In the world of computers, we should trust no one.

If hackers break into the system and steal data that data can be sold in many places. So even if officials can track hackers and put them in jail, that information is gone forever. Or it turns meaningless. If some secretive information about things like stealth materials is delivered to the Internet that material can turn useless. 

The thing that we should worry about is a cyber war that the Russian, Chinese, and North Korean governments are making against Western allies that those countries see as their enemies. The hacking- or cyber espionage, controlled by governments is a real threat. Because governments protect those hackers that means they never go to jail. And then. Another thing is that this kind of hacking can continue even for years, and there is a lot of data that can disappear to the net. 

The problem and threat is that those countries are on a list that denies them to use of things like social media freely. If some North Korean citizen opens a social media account in Europe or the USA, that account is under surveillance immediately. So the Russians and North Koreans can also make contacts with local criminals that they deliver SIM cards and other important things to those countries. That allows them to open social media accounts and other important things that defenders cannot connect with those countries. 

Even if we believe that nothing is interesting in our life we must make sure that our data security is high enough. We might have Facebook friends whose family members or themselves are working in confidential positions. Those people are primary targets for intelligence operators. The banking clerks are also good targets because they might see who pays people's salaries. Sometimes that data reveals that the person works in high-class military-intensive areas. And even access to some Lockheed-Martin buildings makes people interested. 

The access to buildings allows spies to put spy cameras into those houses. And that makes it possible to see highly secured data. So we must also worry about physical security. The firewall will not protect anybody if hackers can walk into the house. 

And then, see passwords from some notebooks that people left on the table. Organized crime members can also search for people who can offer them things like weapon licenses or access to things like narcotic medicines. 

Reseachers make many new things all the time. Most of the systems are not secured. Because the systems themselves are secretive. And they are protected for one reason.  They involve classified or confidential information. And when we think about the modern world hackers can use all information that we share. Even our own identities and things like SIM cards are useful tools in the hands of cyber or data soldiers. 

Those people can open social media accounts in the name of Western people. And that makes it possible to deliver disinformation to Western societies. The trusted application allows data spies to ask people about their relationships with the government authorities. And also people who have keys to things like water supply systems or network-sharing rooms are interesting targets. Those places play a vital role in the society. And making damage to those systems the attacker can try to make people's lives uncomfortable. 

This is why all organizations should do vulnerability testing. All organizations must have zero-trust principles. There should not be people or parts of the systems that are trusted. All information interests some criminal actors or intelligence service. Vulnerability testing means that the system is tested in both levels. People and computers are at different levels. But people operate every system. So, even if there are some kind of written data security orders, organizations must follow that people read and follow those instructions and orders. 

The problem with old-fashioned data protection tools is that they are passive. Those systems didn't make a report if somebody made the login attempts in the middle of the night. 

All instructions are useless if people don't follow them. The AI makes it easier to write things like malicious code. But deny AI doesn't remove malware from the net. The malware like spying tools are not connected to the AI. And the worst thing that people can do is leave the workstations open and automatically log in to those stations. In those cases even the best network sniffers are useless. Maybe hackers try to slip into the offices playing cleaner. 

Then hackers might look for a workstation that is left open and settings allow automatic login. That kind of thing is one of the worst things that can happen. This is why the company should shut down those computers automatically and log them off. There should also be systems. That reports if somebody opens and tries to log into the network outside working time. 

That means that if a person does not have permission for overtime work the system should log that person out when working time is full. Company leaders should know who uses their computers and when they use those computers. The work computers are for work. And own computers are for private use. 

It's possible. That hackers can bypass the generative AI's security.

When man meets AI, the man is more intelligent. That means that the creative AI is not creative at all. The man who uses the AI is creative. The thing that we should understand is this: what we see as an example of "fuzzy logic" is a large number of precise reactions or precise logical points. 

That means that there are multiple words. Or otherways saying:  multiple triggers. That is connected with a certain operation. This allows users to use dialect words or literally while using the AI.  

The dialect words and literary words are both connected to certain operatios. That gives the user freedom in the form of language. What the person uses to command the system. 

In this model, users can use both, dialects and literary words. That makes the AI more flexible than if the user must use precise literal words. 

We can say many ways something that launches the action. The words, connected with certain operations are called logical points. Developers can connect each action with multiple logic points. Which makes the system seem like it uses fuzzy logic. 

That means that when we give a code or order encoded into hex-decimal or ASCII mode is not precise same as the orders that we write in regular text mode. This is the thing that might bypass the protection of generative AI. If somebody wants to use the generative AI as the tool, that makes it possible to write malicious code very effectively the person must just bypass the safety mechanisms of those generative AI. One version can be that the commands are given in the form of Hex or ASCII mode. 

Another thing that can cause the ability to bypass the security of artificial intelligence is to give orders as small pieces. The hacker- or otherwise the malware developer can use multiple AIs to create the code. And then that person can connect the results. The problem is this. The code writer must have deep knowledge of coding. And the other thing is this. Hackers can use AI to develop the base code as legal developers. 

They can freely use AI to develop legal software like chat programs and firewall software. The spyware that hackers use to steal information is the chat program or firewall that is modified to send data without the user's permission. So hackers can make legal applications, and then change the code so that it runs backward and steals information. One version is to create a tool that allows as an example teachers to follow what students do with computers. 

Normally those programs tell the user. That they are operating. Their use is limited to classrooms while teachers teach things like programming.  There can be some red frames and text. That tells the observation program is in use. Or the program requires user acceptance. Hackers can remove those manifests and then they can observe the targeted computers. 

https://cybersecuritynews.com/encoding-technique-jailbreaks-chatgpt-4o/

Networks make AI real.

Linux enthusiast Linus Thorwalds said that 80% of AI is marketing, and 20% is true. That is the normal relation in computer applications. The news about "doomsday AI": or, AI that destroys the world, makes AI look alike more fascinating than it is. But the fact is that the AI and its ability to generate the code should cause re-estimation in data security. 

The neural network can attack systems with powerful methods. In neural networks. The AI can change the attacking computers. And their IP's all the time. The AI-based systems also make it possible to use dynamic IPs that make it harder to track those computers. The large-scale neural networks can also operate as the artificial general intelligence, AGI. 

The news about the AI's ability to make code and even essays brings new users to those systems. Also, things like weapons and other kinds of stuff make the AI news interesting. Networks are things that make the AI real. They allow the AI to search data from the net.  Without them, the developers must program everything to the computer's memories. 

Or it makes it possible to generate a language model that can search data from the network. The data that the AI can use limits its abilities. Another thing that determines AI and its skills is the ability to operate in the physical world. The robot is the tool that turns the real actor who can clean the house and make food. 

Above:) Neural network. 

The fact is this. If we talk about things like AI doctors or AIs that make doctoral theses, we are far away from those things. There have always been people who cheat in universities. And the AI is only one tool for that thing. AI is the tool that makes many things more effective. But it requires that the user knows about the topics. The AI is an ultimate tool for people, who know what they do. 

The AI is a tool that can make many things easier. It can handle well-sorted and well-presented information like numeric calculations with very high accuracy. In a neural network, the AI can swap the calculation to another computer if that computer is required for some other mission. When a user takes the workstation in use, the system can swap the ghost or background process to another workstation. 

In that kind of network-based system, the system is based on the network of workstations. In the network-based solution, the single workstation can try to solve problems in a certain time. If the problem is not solved the system calls other computers to help. In this case, the system can scale the job theoretically unlimitedly. The only thing that limits the number of computers that the AI can use is the number of workstations and supercomputers in that network. 

But the AI cannot make things spontaneously. It always requires humans to begin the operation. 

Social media makes the AI more well-known. But AI is also a tool that requires social media. The AI companies use data from internet services like X, and Facebook to develop and train the AI. When R&D people make some new solutions for AI they require words that activate the process. In cases of the AI the word that people say or write causes a process where the AI searches for match. The word that the AI gets is the trigger that activates the process. 

The AI is still a regular computer program. Or, actually, it's a group of computer programs and applications. The marketing people call a language model that can connect itself to datasets as AI. Each dataset gives some "skill" to AI. The artificial general intelligence AGI is a large number of datasets that it can use to control things like robot taxis and microwave ovens. Otherwise, we can see a large group of dataset modules as one entirety. 

Mesh protocol. 

That means when the LLM calls the robot taxi, it just makes a "phone call" to the LLM that operates the taxi. Then the customer's LLM fills the form. And then it delivers responsibility to the AI that controls the taxi. The appearance of the AGI is formed when the cab transforms the voice it uses with the customer similar to the customer's LLM. There are two independently-operating AI-based systems run on two servers. The network-based structure that swaps missions between systems makes it possible to create a system that seems to be one entirety. 

In this case, the AGI is the network of independent operating systems that humans can control through the LLM. The network-based systems that use open architecture, or partially connected mesh protocols allow to connection unlimited number of modules to that entirety. And that brings AGI closer than we think. 

These kinds of network-based systems can have billions of full mesh networks and in the middle of those systems is the LLM. So each ball in the neural network involves LLM and the mesh network. Those systems can also have independent operating supercomputers. 

The thing is that the network of AI-based systems that connect robots with AI and LLM are impressive tools. The fact is that those robots are good marketing tools. People want to see robots in action. And that brings a new audience to the AI. 

Are we ready for AGI, and robot combination?

Artificial general intelligence AGI is one of the things that cause discussions. Somebody says that it releases low price people. But somebody says that the AGI causes very big problems. The fact is that all changes cause problems. If we want to use some new things, we must learn how to use them right. 

The AGI is a tool that can make many things. Or it should be that tool. 

We can say that AGI is a group of action modules around the LLM. And that makes it one of the most powerful things. 

The thing is that. Nobody knows the limits of modern LLM-based AIs. 

And that causes another thing, that we should think about.

Do we already have the AGI? We can collect almost every kind of action package around the LLMs. And the limit is that there should be a tool that can interact with the LLM. In the AGI model, the LLM takes the order. And then it transfers the mission to the module responsible for that action. 

If we want AI to cook food for us that tool requires an actor that operates in the physical world. The medium between computers and humans is robots. The man-shaped robot is one of the most interesting tools that we can imagine. That tool can affect society stronger than ever before. The LLM-based AI is not even five years old. And it started a revolution, especially in the ICT industry. So, what kind of revolution can the AI that controls robots in independent actions like cleaning? 

That means the requirement for calling things like robot vehicles to the door requires that the LLM has the socket that allows it to call robot vehicles. Or it must have a robot that takes the car. The human-looking robots are tools that can download any type of action package. And that makes those things act as chefs, cleaners, drivers, or anything that the user wants. 

But are we ready for those tools? The AGI is impressive. It learns faster than any human. The thing is that. This kind of system can revolutionize the world. The same robot that can act as a chef or cut our grass and drive our children home can operate as a merciless weapons. Those systems are dangerous in the wrong hands. The problem is that even if there are some kind of laws and other things that limit the AI use as a weapon controller those systems require very high-level data security. Things like organized crime can also use robots for their purposes. 

The thing that makes robots a powerful tool for the underworld is that those people can erase their memories. If we think the man-shaped robots that look some certain people, we face one problem. The problem is that some people can make full-size copies of bank managers. Those robots can walk in the banks. And then make money transactions for hackers. 

When we think about the first-person view robot, that looks like a human, we are facing one of the most interesting things. The hacker can operate that kind of robot using VR glasses and other input tools like data gloves. Then hackers can put that robot play things like cleaner. And when that robot slips in the office hackers can use it as a remote tool to control the computer. That is one of the illegal activities that this kind of robot can make. And those robots can also operate on the battlefield and cover operations. 

Machine learning and AI are tools for nanotechnology.

Machine learning and AI are the best in business when they create large-scale and very accurate models of the universe and other large structures. Nanotechnology consists large mass of physical and chemical variables. Nanotechnology consists of many bonds and chemical compounds that can exist or form only in a certain chemical environment and physical environment. 

The AI can use many types of sources to find out conditions where some chemical compound can form. It can search the stellar and exoplanet databases. 

If the mass spectrometers see some compounds. Near some stars, the system can model things like energy levels that the compounds get from the star. 

But then we can think about the nanomaterials and their "cousins" the quantum materials. Those things can make many things possible, that were been like Sci-Fi before this. The material research units can use mesh networks to combine results from many measurement and production units. AI and networks are tools that can combine many production units into one entirety. 

The system can drive large data mass very fast and effectively. By beginning the drive in multiple points which means the process is more effective than ever. 

"In Caltech’s new fingerprint technique, a single molecule adsorbs onto the phononic crystal resonator device. Then scientists measure the frequency shifts of four different vibrational modes of the device, allowing them to create a four-dimensional fingerprint vector—a unique identifier that can then be used to determine the mass of the molecule. Credit: Nunn/Caltech" (ScitechDaily, Machine Learning Meets Nanotech: Caltech’s Breakthrough in Mass Spectrometry)

 A laboratory that uses network-based AI is the most effective research tool in the 

The system might have different chemical and physical conditions in every reaction chamber. That allows the system to follow the reactions. When some reaction chamber reaches the wanted results, the system can scale those conditions to all other chambers. 

The system can handle multiple measurement points at the same time. AI-based material research units require highly advanced observation and manipulation systems. 

Those systems are things like,  attosecond lasers,  scanning photon microscopes, and mass spectrometers. Those systems can search for the formation of chemical bonds. 

Nanomaterials are new tools for stealth- and computer technology. Those stealth materials are dummy or passive materials. Intelligent materials. That involves microchips and locally controlled abilities. It makes it possible to create more effective things than those passive materials. The microchip network makes it possible to control those materials with very high accuracy. 

When we talk about a thing called cyber metals. That technology makes it possible to create the type of machines. That we see in Terminator movies we talk about one type of drone swarm. 

In that kind of drone swarm the robots touch each other with things like nano-wires. Those wires touch to potholes of other nanorobot shells. Those entireties require new types of nanotechnical processors that can operate as networks. 

https://scitechdaily.com/machine-learning-meets-nanotech-caltechs-breakthrough-in-mass-spectrometry/

The AGI: what is it?

If we think that the Artificial General Intelligence is the LLM that commands robots, we can ask one question. How many things can we order those robots to make? Or, how many things do we make in everyday life? If we forget things like military robots and some special-purpose androids. We can ask how many skills robots must have. That they can clean our houses and go to shop for us. 

And how many things do we do in our everyday life? If we forget things like lunches or some other things, can we make a robot that takes the grocery bag from the shop jumps in an electric car, and drives home automatically? The Large Language Model, LLM, and robot can operate as chefs if we want. 

If a robot has a module that allows it to make food, and check things like how ripe the meat is, it can make automatically food that the client wants. The client must only order some food. Then the system searches the merchandise list for the food. And after that, the robot will take those things from the shop. 

The AGI means general intelligence. That can use everything. But then we must realize that there is no single person on Earth, who knows everything. 

For making things in physical words the AGI requires a robot. The robot is the tool that connects physical items with computers. But if we want to use AI to make things like nanotechnology, that means that nanotechnology requires the tools that are suitable for nanomachine creation. Those tools are different than some screwdrivers, that operators can use to fix the car engines. 

The AI and especially Large Language Models, LLMs are new tools. The route to the Artificial General Intelligence AGI might not be as long, as we think. The modular data structure where the LLM works with multiple modules is a route to the AGI is route to the AGI. In the modular data structure, every skill that the AGI has is in the pre-programmed module. This thing makes it possible to create the entirety that the user can operate through the LLM. 

The system might have modules that it uses to control cleaner robots. The cleaner and housekeeping robots can be the same physical tool. However, the mission modules determine the role of the robot. The system has modules that control the temperature in the house. The module controls robot cleaners. The other module is connected to the robot car, which transports the robot to the shop to buy everyday goods. 

The partial mesh network is an open structure. That allows developers to connect an unlimited number of full-mesh networks into that data structure. 

The thing in the AGI is this. The AGI learns through modular data and database structure. When the AGI gets a new skill it simply creates a new module. The system can use so-called double mesh networking. The partial mesh protocol can involve the internal structure of the full mesh networks. So each ball in the partial mesh networking diagram involves the full mesh network. The open structure means that the system and its abilities can expand as much developers or the structure itself needs. 

Determining the AGI is difficult because the AI must handle every problem that it faces. But the AGI is still only a computer program. The full mesh protocol there the center is LLM. The LLM can call the module to make an operation if it requires it. But that requires the existence of a module. 

Sometimes people compare the AGI with things like mobile telephones. How many applications do we use every day? Is it E-mail, net browser, and some payment solutions? So, even if we have an AI that operates with multiple tools, can we call that thing AGI?

And do we have skills and knowledge to benefit the AGI even if we have it? We can have a robot car that drives a human-looking robot to shop to get the pre-ordered things. The modules limit the skills that those robots have. That means the robot can make many things.  

Robot taxis and other robot vehicles are coming.

Robot vehicles like delivery robots are everyday tools in traffic. The small delivery robots are pathfinders for the next big step to AI-controlled traffic. 

The next-generation tools for traffic are robot taxis or full-size robot vehicles. Robot taxis that can carry humans are one thing that can improve our traffic. 

Or, full-size vehicles that can carry humans and bigger cargo. Things like electric vehicles accelerate robot vehicle's generalization. In many images, we can see that robots drive vehicles. 

Robot taxis can drive themselves using internal computers. Network-based structure makes it possible for those computers can share their calculation power with each other. The system can also scale new rules like changes in speed limits for every vehicle that is part of that network system. 

But those systems can involve human-looking robots. That can assist customers. 

Those man-shaped robots can carry packages. And help with other things. And they can also protect those customers against things like robbery. 

The robot vehicle can load itself automatically from the loading station simply. Putting the manipulator with a plug into the socket. Electric vehicles are the most effective tools in limited areas like in cities where they can operate in well-calculated areas. Where they know precisely the distance to loading stations. 

There are also plans to create robot buses and lorries that can operate independently. Things like GPS and mesh protocol in data transmission make it possible to drive the AI. That controls those vehicles. The customer gives the orders to AI through the LLM, a large language model. Then the system selects the route to that destination. The system follows certain parameters to make selections and those parameters include things like how heavy traffic is. 

Diagram of a fully connected Mesh network

And where the customer can jump out of the vehicle. The central computer can follow those vehicles that can also operate as drone swarms.  If some of those vehicles are in a shadow area the system can search it by using the assistance of the other members of the network. 

https://bigthink.com/the-future/the-robotaxis-have-arrived/

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

The photonic chips are the new tools for computing.

 

Things like quantum computers require ultra-fast data handling systems to control them. The quantum computer is the most powerful calculation system in the world. The problem is that the quantum computer requires binary computers to input and output information to the system.  Researchers cannot connect things like screens and keyboards straight to the quantum computer. That's why there is needed a binary computer between the quantum state and input-output devices. 

When the controlling system notices some anomaly it must react immediately. Another thing that the system requires is that. The system that controls the quantum entanglement should not disturb the quantum entanglement and sensors. 

That downloads and uploads data in and out from superpositioned and entangled photons. Or some other particles. All electromagnetic systems cause electromagnetic fields that can affect data that travels in the qubit. 

So the answer is the photonic microchip. The photonic microchip can load data to photons and then deliver it to the quantum computers. There is one little problem with photonic computers. The system needs regular quantum computers to drive information to photonic computers. And, the new nanomaterials can make it possible to change photons to electricity and backward. 

In this model, the quantum computer has three stages. 

The regular binary computer. 

The photonic binary computer

Quantum computer. 

Image 2

The input will happen through the regular binary computer, which decodes it to the photonic binary system. And then the photonic binary system transfers data to the quantum state. When the quantum state makes its duty, the system will return the data to the regular binary computer through a photonic binary computer. 

This model means that the system is scalable and it saves energy. The binary system calls those other layers or states to work with a mission that takes too long time for the first level. If photonic computers cannot solve the problem in a certain time. The system transfers the problem to the quantum state. 

Image 3 

Those photonic processor rings look like token ring architecture. (Image 2)The processing system can involve many processors. That allows it to drive multiple databases at the same time. Or they are hybrid systems. That uses mesh-protocol-based architecture (Image 3). 

In that system, the central processor shares the missions with the other processors. The neural networks use mesh protocol. The mesh- or distributed networks have one benefit to centralized networks. If one processor has problems or damages, the data can pass that processor. 

When we think about the primary computers the photonic microchips can make the ring where they drive information. The system can involve two photonic microchip rings. It can compile the intermission after each processor drive. And if there are anomalies like different results there is something wrong. 

After a certain time. The system can transfer data to the next processor. And when the processor transfers the mission to the new processor. It can make the backup. 

Then there is the control system between those two rings that can compile the data. And that can be the new tool for systems that drive complex data structures. Things like the large language model. The LLM-type systems require. The new physical tools to handle information. New systems must support quantum calculation more effectively. 

The system must start to drive multiple databases at the same time. The photonic systems allow researchers to make new systems. That supports machine learning more effectively than traditional systems. 

https://scitechdaily.com/harnessing-light-quantum-materials-supercharge-data-transmission/

https://scitechdaily.com/integrating-photonics-with-silicon-nanoelectronics-into-chip-designs/

https://scitechdaily.com/microscopic-marvel-a-photonic-device-that-could-change-physics-and-lasers-forever/

The new nanomaterials can make it possible to change photons to electricity and backward. 

The new quantum materials revolutionize information technology.

"An illustration of the 2D perovskite material that was studied by the researchers. The yellow parts illustrate the linker molecules while the purple and pink parts show the perovskite layer. Credit: Chalmers University of Technology | Julia Wiktor" (ScitechDaily, Unlocking the Future of Solar Cells: Scientists Discover Key to Stable Perovskites)

Wikipedia determines quantum materials like this: "Quantum materials is an umbrella term in condensed matter physics that encompasses all materials whose essential properties cannot be described in terms of semiclassical particles and low-level quantum mechanics. " (Wikipedia, quantum materials)

"These are materials that present strong electronic correlations or some type of electronic order, such as superconducting or magnetic orders, or materials whose electronic properties are linked to non-generic quantum effects – topological insulators, Dirac electron systems such as graphene, as well as systems whose collective properties are governed by genuinely quantum behavior, such as ultra-cold atoms, cold excitons, polaritons, and so forth. On the microscopic level, four fundamental degrees of freedom – that of charge, spin, orbit, and lattice – become intertwined, resulting in complex electronic states; the concept of emergence is a common thread in the study of quantum materials." (Wikipedia, quantum materials)

"Quantum materials exhibit puzzling properties with no counterpart in the macroscopic world: quantum entanglement, quantum fluctuations, robust boundary states dependent on the topology of the materials' bulk wave functions, etc. Quantum anomalies such as the chiral magnetic effect link some quantum materials with processes in high-energy physics of quark-gluon plasmas." (Wikipedia, quantum materials)

The term quantum material means material, that has some quantum-level abilities. Those abilities form when the system manipulates and controls some subatomic parts of the atoms. In quantum chemistry, the system can order which carbon chain bond the reactive part of the molecule touches. That means that it's possible to put the reactive part can in the second carbon (o third etc.) in some hydrocarbon chains. That makes it possible to control reactions with very high accuracy. 

"A laser creates pairs of positive and negative charges bound together (large blue and red spheres) in a device made of three atomically thin layers (sheets of metallic red and green spheres). The charge pairs change the properties of the laser beam (red). Credit: University of Maryland, edited" (ScitechDaily, Harnessing Light: Quantum Materials Supercharge Data Transmission)

The ultra-fast light signals can transform into electric signals using nano- and quantum materials. Quantum materials are new and promising tools for many things. Iron-based AI that uses components that emulate neurons and living neural systems requires new materials. One of those materials is perovskite. The pyramid-shaped structure allows the use of this material as an artificial synopsis. The researchers will put the pyramid-shaped structures against each other. Then the perovskite will transfer data to nanotechnical wires. 

These are in the nanotubes, which protect them against outside radiowaves. In those systems, the carbon nanotubes have a metal layer that turns them into a Faraday cage. Perovskite is a material that computers can use to turn laser rays into electric impulses. Small perovskite plates can also be used to give energy to nanomachines. The thing is that the perovskite is the multipurpose tool for nano- and quantum technology. But then we can think about the secured data transmissions. 

"This image shows perovskite photovoltaics in the background with individual perovskite crystals shown as colorful units. Credit: CUBE3D Graphic" (ScitechDaily, New Design Improves Efficiency of Next-Generation Perovskite Solar Cells)

Traditional secured data transmission means that the data is encrypted. The outside actor can see the data, but the data is sorted in a way, that the actor cannot rebuild the message. Quantum encryption means that the data itself is hidden from the observers. The transmitter can use both, optical and radio wave-based data lines. And it can route data through many physical routes. The thing that helps to protect information is the coherent signal carrier that the observers cannot see from the sides. 

The laser system can transfer data in a hollow laser ray that prevents the outsider from seeing the data carrier-laser rays. In the same way, maser systems can use double maser beams where the outside maser beam isolates the data channel. The system can also minimize the transmitting times using three data lines. There are two data lines for one and zero, and the middle line means the pause for the case, that the system sends two 1 or two zeros in a row. 

If the system wants to transport two ones or two zeros in the row (1,1,0,0) there is a problem with a break. In traditional systems, the clock measures the time, a certain number of time pulses determines the break between two zeros or ones. The new system can use the third wire to determine whether the system will change to the next one or zero. A system that uses two different data lines is less vulnerable to outside effects than a regular computer that measures the voltage in the data line. For being fast this kind of system can have the fourth wire that determines if the electricity is on or off in the system. 

Things like Kagome metals can offer a very powerful tool for making things. Like nanotechnical switches and routers. The Kagome structure can be used to control the low-voltage electric impulses in nano-size computers and electronics. Developers can use those things to control independently operating nanomachines. 

https://scitechdaily.com/harnessing-light-quantum-materials-supercharge-data-transmission/

https://scitechdaily.com/new-design-improves-efficiency-of-next-generation-perovskite-solar-cells/

https://scitechdaily.com/tiny-light-flashes-massive-impact-the-next-gen-of-microelectronics/

https://scitechdaily.com/unlocking-the-future-of-solar-cells-scientists-discover-key-to-stable-perovskites/

https://scitechdaily.com/when-flaws-become-features-diamonds-in-quantum-tech/

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

Biocomputers will be man-made aliens.

If we want to make the biocomputers. We will make the modern Frankenstein. Computers that use cloned brains for thinking are man-made aliens. The most capable and dangerous systems that we can even imagine are in the sector of computing. 

Biological computers are a group of mini-brains. That is connected to computers. Human brains use quantum calculation methodology.  The thing that makes them so powerful is their ability to begin computing at many points at the same time. 

The image series shows how the system scales the mission for a larger number of data handlers. If one data handler does not get the correct answer, the system calls more units to work on that too-difficult mission. 

This makes the brain act as the qubit. In the brain, multiple neurons form the structure and layers that act like qubit states. Which makes brains so powerful. Then we can make a model, where the brain cells or neural centers in the human brain. Are replaced using mini-brains. Each mini-brain would be one neural center in the biological computer. The system can connect many mini-brains into one entirety. 

But then we can think about the thing called mini-brains. Minibrains are lab-grown miniature brains. They are created for testing neurological medicines. However, researchers teach those mini-brains to play things like ping-pong. The mini-brains communicate with traditional computers using microchips and sensors that can exchange information between those mini-brains and computers. 

Theoretically, there is no limit to how many mini-brains are connected to the computer. The system must give nutrients to those miniature brains that communicate with computers. The biological processors, or, mini-brains will get tired. And they need lots of nutrients. The system must have life support that removes waste from the mini-brains. 

When the system gives a mission to those mini-brains it can give the same time to the multiple mini-brains or biological data-handling units. The system can have two or three layers where those mini-brains work with the problem. When neurotransmitters go low in those systems, the system can change the operating layer. That allows the biological computer can operate non-stop. The system records the solution and drives it to another mini-brain group. 

Mini-brains are tools that can operate as vital parts of computers that are more intelligent than humans. Nothing denies researchers to connect adult-size human brains into one entirety. Computer-based EEG coders and decoders can make human brains communicate with computers that operate as neural networks. This kind of system requires the ability to code and decode the EEG signals. 

But if that thing is possible to make. The biological computer would be the most powerful machine in the data-handling sector. That we can create. This kind of system involves many risks and possibilities. But if we someday create this system we are making another intelligent organism, a man-made alien. 

Images: AI

The quantum computer can be the answer to AI requirements.

The combination of pressure and low temperature makes it possible to create new types of superconductors. That thing allows researchers to create new fundamental quantum computers. And as you see advancing of quantum computers advance their advance. When quantum computers participate in quantum computer research and development, that thing makes them more advanced than ever before. 

Another thing is that: quantum computers advance slower or less dramatically than the first quantum computers. The quantum computers are the most advanced tools that we can imagine. When we think about their role in high-power computing, those systems will become more and more effective. When more people and laboratories participate in those quantum computer R&D processes. That makes them more effective than before. 

The "less dramatic" advance means that. Quantum computer advances happen in places. Where the users cannot see. That thing is the increase in the qubit states. Or making more advanced AI. That can control qubits. Somebody is sometimes the next breakthrough in quantum computing.

The next big breakthrough is the room-temperature quantum computer. But to make that thing real researchers must work hard. Things like crystal technology, artificial diamonds, and extremely stable conditions can make the room-temperature quantum computer real quite soon. 

It's possible. The quantum computer's super cold heart is in the pressurized and very cold gas in miniature tubes. Then there can be the thermos tube there vacuum isolates the superconducting part of the quantum computer. The idea is that the pressurized and low-temperature part of the system is as small as possible. 

Almost every day we can read about some fundamental advances in quantum computing. The next-generation materials require very complex models. The manufacturing systems must react immediately if something in the environment changes. Those next-generation materials are things that quantum computers require. 

Quantum computers require new types of encryption. But they can also run the new AI models. The new large language models, LLMs are things that are more and more advanced. Every new skill that the LLM has spreads its size. And LLM requires lots of machine power. The new LLMs are more multitasking and versatile. That means they are larger. And those larger LLMs require new types of database structures and new types of code control. 

Quantum computers are the answer to the computer requirements that the new large AI models require. And even if the LLM does not turn larger or more versatile. The growing number of users means that the physical systems, or iron must increase its power. And that is one of the things where quantum computers can give a response.  The quantum computer itself can be energy efficient. But all its support systems like large-size cooling systems require lots of energy. 

The biological microprocessors

"The Underground Network Fungi grow by releasing spores, which can germinate and form long, spidery threads underground (a mycelium). We typically only see the tiny mushrooms on the surface without realizing that there’s a vast network of interconnected mycelium beneath our feet. It is through this network that information can be shared, somewhat like neural connections in the brain." (ScitechDaily, No Brains, No Problem: The Surprising Intelligence of Fungi)

Fungi are the most impressive species in the world. Those versatile creatures bring a new state to intelligence research and machine-based artificial intelligence. That is suitable for robots as well as larger computers. Fungi can learn and remember things without a single neuron. That is what makes them interesting because those proteins and enzyme-based chemical brains can used to control nanomachines. 

The ability to learn things and make decisions are things that are connected to neurons. Fungi make those things without neurons. And that is the thing that makes them interesting. The ability to communicate through the mycelium network makes fungi an interesting thing to understand neuro-evolution. That makes it possible to create biological microchips that don't all the time require nutrients. 

"A study has revealed that fungi can exhibit intelligent behaviors like decision-making and learning, without having a brain. Credit: ©Yu Fukasawa et al." (ScitechDaily, No Brains, No Problem: The Surprising Intelligence of Fungi)

"Fungal mycelial networks connecting wood blocks arranged in circle (left) and cross (right) shapes. Credit: ©Yu Fukasawa et al." (ScitechDaily, No Brains, No Problem: The Surprising Intelligence of Fungi)

Researchers made experiments with neurons that communicate with microchips. Those tests were successful, but the problem is how to feed those neurons. If the fungi's intelligence is based on the enzyme-protein interactions that get their power from mitochondria. That would make it possible to make a so-called "dry protein processor".

In that case, those proteins and enzymes are in non-organic jelly. And if those reactions are electrically controlled nano-size wires can control them. In a dry protein processor, the proteins and enzymes that make fungi "intelligent" fibers are separated from the cells, and transferred to artificial jelly where they interact with each other. Those things don't need nutrients. But living fungi cells can be used for that mission. 

That thing also makes it possible to create systems that can be used to program neurons. It's possible that fungi are one thing that can used for biological AI-processors. When we think about fungi and their interesting abilities to handle things, we must realize that the same process can offer the possibility to create something that we don't even imagine. 

Fungi and their special ability to remember and learn things cause situations in which we should re-estimate our way of thinking about intelligence. The fungi can make intelligent things without neurons, and that means that maybe somewhere live organisms that don't have neurons at all. 

When we think about things, like the Search for Extraterrestrial Intelligence, SETI program the intelligent creature is determined to be a creature that has neurons. The ability to learn and make decisions without neurons is expected to be impossible. But fungi's ability to make decisions and learn things makes it possible to adjust that model. This thing makes those creatures more interesting than we expected. 

https://scitechdaily.com/no-brains-no-problem-the-surprising-intelligence-of-fungi/

Why is the AI-development so hard to control?

Sometimes, researchers say that the developers of the AI lost control of their product. The reason for that is that the AI advances so fast. But they don't answer questions about why AI advances so fast. The answer is that the AI takes part in the AI development. The other thing is that the AI is the tool, that almost everybody can use. 

The AI is not a tool that everybody can use automatically. The system requires that the user gives commands using good grammar. So learning to give commands in the right way to AI takes time. And the AI use requires practice. Or everything can go wrong. 

The ability to use AI to make new solutions and new applications increases the number of solutions, that use the large language models, LLM, or can communicate with LLM through some other applications. That means that. Many more or less official enthusiasts use AI for software development. And that causes the effect. That the AI-using developers will create many new more or less legal applications. The AI can almost automatically create legal applications. So there is no problem with those things. 

There are people. Who tries to slow the AI advancement, but the problem is that those people work with their own AI projects. Another problem is that they work in high-class positions in their ICT corporations. That means those people will face suspicions about things like cartels. Or otherwise saying dealing over company borders is against the business laws. Sometimes people just say that those leaders want to keep breaking because their own AI projects are not successful. 

When we think the AI as the code generator, that application makes it possible for developers to create large-scale projects benefiting AI. The code, that the AI makes requires biz talk between developers and the AI. The development process is the interaction where the AI and developer play "ping pong" with software. That means, that the AI can operate in a test application and then the human developer tells how the code must be changed. The AI can automatically make code, that the programming application can change. But of course, the program requires adjustment for things like paths and other things. The system can run test applications on the server. 

The ability to make a large code that is ready for run is the thing, that makes the AI a powerful tool. That means the AI can create many things faster than before it. Another thing is that AI is the tool that allows even one man to begin quite a large programming project. This person requires at least basic programming skills. Like putting paths into the right form. And the code that the AI makes under the control of nonqualified coders might not be very beautiful. 

The Chat-GPT style AI unveils cancer with 96% accuracy.

Image: ScitechDaily

This is the breakthrough in the AI and its development. The AI that searches for things like cancer is the tool that can make new waves in medical work. This kind of search is the tool that leaves more time for doctors to talk and discuss with patients. And maybe that returns the medical work to its roots. Or in the worst case that gives a reason to kick more doctors off their work. Decision is in the hands of the people, who control social politics. 

The AI is neutral, and it doesn't make mistakes. The AI can collect data from many data sources into one entirety. And that makes it the ultimate tool for this kind of work. AI can search for cancer using DNA, antibody analysis, and X-ray images. And it can make that search a routine operation. If the cancer is detected in the early stage, It increases the ability to heal from that disease. 

When we think about the Chat GPT-type assistants that operate with doctors we must understand one thing. Its purpose is to make their work easier. If the patient has cancer or some other disease. That system can also give medicines for the disease automatically when the doctor makes the diagnosis. The system will know the special requirements like. Do some antibiotics fit cancer patients? 

When we think about AI and its use for medical purposes we must understand that the AI must use multiple sources. Only by using multiple sources, the AI can make a trusted diagnosis. The basis of the scientific work is that one source is not trusted. Only multiple separate independent sources make research trusted. And the AI must follow this principle when it operates as a scientific assistant. 

That means. The AI must connect data from the different scanners and DNA samples. This means that not one but many systems together can make the system more trusted. And the other thing is this. The AI's mission is to assist doctors. Its mission is not to make decisions. 

The AI assistant can order research while the doctor makes the diagnosis. That leaves more time to discuss with the patient. The AI can fill out things like routine forms and order routine research. 

https://scitechdaily.com/96-accuracy-harvard-scientists-unveil-revolutionary-chatgpt-like-ai-for-cancer-diagnosis/

When requirements grow, programs grow.

The main problem with computing is when developers make something. There is needed new solutions almost immediately. Those new solutions make the application interesting. And many times. Those new solutions mean new abilities for applications. New skills require new libraries. And that thing cumulates the size of the program. 

The new libraries require more space and more effective computing. The thing is that AI is going to more detailed program code. When we think about the first AI "chatbots" the simple programs that asked people some questions, and then the program gave some pre-programmed answers, we must realize that those programs looked intelligent. 

One example of those programs is a program that asks: 

Are you a boy or a girl?

A: Boy

What's your name"?

A: John

How old are you?

A: 23

Where do you live?

Bristol

Then the program said: 

Please to meet you: 

You are a boy, whose name is John. You are 23 years old, and you live in Bristol. And I'm your servant. 

Those things are quite easy to program by using C, Java, or C++. But that program puts answers to the right points in the programmed text. And the form of the text doesn't require us to notice things like "she" or "he".  That simple code is an example of pseudo-intelligence. One "else" command makes it possible to create an answer that if the answer is something else, the computer answers that "you must answer "boy" or "girl". That makes those programs look intelligent. If the programmer has time, it's possible to add all British first names and all the world's city names to the list of allowed worlds. And that gives an appearance of intelligence. 

The thing is that when we think about the modern AI that can control things like cars, we must realize that in the middle is a large language model, LLM. The LLM itself does nothing. But it transfers the data to the part of the code. That controls the self-driving cars. That LLM stays at a tolerable size. But the libraries that the system needs grow. That makes it hard to control data in the system. 

The network-based architecture means that the system can have multiple, limited AIs or LLMs that are connected into the entirety. That thing makes the system more flexible. Because the system has many separate operating cores. It allows the developers to create those cells as modules. Every module is an independently operating part of the structure. 

Above: Mesh network. In network-based systems, every computer runs its own LLM and modules connected to it. So every computer involves certain action series. And, each computer is responsible for its part of the system's skills. 

The structure might look like some kind of mesh topology. That means that there is a central LLM. That is called the specialized LLM to make the mission. The system operates like a mesh topology. When as an example a robot car drives a robot to the shop it sends a mission to the next operator. 

That operator is the robot that goes into the shop. The mesh model means that the system must not bother the central actor all the time. That means that if the central processor or LLM is busy or the system is out of connection the sub-system can continue its mission without the central actor's support or control.  

When a user gives orders to the central LLM. That can transfer the mission to the sub-LLM that has data to complete the mission. The networked structure makes it possible that one mission doesn't keep the entire system busy. When one module operates with some mission. Other parts of the system can perform other duties. 

In modern neural networks, the middle of the system is the LLM. The LLM is connected with sub-LLM systems. The system looks like a neural network. When an owner calls, the car to the door. The central LLM gives a mission to the LLM that controls the car. The car's AI system can interact with surveillance cameras and the surveillance system tells if there are some dangers in that area. 

Or if there are no surveillance cameras the owner can send a drone that looks if there is something behind corners. The drone can hang over the vehicle and send the image to the computer where the car's corners are going. The car requires a 3D camera system and radar. That it can drive safely. 

And then it requires large-size databases that tell what the car should do in all situations that it can face on the road. There are lots of things that the system must avoid. When we think about the models of the complex systems we must create the systems using the cell- or neural-based networked architecture. For these reasons, I told you before. 

 

The AI and creativity.

 

The AI can make programming more effective than ever before. However, the quality of the code can decrease. In the same way, AI can make inflation into computer art. 

Maybe AI will not destroy the world. But it will change it forever. Things like creativity are now possible without a long time and drawing skills. And that makes the AI a fundamental tool for publishers. The images below this part of the text are AI-created. The creation of those images took about 30 minutes. And that thing tells how easy is to use the AI. That is a fantastic and sad thing. The creation of nice images doesn't require any kind of drawing skills. 

The user must only describe those images to the AI and then it creates those images. AI is a tool that can kill real creativity. 

I think that the images that Bing created are examples of how people will start to make things using AI. The AI is effective. Business life respects effective people. Even if we think. That AI will not advance anymore. Or advancing is slower than calculated. The ability to create things like texts and other things like images are things that keep the AI an interesting tool. 

AI is one of the most successful things in the history of the Internet. It's abilities and solutions that people can create with it are expanding. Developers innovate many new things using AI. The AI is the tool that makes coding effective. 

When we think about computer programs in the 1980s. Things, like the Commodore 64 ran good-looking, pretentious software, and modern computers that ran very sophisticated programs. Sometimes I thought about what modern computers can do if their programs are written with the same accuracy as Commodore-64's programs. 

We must say that. In the time of the Commodore 64, or C-64, developers made thicker code. The code was effective, because of the limits of the systems. But today large mass memories and network-based systems make it possible for modern coders to leave things like "killed lines" or "empty code" in the programs. In the time of C-64, the code was made using effective programming languages. 

Today programming languages make so-called bad or empty marks in the program. The programming languages are versatile. But that makes them a little bit complicated. Semi-automatic encoding systems create less effective code.  In the 1980's. Programmers were some kind of engineers. They were trained for years. Today the programmers are trained 9 months or even less. They use prepared libraries. And that means coding faced inflation. 

The AI is the tool that can make the code following descriptions that the user writes into it. The thing is that those AI's or large language models, LLMs are the tools that make the coding more effective than ever before. The thing is that. The AI will not make complete or perfect code. It makes code effective way, and effectiveness is the word for the day. Modern computers are things that have powerful memories and processors. They can handle less effective code than C-64. 

The trend has been this. The code and how effective it is doesn't matter. The number of accepted lines is the thing that means something.  This means that things like timetables. And requirements for creating very much code in a short time and other things. Like acceptance percent force people to use AI to create code for new programs. 

AI masterpieces