AI and social behavior.

Social situations are hard for large language models, LLMs.

When AI tries to mimic humans sometimes it fails. The AI can seem attractive and impressive, but when we ask something there it's not programmed, or trained. The answer is "My algorithms don't allow me to answer". The reason for that answer is that the AI goes outside its box. Answers that the language model gives are programmed in it. If the virtual character is controlled by the human there is no this kind of problem. The AI and humans are the ultimate team. The real-life social situations are a little bit too complicated for the AI. 

The term " to teach, or educate the large language model, LLM" means programming. In that process, programmers teach the AI to give certain types of answers. If the answer is not in a database, the AI asks the programmer or the user how it should answer. The problem with AI and communication with people is that our communication is more complicated than just words. 

Things like sarcasm, the use of voice, and other things like body language are also connected with communication. Because the AI doesn't understand the way, how we say something can turn the meaning of the words opposite. It can give answers that don't fit in the situation. When somebody falls the cup of coffee to the computer's keyboard, and the boss says "nice". That doesn't mean that the boss likes the situation. 

When we face things like social behavior. We face things that we can predict human behavior in large groups. Just like we can predict the movements of large molecular nebulas. But when we try to predict or calculate reactions of the independent people, we face situations in which there are lots of variables in the individual person that we cannot make the AI that can play the human to people, who know that person.  

Our social, and economic background form our way of thinking. The problem with our backgrounds is that there are many things that we don't ever mention. Education happens in three places, homes, schools, and free time activities. Those things don't always communicate with each other. And there can be lots of important things that nobody else than we know. We cannot build models if we don't know all the variables. That is one of the most interesting answers to important things. 

There are so many variables that the AI should handle that it's hard to make a model of the real person. One of the things that we face is emotions. Sometimes we make things that we hate. We can smile in front of some people. And still hate that person. Hidden emotions and feelings can be very common things in professional communities, where outsiders see only things that actors want them to see. 

If somebody is been our classmate, that doesn't automatically mean that we like that person. Or, that doesn't either mean that we like each other. The AI might think that when we were classmates, we automatically liked each other. Same way if we think of dialects like slang people can say that "we hung around in the same gangs". The LLM can think that the gang is always some kind of Hell's Angels. And that makes the discussions interesting. 

The AI searches the word "gang" from the net. Then it sees articles about motorcycle gangs. After that, the AI thinks that the person "who hung in a gang" was a member of those MCs. Same way, because everybody smiles in class photos, the AI thinks that those persons automatically like each other. 

The memories that classmates bring in our memories can be far different than somebody might want. One thing that makes AI fail when it tries to mimic a real person is that it plays a person, who the opponent hates. When we start discussions with old classmates we might say "Nice to meet you". And inside our mind, we can hope that we might not want to see that person ever again. 

AI can make the virtual characters that the real person controls. In that case, the AI operates along with humans. The system can show if the opponent's voice changes. It can see if the opponent looks away from the camera. And it sees things like sweet and dirty skirts. In those cases, the AI's purpose is to support the real person's work. 

The AI can mark points. That is think as dirt or some other remarkable thing. But the responsibility is in the human user. And if a recruiter sees things like disty skirts in an interview those things can help that person to make the selection. We must realize that virtual characters can operate as tools the secret agents and intelligence services use to get confidential information. In the same way, police and other law enforcement can use virtual characters to cheat criminals to tell about their organization. 

The AI can someday think like humans.

What happens when the AI starts to think like a human? That is an interesting question because nobody knows what the person next to me or you really think. They know what that person tells them about thinking but then we must realize, that nobody knows how, or what the next person thinks. We can do many complicated things. And then we must realize, that we don't even know what we are doing.

The AI-controlled robot can select a screwdriver and tighten the screw. The robot can answer questions like what tool it uses and when and where it must use that tool. The computer doesn't need complicated algorithms for that thing. It requires the trigger. That is the screwdriver image. That image activates the database. There are the needed enlisted replies. Those texts might seem like spoken languages. 

Those things are fixed models that might make the AI seem very humankind. 

And if the AI doesn't have the image and database, it can ask the user to make a new database for the new tool. 

There the user can write the description for the AI. 

In that model. The AI can ask questions from the list there the user gives answers. And that is one way to teach the AI. 

We can cut boards without knowing where they are going. We can drive a car. And we must know anything about those vehicles. 

We don't need to know how algorithms work when we use the internet. 

In the same way, we might see that algorithm or the large language model, LLM seems to think. While it sorts information. That it collects from the net. The AI doesn't do anything without commands. There is the user who gives the AI orders. Or there is a trigger in the AI that launches certain operations. The trigger can be a certain pressure level in the tube. When AI notices the pressure it orders to decrease the temperature in boilers. 

Thinking is a complicated process. We say process. Where the nervous system connects information from the senses with memory as "thinking". If we want to mimic that process in the computers we must make billions of databases and connections. The simple thing to make the "semi-thinking" AI is to make it ask questions. The AI might have the list of questions that it should as the coder or developer. 

Some expert's opinion is this. The future is in a small language model. The small language model, SLM is the segment from the large language model. That segment is the group of abilities that the LLM has. The segment can include things like programming models. 

The advanced AI can ask questions about things. That it should be done if the system operator orders the AI to draw humans.

 LLM can ask questions about the clothes that those people wear. 

It can ask about the colors and styles of their clothes. The AI can have a list of the things. That it should notice when it operates with humans. 

Same way the use of AI is always a thing the coder operates in interaction with the AI. The AI should ask questions about things, that the operator wants it to do. The coder must give descriptions about the thing, that the AI must do. Without interactions and descriptions, the AI is in trouble. If we think that the AI replaces coders and does a good job if it takes the command "make me the CAD program". In that case, we overestimate the AI. And it's capacity. 

When we leave programming to the hands of the AI we forget one thing. The AI might seem very creative. But it always follows certain routes for solutions. Those formulas make those AI-produced programs very vulnerable. If the AI always uses certain paths and always gives the same names for the folders. That means that the attackers find those paths and folders very fast if they want. 

AI can seem like a human. It can answer very complicated questions. Or do we just think about that thing like that? 

Are all the questions that we ask the AI difficult? 

Another thing that can motivate us to use AI is simple. That we are lazy. We have no time to read articles. Or we just need that time for some other things. So we might ask for some terms that we would find any way from Wikipedia from the AI. 

https://www.quantamagazine.org/will-ai-ever-understand-language-like-humans-20250501/

"Pigeon-problem" in memory control.

Computers have two types of memory: read-only ROM and random-access memory, RAM. The last one is known, as work memory, where the system loads programs. Every memory unit has a certain address. Those memory units are like boxes. 

Sometimes, the computer's operations are compared with the pigeon. The pigeon is the data package and the packet is the router. The pigeon problem is how that pigeon finds the right box where it drops that data. 

That flies around the slope where there are lots of boxes. Each of those holes, caves, or boxes, whatever you want to call them has a certain address. The pigeon doesn't know which of the boxes is empty that it can leave its package which is the operation that the computer should run. 

Finding the free memory block or memory address happens randomly. That's why that thing is called random access memory, RAM. We can say that when a pigeon drops the packet into a certain memory unit, it pushes a button and tells that the operation is in that memory address. The pigeon's problem is that there are always reserved boxes. 

The operating system reserves a certain number of those boxes for its own use. The operating system can put the list of addresses that it reserves. And then the computer program knows that those addresses are reserved. That means the fixed solution that is suitable in the cases, where the system runs only one program in time. 

This kind of solution is slow and even dangerous if the computer or its program requires polymorphic memory. Another name for polymorphic memory is the morphing neural network. In the flexible memory handling method. There the system drives multiple operations at the same time. Those algorithms or programs require a certain number of memory addresses. When those groups of networked memory addresses made their duty, they asked for a new mission. If there is no other new mission those memory blocks or boxes can offer assistance to other network groups. 

The morphing memory network base is in those memory addresses. The system interconnects those memory addresses into new subentities. In that kind of morphing system there the memory address can have different roles causing the problem to pigeon. The reserved memory addresses are not fixed. And that means. The system should always update those lists. 

But the answer to the pigeon's problem can be the interactive memory. In that case, the box itself raises a flag or the mark that it's free. When a pigeon comes to give a message or new task it sees immediately which of the boxes is free. So it must not knock on the door and disturb another operation. The problem with fixed memory addresses is that they leave lots of free space in the system when they finish their job. The system can use that free space for another mission that it runs. So the system requires a more dynamic solution than just fixed memory address groups. 

The problem with the large language model, LLM-type solutions is that there are billions of data packages. They try to find the place where they put their data. There is a possibility that two pigeons try to put data in the same box. So there are some details that the pigeons can choose right and empty box. The pigeon might have a skirt that has a certain color like yellow. The yellow pigeon travels to the yellow box. 

The pigeon can also have a flag that it shows to other pigeons. The flag, or screen can show information about which number of the box the pigeon selects. And that tells others that the memory box is full. 

The pigeon can fly and systematically search if there is an empty point or base. That kind of thing means that sooner or later the pigeon finds the free address. There is the possibility of increasing memory. That increases the number of memory addresses. And that increases the possibility that the pigeon finds a free memory address. 

The pigeon or the computer program can always ask the free space from the operating system. That means the program comes to the terminal and then it waits for the operating system to tell where are the free boxes. The problem is that this way of operating is slow. The system can also use a method. That is sometimes called "hovering algorithms". In that model, the algorithm or pigeon that leaves the box tells other algorithms or pigeons which box is free. 

The thing that can help this mission is that pigeons have a certain color skirt. The pigeon with the yellow skirt goes to yellow boxes that are sorted in one entirety. When it leaves the package or operand in the box. It leaves a flag in that place. When another yellow-skirt pigeon comes to that box it can simply pull the flag off and then drop it to the controller that takes the operand. 

But in modern computing, there can be billions of units in those boxes. The system can drive even billions of algorithms at the same time. The new types of systems require that the computer algorithms can tell straight to another algorithm. That is place is free without traveling through the operating system. This makes the system more flexible. 

So the pigeon has a very big problem. If it disturbs the operation that runs in a certain address, that thing can cause a fatal disturbance in the operations. The dynamic solution requires that the memory controller can resort to memory in a new way if there is free space. 

The distributed electrochemical random-access memory, ECRAM is a new tool to handle AI.

 

"Researchers have, for the first time, decoded how Electrochemical Random-Access Memory (ECRAM) works, using a special technique to observe internal electron behavior even at extreme temperatures. This hidden mechanism, where oxygen vacancies act like shortcuts for electrons, could unlock faster AI systems and longer-lasting smartphones, laptops, and tablets." (ScitechDaily, Ghost Highways in Memory Chips – The Secret Electron Shortcut to Lightning-Fast AI)

New things like AI require new equipment. The AI is a combination of complex algorithms, and that system requires a new, faster way to handle memory and its combination with the software. In traditional computing, the microchip calls the program from RAM (Random Access Memory) for driving. When the computer must always call millions or even billions of algorithms. 

That thing makes traditional computing slow. "In-memory" computing where data is handled in multiple memory blocks at the same time can be the answer to that problem. In that model, every single memory chip has its own processor. 

Whenever the computer drives a program the code travels through the microchip. That kind of way where all data travels through the microprocessors is quite slow. There are tested systems where each RAM component has its own microchip that gives the system a new multicore way to handle data. The data can be handled in a memory chip which decreases the need to transport it a long way. 

That decreases the temperature in the wires. However, the problem is that all microchips create temperature. And that system just transports the temperature problem to another location. 

The new systems called "in-memory computing" where calculations happen straight in memory are anyway tools that allow to make faster and more effective computers. If we think that the system uses a distributed way of data-handling processes, that system can mimic the quantum computer. 

The system their calculations happen non-centralized in multiple independently operating memory units that can operate as a virtual quantum computer. The system can share calculations or operations with those data handling units. Each of those data-handling units can act like a qubit state. So "in-memory computing" can allow the system that can operate as a virtual quantum computer to make it faster than a regular, centralized computer. 

"A schematic representation of in-memory computing using electrochemical memory devices (ECRAMs) arranged in a cross-point array structure, mimicking the way synapses in the brain process information. When voltage is applied to the device, ions move within the channel, enabling simultaneous computation and data storage. This study reveals how ions and electrons behave under applied voltage, uncovering the device’s internal operational dynamics. Credit: (Pohang University of Science and Technology,POSTECH) ((ScitechDaily, Ghost Highways in Memory Chips – The Secret Electron Shortcut to Lightning-Fast AI)

"In-memory computing" allows the system to handle multiple operations at the same time. The system can share the memory blocks into segments. And when the system accomplishes its job.

It can give output to the screen. Then the block waits for new orders. Or it can tell other blocks that it's free, and if those other blocks require help it can offer its resources to them. 

 But in the same way as human brains the "in-memory computer" can continue some other operations backward. "In-memory computers" acts like human brains. 

There is always space for a new mission. If the system must run multiple tasks at the same time, it can reserve a certain number of memory units for each job. When some job is done the computer can share those free resources with jobs that need them. 

"In-memory computer"mimics human brains. Because the system has multiple data-handling units that can share multiple problems the system doesn't need to stop for a new mission. The single unit must stop but the entirety can always take the mission. The reason why our brains use that model is simple. All neurons work as groups. There are always free neurons that can take a new job. And when the other neurons finish their mission. Those neurons can call others to assist them. If the problem is difficult brains will connect more and more neurons to operate with it. 

But that system requires new memory technology called Electrochemical Random-Access Memory, ECRAM. The system mimics human memory. This means there are two states in that memory. The electric state and chemical state. Basically, the idea of the ECRAM chemical memory is simple. When the molecule is in a certain position, it has a state of 0. And the other position is 1. The problem is how to make that thing in practical solution. Those positions can be physical or they can be the ion states. Or anion can be 1 and an ion can be 0. There are multiple ways to make that thing possible. ECRAM technology should produce less heat than a regular electric data-handling process. 

1) "ECRAM (Electrochemical Random-Access Memory): An electrochemical memory device whose channel conductivity varies according to the concentration of ions within the channel. This behavior allows for the expression of analog memory states. The device features a three-terminal structure consisting of a source, drain, and gate. By applying voltage to the gate, ion movement is controlled, and the channel conductivity is read through the source and drain."(ScitechDaily, Ghost Highways in Memory Chips – The Secret Electron Shortcut to Lightning-Fast AI)

2) "Parallel Dipole Line Hall System, PDL Hall System: A Hall measurement system composed of two cylindrical dipole magnets. When one magnet is rotated, the other rotates automatically, enabling the generation of a strong, superimposed magnetic field. This configuration allows for enhanced sensitivity in observing internal electron behaviors." (ScitechDaily, Ghost Highways in Memory Chips – The Secret Electron Shortcut to Lightning-Fast AI)

https://pubs.aip.org/aip/apl/article/106/6/062407/29245/A-parallel-dipole-line-system

https://www.quantamagazine.org/what-is-distributed-computing-20241125/

https://scitechdaily.com/ghost-highways-in-memory-chips-the-secret-electron-shortcut-to-lightning-fast-ai/

Artificial viruses bring new winds in medical treatment.

The new biological sciences and genetic applications are impressive. Things like 3D collagen can help to print living tissues and maybe organs. The system can make 3D bioprinting using normal bioprinters.

And the problem is where to get those cells. If those cell's shell antigens are wrong, the immune system destroys those cells immediately. 

When researchers want to make things like bioprinted bones they need lots of cells. And one answer to that problem is cell reprogramming. In that process, the system changes the DNA from the cell's nucleus. The problem is how to make enough cells in a short time. 

Because if people need that kind of thing, that happens in non-predicted accidents. The system must make a number much cells in a short period. The answer can be the artificial viruses that can make that reprogramming process. 

If researchers want to print a 3D organ like the liver or even the brain the system can use large-scale cell reprogramming. The idea is that the system puts those cells in the 3D mold. 

Then the artificial viruses can reprogram those cells by removing their DNA. Changing those DNAs into new ones the virus changes the type of the cell. 

That should transform those cells into the wanted tissue and organ. This kind of therapy makes it possible to turn even bacteria into the tissue that replaces the old one. 

The new knowledge of the cells and bacteria. And their DNA makes it possible to manipulate cells. And their operations with new and effective accuracy. 

Another thing that researchers noticed. The cell's role in the human body depends on the nutrients and metabolism. This knowledge can make it possible to create artificial tissues using cell cultures and boost things like immune systems. 

The growing knowledge of the DNA sequences. And their effect on cells. 

Make it possible to create cells. That transforms themselves and their roles. The same cell can destroy bacteria. And then it can act as the building block of the blood vessels. 

The artificial immune cells can transport bacteriophages to the desired point in the human body. 

Those viruses can search and destroy non-wanted cells. 

The AI is the tool. That can search the DNA sequences in a very short time. The AI can see people's DNA and find out why somebody will not get the flu. 

The artificial cells and bacteriophages can replace many antibiotics. But before we can say "goodbye" to antibiotics. We must have complete information about the mechanisms. That the bacteriophages use, when they target bacteria. That mechanism helps to find or create artificial viruses that can target things like cancer cells. 

The human gut has its bacteriophages the mission is almost certainly to make sure. That the gut bacteria are in balance. 

By following those phage viruses researchers can get more information about how the phage selects the bacteria. And maybe in the future, those bacteriophages can also transfer DNA bites that cause bacteria to die. More or less artificial viruses can be used to destroy non-wanted cells. 

And researchers think about the possibility. That they can remove those phages from the gut. During antibiotic treatment. Then they will return those viruses to the gut by using genetically engineered cells. Those cells create phages.

Another thing that those phages can do. It turns the immune defense stronger by helping immune cells destroy bacteria. That they cannot otherwise reach. The intestine is a hard place for the immune cells. And maybe those phage viruses handle immune cell missions in the intestine.   But otherwise, they can make the gut bacteria more vulnerable to antibiotics. 

Artificial viruses are excellent tools for genetic therapy. Artificial viruses can also boost the killer cells's ability to find the non-wanted cells. The idea is that. 

The virus can carry the DNA, or mRNA bite. That gives immune resistance against some antigens. That thing can boost an ability to fight against cancer. The mRNA programs the immune cells to select and destroy things like zombie cells. 

The hope is in so-called NK (Natural Killer)-cells that memory is enhanced using the mRNA or some kind of microchips. This requires precise operating systems that can select non-wanted cells more accurately than before. But that requires very highly accurate genetic engineering. 

https://www.quantamagazine.org/how-metabolism-can-shape-cells-destinies-20250321/

 https://scitechdaily.com/can-we-program-life-rewriting-the-rulebook-on-how-cells-self-organize/

https://scitechdaily.com/printing-the-future-of-life-how-3d-collagen-scaffolds-grow-real-tissues/

https://scitechdaily.com/scientists-bioprint-living-tissues-that-could-revolutionize-diabetes-treatment/

 https://scitechdaily.com/scientists-flip-a-gut-virus-kill-switch-and-expose-a-hidden-threat-in-antibiotic-treatment/

 https://scitechdaily.com/new-hope-memory-enhanced-nk-cells-could-revolutionize-cancer-treatment/

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

What if we put computers to think mathematically?

When we think about programming and the computer's memories every single memory unit in the computer hardware has a certain address. The artificial intelligence connects and disconnects those memory points into the new orders. And that can make a computing process that mimics thinking. Every single memory address is like a piece of the puzzle. If every single memory unit has a certain number that makes it possible to point certain points from the computer memory. 

When we think about things like thinking we could easily connect those memory units with orders that the large language model, LLM gets by using the numeric values of the memory units and then calculate them with the ASCII marks. In the ASCII system, every single mark on the keyboard has a numeric value. 

For example, the letter A has a numeric value 61 in decimal and 41 in hex. A little a (a) has values 97 in decimal and 61 in hex. That's why it's not the same as the letter big or small in passwords. The numeric system is also important. The hexadecimal ("Base-16" system where the 10 comes after 16) and regular decimals are different. 

In that system 10 is marked in the numeric line like this. 0,1,2,3,4,5,6,7,8,9,A,B,C,D,E,F,10. In binary system 10 comes after 9 like this. 0,1,2,3,4,5,6,7,8,9,10.

Same way every single color has a numeric form in the computer memory. The system is known as RGB.  The system can use CCD cameras to make observations. 

Another thing is to use the values that fit to computer or programmer better. The color red can have a numeric value "200" and then the depth of that color can have 99 states. The system can turn every color into its own numeric value. 

The deepest red can be the 299. The data that CCD camera pixels give can be numeric. The system can see what numeric value every pixel gives and then it can make the model about things that it sees. So all data that travels into the system can turn into numeric. 

Token ring. If we think of this model as the computing cycle of the AI.  The system connects data into that data cycle. Every point in the cycle. There is the computer's image.

This can be the new way to handle large language models, LLMs are not to turn their mathematical models for words. The system can translate data that users input there into the mathematical model. Then the LLM starts to operate and process data in the mathematical form. That kind of thing can be lighter for computers than the words that we use. Mathematics is easier for computers, and when we think about the ability to turn words into mathematical form, we must remember that ASCII codes are basically numbers. Those numbers can sum, division, and multiplicate easier than words. 

That means the LLM can turn every single word that it has into numbers. Then that system can make calculations using the numbers. The ability to handle data in numeric form makes those systems more effective. The system can use the "token ring" type data handling, or computing model. The token ring model is known from data networks. However, the same model can introduce how the system surrounds data in it. Every time, when the system makes the data cycle it connects information into that data cycle. 

The system makes a certain number of calculations in every round. In those calculations, the system connects data from the sensors and memories in the data flow. The system doesn't need to show that information to the users before it drives it through the cycle as many times as ordered.

https://www.geeksforgeeks.org/ascii-table/

https://www.quantamagazine.org/to-make-language-models-work-better-researchers-sidestep-language-20250414/

https://www.rapidtables.com/web/color/RGB_Color.html

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

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

The quantum network can be closer to reality than we think.

"The new operating system is the first in the world that allows quantum computers with different kinds of qubits to function together in a single network. (Image credit: hh5800 via Getty Images)" (LiveScience, World's first operating system for quantum computers unveiled — it can be used to manage a future quantum internet)

The quantum network can offer a new. And a very secure way to communicate over distances. The quantum Internet will be a very trusted way to transmit data because data is connected with particles. The particle that travels in the quantum internet plays the same role as neurotransmitters play in the nervous system. The qubit can be a photon, electron, ion, etc. 

Basically, the quantum network's principles are known. Details cause problems in the system. When the main problems are solved. The next step is to turn to solving problems with more and more accurate details. 

And the final steps before the full-scale operating quantum networks are very short. When we think about this kind of network from the point of view of the R&D work the first steps are long, but then the accuracy increases and that makes the steps in advance turn shorter. So the last things before the goal are the longest. 

But when the quantum network comes, that thing makes the ultimate state of security for communication. 

A quantum network can be like a hollow tube. 

That tube acts like a particle accelerator. And a qubit travels in that system. The system mimics the human nervous system. The problem is how to eliminate the Hall effect because vertical fields can damage information in a qubit when the system shoots it through the line. Another problem is unexpected effects like gamma-radiation that can destroy the qubit. 

The qubit travels in the quantum channel mimics the axon. The computer centers mimic neurons. The system routes the qubits into the right routes. 

And the computers or the nexus centers can also make copies of those qubits. And that subsystem sends them into different routes. The information about the right routes can travel in the shell of that system. 

The quantum network might be closer than we think. The quantum computer is a good tool for controlling and administrating quantum networks. In those systems, every state of the quantum system can administrate or control certain quantum channels. The system can create a copy of the arrived qubit and send it back. 

That allows the system to check. If there are some errors. The system requires at least a duplicate quantum line to make the data check. If both lines have identical solutions the answer is true. Increasing the number of data lines makes the system more trusted. 

The quantum network can mimic the axon. Electric signals, or control signals operate the quantum route. Can travel in the quantum channel's shell. 

The qubit can travel in the hollow quantum channel. The qubit has the same role as neurotransmitters in the human nervous system. The data that the qubit carries is connected to the particle. And that makes the quantum computer and quantum network safe. There are two ways to make the quantum network. The first one is to use the superpositioned and entangled particles. 

But making that spooky action in distance possible at long distances is very difficult. Another way is to pack information into the particles like photons, electrons, or ions and shoot them through the quantum channel. The quantum channel itself is like a particle accelerator that accelerates those qubits. The problem is how to eliminate the Hall effect or Hall field from that channel. 

Those vertical energy fields can destroy information from the qubit that travels through them. And other problems are things like fast energy bursts from the universe. Those things can destroy the qubit.

https://www.livescience.com/technology/computing/quantum-internet-breakthrough-after-quantum-data-transmitted-through-standard-fiber-optic-cable-for-1st-time

 https://www.livescience.com/technology/computing/worlds-first-operating-system-for-quantum-computers-unveiled-it-can-be-used-to-manage-a-future-quantum-internet

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

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