AI is the tool that can change web searches forever.

The new AI will not destroy Google immediately. But those new systems can have a big influence on Google for a longer period. The fact is Google controls so large a data mass that its power on AI development is stunning. However, the new AI-based tools can connect search results from multiple search engines. And then it can refer to those results and make the list of homepages that it used for the solution. Google's dominance ends when those AI-based search solutions can create such large databases that it can turn independent from traditional search engines. 

And that is the main problem with those things. Google is not the only search engine in the world. There are many other search engines that want to drop Google from its position. But some of those search engines are powered by Google. They offer one interface between a search engine and the user. Search engines require lots of computer power as well as AI needs. Companies like Google sell data. 

That means those search engines are operated by private corporations whose business is to sell data. The thing that makes those companies so powerful is that they collect data about the clicks that users give to them. The number of clicks raises the page rank. And that raises the homepage’s position on the search result list. This is one thing that causes critics against that system. It’s hard to get new home pages to the top of that page ranking list. People normally see only a couple of top homepages from that list. And then they select the thing that they think is the best. 

This is the Matthew effect in the web searches. Homepages that already have a massive number of clicks will get much more. And homepages that have no clicks will not even get them. That is the thing in web dominance. AI-based solutions can use many search engines at the same time. The thing that makes those applications interesting is the results or references that depend on the information that it gets if the AI-based web search application can see what type of persons will read those homepages. If some homepage is used by professors who work in a trusted university, that can justify that other people can trust those homepages. But how to confirm those people’s real identity? 

One solution is the quest book where people can say their work. And then we must also realize that confirming those answers is quite difficult. People can write anything they want in those quest books. Confirming those answers requires hard recognition. And that’s against privacy. Privacy protects people on the net. But the same thing offers protection for cheaters, propagandists, and net criminals. There are countries that collect data from all their citizens. 

But people will not need only search lists. The problem with those lists is that they are based on web addresses. There is a possibility that somebody changes the data that those homepages involve. First the page rank will rise using some addictive material. Then the workers change texts, or information from the homepage. That is the tool that is effective for hybrid operations and propaganda work. That is one thing that causes the need to create more advanced tools that can use data deeper than regular search engines. 

The biggest problem with modern networks is disinformation. The other problem is how to describe disinformation? People like V. Putin has a different way to see that thing than regular western actors. That is one of the things that we must realize. The same tool that works against propaganda and disinformation can turn the ultimate tool in their hands. 

https://www.rudebaguette.com/en/2025/06/chatgpt-wont-kill-google-sam-altman-downplays-the-hype-while-quietly-reshaping-the-future-of-search-with-every-new-update/

A new way to control photon chains makes it possible to create scalable quantum computers.

"Scientists have found a way to generate entangled photons using metasurfaces, simplifying quantum computing and communication. (Generating multiphoton entanglement with a tiny metasurface.) Credit: Peking University" (ScitechDaily, New Photon Entanglement Breakthrough Could Miniaturize Quantum Computers)

The photon chain makes it possible to create new types of quantum systems. Those systems can combine data handling and data transportation. The quantum computer in the future can look like a tube, there information travels in light flow in the photon-bound chains. 

The new types of quantum entanglement make it possible to create long-distance quantum data transportation. The idea is that the data travels between photons. Those photons are in chains. 

The system can make it possible to create a quantum chain where the other side is always at the lower energy level. The system can also let one of the photons in the chains to a very low energy level. Then it can dump data into it. After that, the system can raise its energy level. 

If data can travel between curved photon chains. That makes quantum computers more scalable. 

It can transmit information between multiple photons at the same time. But it can transmit identical information between quantum-, or qubit lines.

That ability makes it possible to make more effective error-detection protocols. That kind of system is one of the most fascinating that we can imagine. 

The new photon quantum entanglement makes the new types of quantum computers possible. The quantum computer's "heart": the quantum entanglement can be put into the series. And that means information can travel long distances in that kind of quantum computer. The difference between regular quantum networks is that in this kind of system, the quantum network can also handle data as computers. 

In traditional systems networks just transmit information. And the computer handles it. The quantum network can connect those systems in one entirety. So when this kind of network transports information it also handles it. 

That means a quantum computer can be the tube, there is a bundle of the series of superpositioned and entangled photons. The series of entangled photons have one superiority that the simpler systems have not. The system can cut the photon chain at a certain point. When it wants to transport more information into it. That means the quantum system can create a loop. 

There other systems can transport information in the middle of the process. By connecting multiple loops. That system can make a structure that can handle things more complicated than regular linear quantum computers. The circuit-shaped data handling process can make it possible for the system to make the data circuit, or data loop that allows it to drive calculations as cycles. So the quantum system can calculate series in those circuits. 

https://scitechdaily.com/new-photon-entanglement-breakthrough-could-miniaturize-quantum-computers/

https://www.space.com/space-exploration/tech/scientists-discover-simpler-way-to-achieve-einsteins-spooky-action-at-a-distance-thanks-to-ai-breakthrough-bringing-quantum-internet-closer-to-reality?utm_source=flipboard&utm_content=topic/technology

All quantum systems, including black holes, can link each other into complex entirety.

 

"Oxford researchers created the first distributed quantum computer, solving scalability challenges by linking small quantum devices via photonic connections. Credit: SciTechDaily.com" (ScitechDaily, Scientists Just Linked Quantum Processors in a Historic Step Toward Scalable Supercomputers)

Researchers linked quantum processors together. And that is one step forward to scalable quantum computers. The problem with scalable quantum computers is that the quantum entanglements in those systems should be identical. And multiplying the quantum entanglements is quite a difficult but not impossible thing. Theoretically, we can say that things like oscillating hydrogen atoms can make it possible to create a quantum neural network.

Maybe quite soon, researchers can create a molecule where the hydrogen atoms transport information in the structure. The quantum system can transport oscillation in the quantum processors. And that is one way to make a networked and scalable quantum computer. Maybe those frozen molecules can someday create the quantum neural network. That kind of molecule can be a new tool in quantum technology.

The hydrogen atoms are the tools in the new quantum networks. The information travels between those atom's quantum fields. Or actually, information travels through this quantum structure as it travels through the winch wheel series.

That thing causes an idea that maybe black holes can form similar structures in the universe. The black hole is the most powerful quantum phenomenon in the universe. Information can travel also in the black hole's quantum fields similar way as it travels in other quantum networks. And one interesting thing is that also things like gravity fields are quantum fields. 

Above hydrogen ions can create networks. And also is possible that black holes can make similar chains. The hydrogen ion acts as quantum dot that connects other quantum dots into the quantum networks. "Rendering of the tilting of relativistic Dirac cones in the bulk electronic bands of a quasi-two-dimensional (2D) magnetic topological semimetal achieved with insertion of hydrogen that generates tunable low-dissipation chiral charge currents. Credit: Krusin Lab" (ScitechDaily, Hydrogen Ions Are Revolutionizing Quantum Tech – Here’s How)

"Illustration of a quantum simulator with atoms trapped into a square lattice with lasers. The small spheres at the corners are atoms in their lowest energy state. The ones inside a blue sphere are exited (higher in energy) by the first laser, the ones inside yellow spheres are excited by the second laser (even more higher in energy). Credit: TU Delft"(Phys.org, A new design for quantum computers)

The black holes can make the quantum networks as well as all other quantum particles. In that thing, the quantum network's quantum dots are black holes. 

 

 

Above: The quantum network can look like this. The outside forces. Like still hypothetical fifth force destroy those networks.

The black holes will get their "lunch" when they transport energy from the gases around them. Some parts of energy and the particles, including photons, are trapped in the point. Called event horizon. There also photons travel around the black hole. The event horizon is the place where escaping velocity reaches the speed of light. And if the phenomenon where the photon is half outside and half inside the event horizon is possible that thing gives new ideas for the gravitational radiation models.

It is possible. That the superstring can start to travel through the photon. And that makes the structure able to create the system, that forms gravitational radiation or gravitational waves. Gravitational waves are like cyclotron radiation. There the top of the wave is quite low. But behind it comes the energy ditch that is far deeper than the energy wave before it.

In that model, the particles that close black holes at a certain angle start to rotate in the event horizon. If energy and particles come to the black hole at a straight angle. They go straight through the event horizon. If they impact particles that are at the border to fall behind the event horizon. Those particles take energy in their quantum fields. And then send it backward. So that means the gravitational wave source may be in the particles that travel almost with the speed of light around the black holes. So, maybe black holes form similar networks. As other quantum systems can form.  

https://phys.org/news/2024-02-quantum.html

https://scitechdaily.com/black-holes-cook-their-own-fuel-in-a-cosmic-feast/

https://scitechdaily.com/scientists-just-linked-quantum-processors-in-a-historic-step-toward-scalable-supercomputers/

https://scitechdaily.com/hydrogen-ions-are-revolutionizing-quantum-tech-heres-how/

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. 

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/

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 Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game.

  The Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game. 

The Chat GPT,  Bing, and many other AI-based chatbot versions are massive systems that should fit every situation. The problem with common AI is that these kinds of systems require lots of capacity, and there are lots of sources that those systems must use. 

This thing means that the trustworthiness of sources is problematic. The reason for this thing is that the AI doesn't think. It collects data by following certain parameters. That thing makes those systems vulnerable in cases where they should search for information that is not very common. 

The smaller-size specific AI-based systems that can use the same engines with Chat GPT and Bing are more suitable for things like scientific writing. The AI is an ultimate tool if it has a pre-programmed list of trusted and estimated sources. If a writer wants information about some very uncommon things like quantum mechanics. The AI can use sources. That passed scientific estimation. The results are best in business.

The limited AIs can act as independently operating modules in the networked AI-based systems. In that case, those limited AIs act as event handlers for the common AIs. 

Maybe we think that those small AIs operate independently. But the fact is that those independently operating smaller AIs can used as event handlers. That system is connected with bigger AI:s. In that model, those limited AIs can form independently operating module networks, which makes the common AIs more powerful, and accurate than ever before. 

Those independently operating limited AIs can network below the Chat GPT style AIs. The idea is that the limited AIs can form the entirety under the common AI control. This means that the limited AI:s can form a network. That the bigger AIs can be used as event handlers. 

Next comes two examples of limited and powerful AIs. Those things can turn game changers. 

The Finnish AI predicted very accurately where the wildfire started. 

One of the examples of specific highly accurate AI is the AI that predicts wildfire. The researchers from Finnish Aalto-University have created an AI that can predict wildfires. And that AI has shown its success. In this case, the AI uses parameters like humidity in the air, air temperature, and wind speed. The system also can use statistics about the conditions and places where wildlife is starting. 

Also, things like the frequency of lightning and things that are lightning common along with rain or in dry weather are things, that help to predict wildlife. The system also can follow volcanic activity and how often and in what kinds of conditions people are making fire. If there are no spark arresters in chimneys that increases the risk of wildfire.  In that case, the specific AI follows only a limited number of variables. And that thing makes it very accurate. 

The military AI can predict where the enemy attack comes from. 

The military AI can use variables like how hard the ground is, is there some muddy river bottoms, and other kinds of things to predict the place where the enemy might want to attack. There are also many other variables like enemy vehicles and weapons that can affect to that place.

But if the enemy uses tanks the ground's hardness is extremely important. Another thing that the system must know is how steep the riverbed is. That is important information for tanks. In the cases that they cannot use bridges. 

There are, of course, many other variables that the system must have. But those two things are examples of small, and specific AIs. Those RISCs- AI:s are not as flexible as Chat GPT and Bing, but they are highly accurate. And the limited operational areas make variable handling easier than in some common AIs.  

https://www.dezeen.com/2023/08/24/ai-wildfire-model-firecnn-aalto-university-aitopia/

https://www.aalto.fi/en/news/new-ai-system-predicts-how-to-prevent-wildfires