind. That thing would make it possible to create the learning process that continues over generations. So that thing could be the real "deep learning". That means people like highly trained military officials would multiply. 

https://www.newscientist.com/article/2301500-human-brain-cells-in-a-dish-learn-to-play-pong-faster-than-an-ai/

https://en.wikipedia.org/wiki/Kaspar_Hauser

https://thoughtsaboutsuperpositions.blogspot.com/

How ERP learns things?

How ERP learns things?

The ERP (Enterprise Resource Planning) is the modified or expanded spreadsheet, what mission is to automatize things like storage bookkeeping. The way how ERP is interacting with databases is that when something is sold out from the storage or moved to another room, the system makes the automatic markings in the database, and the system can be acted as fully automatized.

If every single particle in a warehouse or building is equipped with an RFID system, the system would locate every single item in the building. That means that the controller would see the position of the video cannons or computers by searching, and if the system would be perfect, it would also see the reservations of that thing. This is one version of the fully automatized ERP-system, and that would be the advanced version of traditional ERP, where computers would make marking to the database whenever some item is sold from storage.

But what the learning ERP can make? If we would use an intelligent ERP-system, that means that if the ERP would allow locating every particle, while they are moving in some building, it can keep a record, where are the problems. If some particles would be destroyed very much, the system can also use cameras to find out where is the thing, what brakes things like insulators or clips. And that thing can cause momentum tools or something like that can impose again, what denies the damages of the parts.

The ERP system can also see if there are problems with physical movements in someplace, and that thing can be told to the managers, and they would see, is there some tight spot in the warehouse, that the material would not fit to go through that point. And that would cause the items, what dimensions, what will across certain value guide to some other route, and that would make working more flexible and effective because there is no need to wait for things in the work points.

The thing that makes the learning ERP-system more effective is that it would make orders when the storages are low. That makes possible to make things effectively and minimize the need to store things. The intelligent system will notice the real level of storage and also things like, how long those deliveries would take. And it minimizes the breaks of the production and the mixing of the storage. That means that the system will make sure that all old parts are used when the new ones are coming to storage.

And the system can also see, what kind of merchandise would be used mostly, and that means that the storages can be sizing for that kind of thing. If some merchandise is used mostly, that kind of stuff must be ordered lots of more than something, which is not used as much. And when the storages are running low, the system can send the ordering letter automatically to the merchandise deliverer. In this case, the artificial intelligence can fill the order form, when there is a need for more merchandise, and that thing makes it possible to make the work of human workers lighter because the system would make routine operations automatically.

How to teach robots to follow spoken orders?

How to teach robots to follow spoken orders? 

The idea of machine learning is the same as the people way to learn things, and that's why computer game-style platforms are very interesting things to teach robots. The action or learning process is similar, what is used to teaching dogs. The robot must connect spoken words to some actions, what the programmer can do with it. 

And this makes possible to make robots, which are understanding spoken language. This method is suitable for every kind of robot, and the idea is that the words are connected to recorded actions, so when the programmers are teaching the robot to open the door, they might say "open the door" and then they would transfer to use the virtual workspace, where they can use data gloves and probably Virtual Reality glasses, and make the action by using the virtual movements by using character, what is similar what is seen in the computer games. After the system has been successfully recorded the action, that means that the command "open the door" would activate this kind of movement series. 

The machine learning means that the character, what is the thing that is controlled by a computer would say some words, then the character is moving and acting as the programmer would want. And then those words are connected to the actions, what the programmer has made for the character. 

Moving of the character can be done by using the game platform, and then the virtual character would be used to test the effectiveness of the movements, and then they can transfer to the robot, which will connect those movements and other actions to the words, what the person says. This kind of technology can also be used with combat robots. And those robots can follow the orders, what the commander says without excuse. 

This kind of robot would be very interesting in the many dangerous actions. And thing what makes character dangerous is that some game character would get the physical form. When we are thinking about computer games the programmer, who will make commands to those systems could use the computer games to selecting the most suitable series of movements, and then the commander must just select the most suitable movements and connect the voice command to that series of movements. 

When we are thinking about the actions of robots every kind of action is a series of movements. That means the system would just connect the movements to the command, and that makes that kind of robot very dangerous. The robot itself would not care, what kind of commands it would get, and if the voice recognition or other safety system accepts the command, that allows the operator to give the command "sweep that area", and then the robot will do everything, what includes in that action. So this kind of thing can be used in the Pentagon's terrifying new robot army. 

Image.

https://upload.wikimedia.org/wikipedia/en/5/50/Cylon.jpg

How to teach character in computer games?

How to teach character in computer games?

When we are thinking about machine learning, computer games would be the right place to test and innovate that kind of thing. Computer games are one of the biggest parts of the world of computers, and when we are thinking about artificial intelligence, what would be an effective tool for making those games more realistic, interesting and difficult, we must think the method, what the programmer would use when that person would make the character learning things, what are important in the game. The thing is that the learning of the character would happen by a very effective method, where the movements of the most successful player would be recorded, and then that data would transfer to the automatized characters. That means that the system might copy the master player to the virtual characters.

And then somebody would ask, how the master player would be selected. The answer is simple, in every scenario of the computer game are the character, what is controlled by a human, and the character, what is controlled by the computer against each other. Then the master would be selected by a very easy method. If the character, what is controlled by the computer will win, that means that the computer is master, but if the human player is winning, the character, what is controlled by a human is master. And the code of the game would order the system to use the movements of the master.

This means that the movements, that are recorded from the human users are transferred to the character, which is lost in the battle. When we are thinking about this way to increase the skills of the computer-controlled characters, we might think, that when the player would step in the virtual game board, and start to play, the artificial intelligence would record the things, what the player makes, and then transfers those actions to the characters, what are coming after fallen characters.

That means that the computer-controlled figures, what are coming later would have more complicated movements, than the characters what the gamer faces in the first moment. In this scenario, the gamer would use more complicated movements every time, when that person would be advanced in the game, and the idea is that the gamer would teach the solution, how to fight against the player.

This would make possible to create harder and harder computer games. The machine learning is one of the key elements in computing, and by using this kind of method, computer games would become very difficult. When the gamer would play the game, the movements and actions would be recorded, and transfer to the next characters, until the computer would win. This is one version of using machine learning.

https://upload.wikimedia.org/wikipedia/en/thumb/e/e4/Tetris_DOS_1986.png/250px-Tetris_DOS_1986.png

Artificial intelligence can be taught by using games

Artificial intelligence can be taught by using games

Artificial intelligence can work with console- and computer games. The thing is that the movements of the gamer are collected to the database, and the computer would calculate, what mistakes every character, what it moves makes. Then it would simulate the choices, what the character must do, that its action would be more effective. The thing in artificial intelligence is that it can analyze things like the direction of sight and another kind of things.

And then the system would have parameters how to close the opponent, and what kind of things must be done. And the action of this system would be, that if some action would not be done again if the character fails. This kind of simulations can be extremely realistic because modern supercomputer technology can use multiple parameters, like simulated flight trajectories, and the thing is that the virtual world can be more difficult than the normal world.

There is claiming that the multiple net games are used to teach artificial intelligence to act as the military commander. In the world of the military, artificial intelligence would allow something, what we ever created before. The commander, who would not have emotions. The thing what artificial intelligence needs, when it will send the men to the mission is the tolerated level of own and civilian casualties. And that is the point, why this kind of solutions fascinates the national leaders. The feelings are a bad thing for the commander and that's why they wanted to cut off from the commanding system.

The target drones can be used to collecting data for artificial intelligence.

As you know, the difference between robots and human is that robot ever excuses the orders, and that means that the robot aircraft would just operate as the "kamikaze". That means the aircraft can operate as the regular fighter-bomber, but it can just impact with other things. There is a possibility, that the targets are pointed in the memory of the computer.  And if the target has enough points, which means that it would be the aircraft carrier or some other key unit, that aircraft can just collide with the target.

That system is mentioned for saving the aircraft for the most important targets, and if we would think the aircraft like QF-16 Robo-Falcon, that aircraft would be thought only the advanced missile. But it has more usages than with the normal missile. That aircraft can operate like normal aircraft, but it can also act as the missile if it sees the suitable target for that mission. During the exercises where the remote controllers operate with target drones, there is a possibility to collect the data, what can be used to train the artificial intelligence for the independent operations.

But those drones can also be used as the attack aircraft if they are equipped with the same systems with "Predator" drone, and the operator could fly that aircraft in the hangars of the targeted airbase. There is a possibility, that somebody would use "Troy horse" which means remote-controlled aircraft, what is made from the same fighter or some other aircraft, what is used by the enemy. That aircraft would just be flown in the hangars of the enemy airbase, or another important target like an oil refinery and ammunition factories. This can be the future of the battlefield.

Why the brains of insects are so fascinating?

Why the brains of insects are so fascinating?

https://kimmoswritings.blogspot.com/

The power of brains of insects is the reason for one very simple thing, and that thing is the neurons of the insects have connections with themselves. Those round or circle connections would allow that one neuron can do many things, and that thing makes most of the insects immune against VX-nerve gas. The reason why VX doesn't affect the bugs is that the bugs don't have synapse slit between neurons and muscle cells, and this would make impossible to get the effect in the synopsis slit, and the effect of the VX-nerve agent is that chemical denies the operate of the enzyme, what will break the neurotransmitters.

And if the human would have a nerve system, where the neurons would have contact with together only with electricity, which means that the neurons must connect straight together without slits, would the neurotoxins like VX lose the effect against that kind of species. The reason, why the wings of the insects are acting so fast is that every muscle cell has one neuron, what is connected to them, this makes possible to react extremely fast because in the emergency situations is only one way to act, and that is jumping to some direction before starting to flap, and the direction is ordering the neuron, what first activates. The order is given by the eye or another sensor, what first activates the neuron.

Learning makes neurons slow

The reason, why the neuro-agent would cause death is that the nervous system simply overexcites, and the reason is that the cell must handle multiple things at the same time. But when we are thinking about the overexcitement of the neurons in the point of view of insects, the situation that bugs would be exposed by nerve gases would make those insects more intelligent and allow them to learn more things than in normal circumstances, because those neurons would let more information to go thru them.

When the insect is young the operation of the neurons would be extremely fast because there are fewer connections to the neurons. And that means that the neurons would act like RISC-processors. They have only one way to act, and that thing makes also the people, who live in the extremely low stimulus field are handling things, what are thought to them very well, but they have problems with things, what are happening in the field, where the level of secondary stimulus is high.

And in those cases "unnecessary data" would cause that the number of connections in that neuron would increase, and it makes them slow.  The thing, what would make neurons slower is that even insects like flies are learning things. And the learning process would cause the neurons would get more connections, which means that the neurons must choose the connection, what it uses.

The thing is that there are some ideas, that by using nanotechnology the number of connections of the neurons would be increased. The idea is that the internal communication of the neurons would happen by using electricity, and the neurotransmitter used only in the communication between cells.  In real life, there would be no limit for the connections of one neuron, except the area on the surface of the neuron is limited. That means that the synopsis, what is connected to the surface of neurons must be very thin if there would be wanted to create many connections, but otherwise, one neuron can have billions of connections, if those synopses are small enough.

The nanotechnology would allow creating the very thin wires, which are made by using fullerenes or in the extreme visions by using the chain of the carbon or some metal atoms, what would create the synthetic connections for those neurons. Those artificial synopses can inject to the body, and then they would be inside the nano submarines in the tiny reel. Then the submarine would travel in the nervous system, and start to make new connections for the neurons.