The network-based solutions can help to research AI.

When we are thinking the AI as a tool. That analyzes things there should be something that confirms the solution. The confirmation of results can be made by using multiple data handling tools. Those data processing tools can form the network of the multiple independently operating units. That works with the same problem. When data processing units are making solutions. They send that thing to the central processing unit. 

And then if there are no errors or differences that solution would have no mistakes. But there is the possibility that some data processing units would get a different solution than the main group. And the system should inform the user that there is some kind of anomaly in the solutions. The information that the user gets. 

The model of modern AI is that there are two layers of AI. 

1) The AI-based operating system can independently determine the retake of the code of the linear AI-based software. The AI-based operating system can choose the algorithm that is in use in each case.

If the AI would pick up things like stones there is an error level that makes the operation acceptable. The AI solution tells that 12 stones must pick and put in a bag. And then the operating system would make the software run 12 times for collecting enough stones. 

2) The AI-based solution that controls things like robot hands or data collection. The AI-based software is the solution that is running on the AI-based operating system. The solution would see that if the tool that it uses is not fine. That means that the system might first choose the shovel. 

But if the stones are too heavy. The software might ask to use another tool like a forked stick hand. In that case, the operating system just turns responsibility for the action to another hand. Or it can call another robot to the place. 

The learning system measures the weight of the stone. And then it makes the query which machine has enough powerful tools for picking up those stones. The controlling AI can search that can the system make its job. And if there are not strong enough robots it can ask permission to call assistance from other companies. The system can tell that "there are about 100 kg stones that must put the kevlar bag". And then the system can ask the machine that can make that thing. 

The operators must determine the error level. In this case, there must be a certain part of the processing units that gets a certain value. 

Might be an example like that 2/3 processing units have this kind of solution. But because 1/3 has a different solution. That means there is possible that there is a mistake. And then the user can use the solution but the system can retake that operation. The thing is that the neural networks allow control and observe the operations of the AI from outside it. And that thing can use to create more effective and powerful code. 

The outside system would observe the functions of the main system. And that information can contain data like. Is there some kind of problems with memory handling? Or are there some unnecessary loops? And that thing makes it possible to create more effective code. The thing is that the system that drives the program code of the AI might also use the AI. That means the system can independently retake the part of the code if the operation is not successful. And that thing is making those systems extremely powerful. 

The fuzzy logic is making the AI effective. 

When we are thinking about the case that the AI should calculate things like lorries. There are two ways to make that thing. The AI can use certain logic that makes it slow. A certain logic means that the AI has images of every lorry in the markets and then the system would take the image of every car. And then the AI can compare the images that it takes with images stored in the database. 

That thing takes time. Another way is to take images by benefiting the CCD camera. Or the CCD camera's electro-optical element. When the system takes an image it can compile it with the matrix images pixel by pixel. In that case, there is a port image of the typical representers of every type of vehicle. What the system can face on the road. 

The system compiles the image by using the port image. When a certain number of pixels are matching the system would recognize that the vehicle is a lorry. If the system would need deeper analysis, that thing can send an image forward of the data line to confirm the vehicle's type and owner. In the case that we want to make a car that drives automatically, we must remember that the bus that comes from the stop has the right to come first from the bus stop. 

In the same way, the emergency vehicles must go first if they are at the emergency drive. The AI can have images of this kind of vehicle that need special attention and actions. So when the CCD camera makes the match with those things it can slow down the vehicle. The fuzzy logic means that there is a parameter like 80% of the data must match for some case. And that thing allows the AI to make something. 

If the system uses precise logic even the different text or color in the object can make the system react the wrong way. The fuzzy-logic means that the system cannot always get the precise same data from nature as from a controlled environment. The cars that the system calculates can be dirty. Or they can have something like ski boxes on the roof. And the system must separate and sort them by using images that the CCD camera takes from traffic. That case gives information about what type of vehicles are traveling on the road each time. 

Image:https://scitechdaily.com/images/Artistic-Artificial-Intelligence-Concept-1536x1152.jpg

https://thoughtandmachines.blogspot.com/

Large neural networks have a larger number of abilities.

Above this text is the model of the neural network system. Every point in that diagram is the ability or skill that the network has. And every line in that image is the connection between those skills. So every point in that image is the database. And the line is the database connection. Every single database can have a limited number of connections. Everything that the robot must do is stored in databases. And there is a series of actions that are connected to a certain table. 

The neural network can be physical. It can be the network of physical systems like computers and surveillance cameras. 

Or it can be virtual. The neural network can be the network of skills. And whenever the system learns a new skill it expands. The network of skills means that every single action requires sub-actions. The sub-actions like turning the steering wheel can use in many places. Same way robots can turn cars, tractors, and forklifts. So that means the robot can use the same skill to turn every vehicle that has steering wheels. The virtual system means a large number of networked databases. 

The things like red traffic lights can act as triggers that launch a certain reaction. So when a robot sees red traffic light. That thing launches certain action. A great number of databases means that AI can search solutions more effectively. If the system already knows that the screwdriver is the tool it can search that thing faster. In that kind of operation, the robot goes to the toolbox. If there is no screwdriver it might ask alternative places. So the operator tells that maybe that tool is on the table. So the robot searches that place. If that screwdriver is there, the robot would involve the table to list places where the screwdriver could be. 

Of course, the robot can search automatically screwdriver from the floor. The AI uses image recognition for separating the objects. If there is no screwdriver the AI can search the image of that tool from its memory and ask is this screwdriver? The human operator can check that there is the right image in the memory of the AI. If the image is wrong the operator can change it to right. The robot is not necessarily physical. It can be the algorithm that collects data from the Internet. 

The learning system means the number of databases increases. And the number of connections between them also increases. That means that if there is a lot of databases at the beginning of the independent learning that system can use more connections at the beginning of operations. And that makes the system can use databases versatile if there is a large number of data for use at the beginning of the self-learning. 

Self-learning or autonomous learning process means that the system can increase the number of databases and database connections without human assistance. There is a theory that all databases on the internet can interconnect to one large database entirety. And that thing makes it possible to create the ultimate artificial intelligence that can interconnect all computers and other systems to one entirety.  

The self-learning process can turn more effective. If the databases are pre-sorted by using certain parameters. In that model the database groups are sorted under topics like "visiting shop", cleaning the house" etc. Those databases involve what kind of things the AI must use for completing the mission successfully. 

So when an AI-controlled robot is taking the order to go shopping it can interconnect the databases that are involving things how the robot should walk to the shop. Where are traffic lights, how must react to traffic lights etc.? The thing is that robots can use the same databases for multiple uses. The knowledge of how to react when traffic lights are red. Can use in all missions like walking, driving cars, and other things. 

Image)https://www.quantamagazine.org/computer-scientists-prove-why-bigger-neural-networks-do-better-20220210/

https://thoughtandmachines.blogspot.com/

The IBM Unveils 127 qubit quantum computer.

That quantum computer is a big step to making fully commercial quantum computers. And those quantum computers would open new and bright visions for military and civil purposes. Quantum computers can hack any code that is made by using binary computers. And that thing means that they are causing a need to remake the entire security of the Internet. The history of quantum computers would repeat the history of binary computers. 

At first quantum computers are the systems that are locked at the calculation centers. But then they will turn to every-man machines and perhaps quite soon the regular personnel computers will turn to quantum computers. Things like programming language for quantum computers are bringing more users to them. The new programming language for quantum computers is making them easier to use. 

And user-friendly applications like AI-based code translators are bringing quantum computers to more users. That translator means that well-known computer code like C++, Python, or Jave can turn to quantum computers. And the new quantum programming language will benefit the abilities of the quantum systems. So while we are waiting for the personal quantum computers we can use quantum systems remotely. 

That thing makes it possible. That users can rent the time from the quantum computer centers. And that thing brings more money to the quantum computer projects. More projects and more solutions are bringing the quantum systems more common. But also more powerful and more multi-use. 

Quantum computers are only platforms. The abilities of quantum computers are determined by program code. And those systems might make the revolution in the civil and military systems needed to handle big entireties. The fact is that nobody expects that portable quantum computer have the same capacity as data-center-based fixed systems. 

In the same way. We don't think that a laptop is the same way powerful as a supercomputer.  But when we remember the advantage of supercomputers in the early 1980's systems had 1 mt. memory. We can say that modern laptop are far ahead of those computers. And the same thing will happen with quantum computers. 

The quantum network is at the door. The idea for the nanotube-based quantum network took from the nuclear test "Ivy Mike". 

When the first full-scale thermonuclear weapon detonated at the Marianna archipelago radiation from that bomb was conducted to the sensor by using a vacuum tube. That allowed those particles to reach the sensor before the particles that are traveling in the air. And that made it possible to observe the particles that were released from the hydrogen bomb. 

The quantum wires will protect against outside radiation effects. And then those nanotubes will be covered by electromagnetic fields. The electromagnetic fields are the thing that is covering the qubit against outcoming effects. The qubit could be an electron that rides with the laser rays in those nanotubes. So that thing makes it possible to create a system that connects quantum computers by using qubit-based connections. 

The quantum network can be a series of nanotubes. There might be a laser ray and a powerful electromagnetic field around those tubes. The purpose of those things is to minimize the outcoming errors that are affecting the nanotube. There would be an absolute gas vacuum in that tube. And that makes qubits possible to travel through that tube. '

Because the quantum computer sends photons through the vacuum. They are reaching sensors faster than photons that travel in the medium. The other way is to make the laser ray and the photon would ride in the tube in the fully controlled electromagnetic environment. 

https://www.eejournal.com/article/ibm-unveils-127-qubit-quantum-computer/

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

Image 1:) https://www.eejournal.com/article/ibm-unveils-127-qubit-quantum-computer/

Image 2:) https://en.wikipedia.org/wiki/Ivy_Mike

https://networkedinternet.blogspot.com/

The first programmable quantum computer is made by using neutral atoms.

The ability to use neutral atoms in quantum computers is a remarkable thing. Until now, quantum computers used trapped ions or superconductors in their structure. But the problem with those versions is that they are very sensitive against outside effects. If trapped ion touches the core of the chamber. Or the temperature of a superconductor is rising too high. That thing causes the problems. When we think about the possibility to make the room-temperature operating quantum computer. 

The researchers must "simply" calculate the resistance of the wire is possible to calculate precise points where the qubit reaches a certain state while it loses its energy. By using that information. The computers can calculate the points where qubits are releasing their information. So what if we want to make so-called quantum brains? What would we need for that? There is the possibility that the hybrid system is made by using a nanotechnological structure. The system's core would be made of silicon-carbon material where are chambers. 

Those chambers are connected as an entirety by using nano-tubes. In each chamber is the ion. That anneals by using radio waves or some other electromagnetic radiation.  The brightness of the ion is determining the state of the qubit. The annealing system will measure the brightness of the qubit by using the photovoltaic cells. In that system, the silicon core is also acting as the independent quantum computer. Which controls the quantum annealing system. 

Light is a good data transporter. If the brightness of the laser rays can adjust. That thing can use to transport data in quantum computers. The laser ray can shoot to silicon atoms. And that reaction can turn to electricity that can act as a qubit. The silicon atom-based quantum computers can get their data by using laser rays. And maybe those systems can operate at room temperature. 

The biological quantum computer is one futuristic vision of this system. 

The use of the biological components will decrease the need for energy in the quantum annealing system. 

If we are thinking about the most futuristic way to make the quantum annealing quantum computer. There is the possibility to use the biological components in this system. If we want to put the cells that are creating light in those chambers we could make the light. 

And then the brightness of the light can adjust by using the iris. So the core of that chamber would be equipped with systems that are looking like a camera shutter. And that shutter adjusts the brightness of the light. 

The problem with quantum annealing or other quantum systems is how to make the data travel in lines. The power of the quantum computer is this.  The system can share data with multiple central processing units. The idea is similar to the book that some school classes should read. There are two ways to make this thing. All members of the class are reading the entire book.

Or the teacher can share the book with all members of the team. And then every member of the team is reading small parts of the book. So the book is shared in pieces with team members. That means every person in the team will read only 10 pages from 200 pages of text. 

And after that, the members of the team will tell what happened during their 10 pages. That model is very good if the data mass that the system handles is linear. So the row of the data is like the book. And it will share between central processing units which are operating with a small piece of that data mass. 

When the system shares data there might be some top processor. That processor is like the teacher in the classroom. It preprocesses the data as the teacher looks at the number of pages in the book. And then the teacher asks, which part of the book the members of the team are taking. When the team members are ready. They send the mark that they are done their job. 

There is the possibility. That in the middle of the system is the light source. That shares data to the entirety. In that data flow is the marks where the system cuts it. Then every single part of the system tells others which part it takes to handle. 

It eliminates the work that has no meaning. The error handling requires that somewhere in the system that makes the similar data-handling processes. If those results are the same the solution is right. 

Then the rest of the others would select from the remaining pieces of data. That thing requires complicated structures. Human brains are the biological quantum computer. But to make a copy of human brains engineers must have at least 200 billion data handling units. And controlling those units is a very complicated mission. That requires multi-level quantum computers. And complicated AI. 

https://futurism.com/the-first-reprogrammable-quantum-computer-has-been-created

https://www.sciencealert.com/silicon-quantum-computing-has-reached-over-99-percent-accuracy

https://writingsaboutmysteries.blogspot.com/

The brains will be predicting their perception.

 The brains will be predicting their perception.

Brains are predicting their perception. And that thing makes them energy efficient. When the information comes from the senses, it will not activate all neurons immediately. 

 When the signal comes as an example from the eyes to the optic lobe.  At the occipital, there is also a system or a group of neurons which mission is to recognize threats. If those neurons would not recognize the threat they will send that signal to the optic lobe. There the signal will transmit to the activator neurons that are selecting the tracks. 

Where the optic lobe preprocesses and resends senses signals, they reach the activator neurons. Those neurons will not activate all neurons right away. Their mission is to tell can the neuron group handle the data that is sent. 

When the signal comes to neural structure, only a couple of neurons that mission is to route the signal to the right neuron group will activate. They are waiting for the response. And if some neuron group recognizes the signal they send the positive mark. Then the sending lobe would send the standby signal to leading neurons of the neuron group. 

 And then it starts to activate other neurons. In the brain, all neurons do not handle the same things. So the system works like this. The optic lobe sends the part of the sense to the different groups of neurons. And if some group recognizes the sense they are sending the signal "send the rest" and then the optic lobe will send the rest of the data. 

The reason why this thing is made in the brain is simple. The purpose of that thing is to deny that the brains would use too many neurons for some action. And that guarantees that there are always free neurons that can use for some emergency condition. 

The reason why human brains are using the network-type protocol is simple. That means there are many different types of cells with many different types of abilities is to make sure that while the person is thinking the brain can also observe the things that happen around the person. 

The signal must find the right neuron group, and then it will start to activate the other neurons that are handling that kind of data. When the information reaches the right neuron group it will activate the key or leading neuron which, calls other neurons to handle the things. In the computer world, this ability is called "preprocessing". 

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What if the quantum computer can predict how many layers does it need for some data rows?

If quantum computers could predict how many layers or states it needs would make it more effective. That thing makes it possible that the quantum computer can handle multiple problems at the same time. 

If the quantum computers would have a similar process. That allows it to predict how many layers it needs for some processes that thing makes them more effective. In the network-based systems, there might be the pre-selectors that are preprocessing data. In those cases, there could be an AI-based system. 

That selects the necessary computing systems for the processes. The AI-based system would guarantee that the top processors are not overloaded. That system has the descriptions of the problems that are needed the quantum systems.

And if the problem doesn't fill those descriptions, that problem can send to conventional computers. Or the AI can determine how many states quantum computer uses for the problem. But if the calculation will take too long, that second route will send data to the quantum system. Or it can take more layers for handling the more complicated data than predicted. 

Human brains are an impressive tool. They are using less power than some pocket lamps. So modeling the processes in the brain is making it possible to create more effective and compact quantum computers. That can operate on a longer temperature scale. 

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Brains can handle many things at the same time. And they can operate in all temperatures. But the most impressive thing is that human brains are so compact. 

Even the smallest quantum computers are extremely large in comparison with human brains.  So what if we would try to make the computer which is so compact and has the same abilities we are facing one of the biggest problems in the world.

There is the possibility to make artificial neurons by using the nanomachines that are acting like neurons. But the problem is that there needed 200 billion nanomachines for making that thing. 

When we are trying to model the functions of the human brain we must start from the inside out. While that process, we must know that all elements and structures in the brain and body have a purpose. There is an organ called the "pineal gland" inside the human brain. The signal that activates brains is maybe coming from that organ. 

Then the magnetite bites that are in the connection point will anneal by using electromagnetic radiation. When the temperature of the magnetite rises it will strengthen the magnetic field of the magnetite. 

Then that thing pulls the iron bites to the right point of the axon. In some artificial neuron models, that small sodium or iron bites are replaced by using springs.  The neurotransmitters are acting like chemical qubits. There is an element like iron to aim that chemical qubit over the synaptic cleft. And then that neurotransmitter touches the layers of the axon. 

That thing activates the electric reaction. So this reaction can happen when the iron in the neurotransmitter faces the sodium. There might be a series of iron and some neutral atoms. 

And that thing makes it possible to send the electromagnetic impulses in a certain order to axons. So if we can model that kind of process in the quantum computer. We can make the new type of more powerful computers than ever before. This thing is only the model that must handle if somebody is trying to make artificial brains. 

https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/

https://interestandinnovation.blogspot.com/