Laser weapons are coming.

Above: Spaceborne mirrors aim laser beams from Ground-based lasers. (DoD image)

The latest laser technology is represented in the Chinese laser system. That man-portable laser. That fits in a backpack is the tool that could shoot down drones. This means that the laser weapons. They are entering the battlefields. The man-portable laser. It is so small and compact. It can be installed on the roof of trucks, tanks, or jeeps. The AI-based camera system. It can search for drones. And aim that system. Into the drones. The system. It can be used with the same technology. 

That is used in the laser-based mosquito hunting system. The laser-based mosquito trap killed eight mosquitoes in a second. A small-sized compact laser system that uses that technology can shoot down drones. The same system that eliminates pests. It can eliminate drones. If the user knows how to train that system to find drones. And  its laser systems are more powerful.  

The Israeli Trophy system is the rifle-calibre system. That shoots bullets against incoming anti-tank missiles. Those active protection systems are used in tanks and other vehicles. The laser-based version of that system. It can protect aircraft, tanks, and other targets. Against incoming missiles, rockets, and other ammunition. The high-power laser can also be used against enemy  vehicles and troops. 

The high-power laser. That is installed in the high-flying aircraft or drone. It can be used against hypersonic missiles. And satellites. The laser system. It must only cause a small amount of damage to the nose of a hypersonic missile. And that rips the missile or aircraft into pieces. The high-power laser systems are always dangerous. The laser system that drops drones can be installed in drones, helicopters, or even in strategic bombers. The high-precision lasers can also destroy things like incoming bombs and shells. 

Above: Artist's impression of truck-mounted 100kw laser systems. 

This system. It can destroy incoming missiles. And that kind of laser system. They can protect. The high-flying bombers against the anti-aircraft missiles. Those systems. They can be tools. That protects things. Like the jet fighters. B-52, B-1, B-2, and B-21  bombers, helicopters, ships, and other ground vehicles from incoming drones or missiles. The active denial system. Can increase the elder aircraft. And tanks' survivability on the battlefield. 

When we think about things like orbital laser platforms. That purpose. It is to destroy intercontinental ballistic missiles. Those lasers. They will turn. Into very large and complicated things. The solution to that problem. It can be a modular system. There, the laser will be launched into orbit in pieces. Those bites are like satellites. And they can dock with each other, forming a chain. Those satellite chains. They can be kilometers long. 

Another version is that. The rocket. It can launch mirrors into space. The mirror chain.  It can aim ground-based high-energy laser beams precisely at the desired point. In those systems. The laser is on the ground. And it shoots its beams to spaceborne mirrors. Using the relay mirror. The relay mirror on the side, where the beam comes out from those ultra-powerful, kilometers-long lasers. They can aim those beams into the orbital mirrors. The smaller lasers can be mounted in the telescope towers. In those cases. The smaller lasers. They can act as groups. 

The laser weapons are suitable for anti-aircraft systems. But their weakness is that they cannot fire things. Those are behind obstacles like houses or hills. The answer to that problem. It can be found from drones. The drone can carry the mirror. That aims a laser beam precisely at the wanted point. Those small mirror drones. They can aim laser beams over or around obstacles. But as we know. High-energy lasers can also be used to detonate explosives. When those high-energy laser beams hit things like drones. Those systems. They can pump. A lot of energy. Into targets. And if that energy detonates those drones. The detonation. It can be very powerful. And throw shrapnel around. 

https://www.defensenews.com/land/2019/05/16/dynetics-lockheed-team-beats-out-raytheon-to-build-100-kilowatt-laser-weapon/

https://interestingengineering.com/ai-robotics/ai-eliminates-pests-real-time

https://interestingengineering.com/military/chinese-man-portable-laser-weapon

https://en.wikipedia.org/wiki/Trophy_(countermeasure)

The modern Frankenstein. The BCI.

Some people say that the BCI. A brain-computer interface is just another virtual reality. That the BCI is not. The BCI means the borderless communication between a computer and living neural tissue.  The BCI can make it possible to create so-called intelligent animals. The brain implant makes it possible to create animals. That follows the orders. Of the computer. And the user of the computer gives. Those biorobots can be the next-generation tools for animal research. But the BCI chips. They can make it possible to create a modern Frankenstein. The modern Frankenstein means technology. That is developed for serving good. 

But turns into evil. In the same way as legendary Dr. Viktor Frankenstein in Mary Shelley’s novel “Frankenstein, New Prometheus” believed in his technology. The returned dead body. Back  alive and wanted to serve good. This monster that Frankenstein created turned against its master. The BCI technology allows users to control robots as their external bodies. Allows masters to control them. The use of the BCI. It is an interaction. The use of the BCI allows computers to interact directly with the brain. And that means that. Those systems are the key to robots. As intelligent as humans. 

The BCI makes it possible. To train AI.  By using virtual characters and simulations more effectively. And that means the BCI is a big step. Also for the AI development. In those systems. The controller uses the virtual character. Like a virtual jet fighter. To train an AI that controls robot systems. 

There. A brain in a vat. It can remotely control the living bodies. It’s theoretically possible to remove human brains. And replace them. By using a computer that controls the living body. Or otherwise. The living brain in a vat can control humanoid robots by using the BCI. Or the humans can communicate with those systems. And the BCI. It is the key to TRV, technical remote viewing. In those systems. The BCI. It communicates with drones or other devices. And the implant. That is put in the animal's brain. It can transmit the same senses to the computer. That the animal itself gets. 

The most dangerous dual-use products are brain-implanted microchips. They can give new life. For paralyzed people. ‘They can allow a person to control an exoskeleton. Or click themselves into the internet. By using those microchips. And we know their potential in the new types of user interfaces. The shadow is this. BCI. Brain-Computer Interface opens the possibility. to read the user’s thoughts. 

And another problem is that those microchips can be misused. Those microchips open the road to human brains. This means that those systems allow. The controller stimulates targeted areas in the human brain. This means that if the person who has that microchip doesn’t follow orders. 

That controller can give a pain signal to the victim’s brain. Or, in the worst case, take the person under control. The BCI is the ultimate version of virtual reality. If we want that thing to happen. If the person uses the BCI, then there are no security systems. That thing can cause a situation. Where users will forget themselves in that virtual reality. The virtual reality. The matrix can be a complex structure with multiple internal spaces or levels. 

“A brain in a vat that believes it is walking” (Wikipedia, A brain in a vat that believes it is walking). Same way. This thing can control a virtual character on the screen. It can control physical robots. And that makes this technology promising. But there are always ethical questions. 

If the user falls asleep. That means that person will not remember being in the virtual reality. And that is the biggest thing in the BCI. The user. It will not make a difference. Between stimuli. That comes from the senses. And stimuli. That comes from the BCI systems. So, this means that. The user will not separate things that happen in the computer's memory. From things that happen in real life. 

The BCI can create ultimate fake memories. But the biggest problem is that. The user can forget being in the matrix. And this means that the person can die. Because that user forgets to drink and eat. Or they eat things in virtual reality. The BCI is a tool that interacts directly with the nervous system. This means that hackers can hack. The mind of that user. 

But the BCI is the next-generation tool to control computers. And the computer can also control humans who are connected to it. The ability to adjust. The thoughts of the person. It can serve good purposes. But in the hands of evil. That thing. It can be a gateway to a very powerful dystopia. The ability to read minds is the key to a better justice system. But the same tools are in the hands of dictators. 

The ultimate tool for freedom is the ultimate tool for imprisoning the person. This means that. This kind of system can be misused. If it exists. The singularity between brains and computers is a thing. That opens new paths for many things. Some of those things are good. But some of them. They are the most evil things that we can imagine. Robots that are as intelligent as we are require real brains. The cloned. Lab-grown brains can control robot bodies. 

Microchip technology allows developers to connect those brains to the robot body. The only thing that those organic computers require is nutrients. The robot with real brains can be exactly as intelligent as humans. And the BCI technology makes it possible to create interfaces between those cloned brains and their robot bodies. This technology. It is at least partially already exists. And this means that those systems. They can be at the door. The BCI allows training the AI with a very strong boost. The BCI allows the use of simulators. More effectively for training robots. This means that by using virtual characters. The user can create more complex operational models than without the BCI. 

https://interestingengineering.com/science/chinas-new-flexible-brain-computer-chip-retains-94-efficiency-after-18-months

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

The ability to generalize things. It is the main element for AI. And it's trustworthy.

The term Matrioshka brain. Means a giant, but hypothetical supercomputer. The idea is taken from Dyson’s sphere. The ultimate megastructure. Created by the physicist Freeman Dyson. The Dyson sphere means a theoretical massive sphere. That surrounds an entire star or solar system. The evolution model of that hypothetical megastructure called the Matrioshka brain. The giant supercomputer that surrounds entire stars. Even the solar system-sized megacomputers. They can interact with human-sized robots or laptops. And that makes those systems interesting. This kind of data chain can be like a group of matryoshka dolls. In those dolls, there is always a doll inside the larger doll. In the same way as the matryoshka model. The supercomputers form. The chain there, the larger computers operate the smaller computers by using chains. The next computer in the line is only a little bit smaller. Than the larger computer. 

AI learned laws of physics. And gamers could play games using their brainwaves. These kinds of things tell about the abilities of the AI. The AI, or the LLM (large language model), is a tool that can learn to read things like brainwaves. In the same way as the AI observes and compiles the radio waves that neutron stars transmit. The AI compiles physical movements and reactions in the human body into brainwaves. That its sensors observe. The AI can connect certain brainwaves to certain actions. And that is the key element in the BCi (Brain Computer Interface). The AI requires training. It can connect certain. Brain waves. With certain actions. 

The AI can also read the human mind. By benefiting from signals that certain brain lobes send to the spoken words. Those signals can be used. For giving data. To a speech-to-text application. And then the system can drive that text into the LLM. The computer games. They can be used as a simulation to map the brain waves. And connect that data. With things like physical objects, such as robots. The BCI can control robots and things like aircraft. There is no limit. For the use of the BCI. And that is one of the things that we must realize. 

AI learns things faster than humans. When it must guard the qubit. The AI must only adjust its temperature. And it must know the point. There. The information must be stored in some mass memory. And the qubit must be re-adjusted. This means that the AI is a very effective tool. When it must operate in a limited and well-controlled environment. The reason why the AI is the tool. That can solve physics problems. Or it can create new math. The AI can operate with longer prime decimal numbers than ever before. This is why the AI is a revolutionary tool. When AI must use only observations and connect that data with previous observations. It can make things that we cannot even imagine. Creation of the new physics. Requires the ability to interconnect objects from multiple sources. 

How does AI win architectural competitions? The problem with AI is how to transform things like buildings and their details into a numerical model. The numerical introduction. The main work is. How to transform materials, angles, and colours into numerical form. The AI can use statistical methods to create new houses. In those cases, the AI must only find a formula. That it must follow.  The idea is that. The AI must find the way. To please the jury. And the answer. It can be the statistics. If people have similar backgrounds. They probably like similar things. And the AI can use that thing in their work. 

The architectural competition is abstract. But when we think about the AI and the abstraction that people must handle in that work. We must realize that when people have similar backgrounds. They have similar ways to handle abstraction. So if we have a certain group of people. Acts in a similar way. We can generalize those people and their behavior to all people with similar backgrounds. So, that means that. If we cannot predict an individual person’s behavior. That means we don’t know everything about that person. 

But then we can ask one thing: can a computer handle abstraction? The computer can create things that do not physically exist. The system can use complicated flow simulations and connect them with CAD images. This means that. The AI. It can design things like aircraft. The system can connect those simulations with things like matter matter strength simulations. And this makes it possible to create. The simulations about the structures that don’t exist. The AI makes things using mathematical or sharper. Statistical models. If the AI must design houses for the architectural competition. It must collect data about the environment where the planned house will be built. Then it must search for similarities. With other winners and their environments. Those similarities. They are the purpose of the house. And the type of houses were there before the work was introduced. 

Then the AI requires information about the background of the jury members. Where those jury members sit. Before they entered the jury.  What kind of decisions did they make before that competition? What articles are written about houses, and how are they written?  And how do they show? Are they qualified people? For that architecture jury? This means that if similar persons with similar backgrounds judge competition entries. They should like similar works. And that helps the AI to create its advertisements. The AI also requires building instructions and material restrictions. So that it can create works that please the jury. This is the idea of the competitions. The works should only please the jury. And the AI must find data. That uncovers the type of buildings that pleases the jury members. This means that the AI. It can make creative work. Buildings are always full of geometrical details. 

That thing can be used to calculate statistical things like angles of certain walls. The most challenging thing is this. How to transform things like materials  into numbers. The solution can be that. The concrete. It can have a numerical value of 2. Colors can also have numerical values. Red, it can be 4. If the concrete element has two colors. The system can calculate. On what percentage of the area can certain colors cover? And of course, scattering of those colors can also be calculated. The system must calculate the area. That certain color covers. And that makes it possible to create numerical statistics about things. It must notice when it tries to win that theoretical competition. Maybe quite soon, we will see the AI create things like houses in architectural competitions. 

https://scitechdaily.com/scientists-let-people-play-video-games-using-only-their-thoughts/

https://scitechdaily.com/this-ai-learned-the-laws-of-physics-and-could-accelerate-quantum-computing-breakthroughs/

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

The size matters in cosmological models.

“Two images from the Quijote simulations used in this study. The panels show the same region of the Universe, but in different cosmological models. The top image corresponds. To the standard ΛCDM, adiabatic cold dark matter model, while the bottom image shows a universe with massive neutrinos and modified gravity. “(ScitechDaily, AI Learned the Rules of the Universe and That Became a Problem)

The differences are subtle, but they reveal how changes in the underlying physics can affect the formation and distribution of cosmic structures. Credit: Francisco Villaescusa-Navarro (ScitechDaily, AI Learned the Rules of the Universe and That Became a Problem)

The term ACDM can also mean : the associated critical data model. That is the critical tool, when the sensor. It transmits information to the AI. 

AI can help cosmologists, but it can also become a problem. 

The method researchers call transferable learning can help them develop new models in cosmology and many other things. The term transferable learning. Means when the system learns something. It can apply. That learned thing. To other similar cases. So, when AI sees similar curves in some other cases. It can use things that it has already learned. To that other problem. This means that. The researchers must not always. Begin the training process. From the beginning. 

The AI can search for similarities for the new thing in its memory. And if there is a match. That thing means that the AI. It can use that model for reaction. This should make AI more effective. The problem is this. The AI selects its sources using statistics. And that can make it hard to bring new data for the AI. Old research. They are very often-used sources. If somewhere is the new data. Before, nobody used the new data as a source. Old data dominates search engines. The AI is an excellent tool. When it must collect and analyse data from the galaxy movements. 

But in cases like supermassive neutrons, the AI is in trouble. The AI is the best in business. When it must analyze precise information. Things like galaxy clusters and their movements are precise information. But in cases like supermassive neutrinos. The AI is not very good. At things where it must create models for new physics. When AI must observe phenomena. It can interpret them as the same. Even if they are different. Or in the cases. 

There are some observations. Objects’ temperatures change. The AI might not know that the object’s temperature can change virtually. Because if something travels between the telescope and the object. That means. that the brightness or temperature. That reaches the observer changes. The AI might not notice things like clouds. In the Earth's atmosphere. Or other surprises when it observes some targets like Cepheid variables. If the system doesn’t know about that thing. It can recognize the Cepheid variable as a new star. If it doesn’t know that the star is a Cepheid. 

When AI tries to analyze a certain point. That thing is very hard to do. But when AI must analyze. A very large entirety. The AI becomes more effective. The AI sees things. Like movements of galaxy clusters. And it can make. An analysis of the changes in those movements. We can use fuzzy logic to analyze how the star clusters move in the galaxy. But then we face a problem. If we try to predict. The movement of the galaxy. In its supercluster. That is hard. 

We must know the entire system to make. A complete analysis with high precision. 

The problem is in perspective. The thing that seems large on Earth. Seems very small in the scale of the Sun. And the sun seems very small in the scale of the galaxy. When the scale of the system turns bigger. The forces in the system are also stronger. In big systems. The phenomenon scale is larger. But they affect more slowly. From our perspective. The forces that travel between galaxies take millions of years to reach other galaxies. The distance between the Andromeda galaxy and the Milky Way. It is 2.6 million ly. So light travels 2,6 million years from that galaxy to the Milky Way. And that means that any force traveling between those galaxies needs 2,6 million years for that trip. 

When we try to create a model. Of how one small sand bite behaves in a river. We must know many things. Like changes in the forces that affect the sand bite. But if we want to predict how the sand bottom behaves in the river. We can make that calculation very easily. When we think about galaxies. Stars are like sand bites on the bottom. 

One star’s behavior is hard to predict. But the entirety is quite easy to  calculate. And then we can go to bigger systems. In galactic superclusters, the galaxy is like sandbite on the bottom of the river. The force that affects the entire galaxy. Must be much harder than the force that affects sandbite. But millions of galaxies. They send. A very much. Energy. Many sudden things can happen in the galactic superclusters. Those events might not. Seem.

Like a very sudden thing. But an eruption in the core of the galaxy can start in milliseconds. Shockwave travels across the galaxy at the speed of light. So, if the star is at a distance. Of two light-years from the eruption source. The shockwave of radiation. It travels to that star. So, if Sagittarius A erupts violently in the core of our galaxy, the Milky Way. The radiation travels to Earth 26.000 years. The distance between Earth and that supermassive black hole. It’s 26.000 ly. The material, or plasma shockwaves, travel far behind that radiation shockwave. And the distance between plasma and wave movement increases all the time. 

 But. If things like supermassive black holes are in the trajectory. That makes them collide. That thing is very hard to change. When we face things like galactic superclusters. Things that happen on that scale seem very slow. But forces that put galaxies. To turn their trajectories into travel. At the speed of light. The force. That affects things. Like, turn their trajectories. Must affect a certain time with a certain force. 

If we want to create an AI that analyzes galactic clusters star by star. We cannot make that thing. In the galactic scale, it suddenly happens. Violent eruptions. Those eruptions can break the entire model. In the scale of superclusters, events like supernovas don’t have enough force to affect the macrosystem. But a supernova could destroy things like dwarf galaxies. But if the supernova explosion happens in dense star clusters. That shockwave. Can. Launch other supernova explosions. 

https://scitechdaily.com/ai-learned-the-rules-of-the-universe-and-that-became-a-problem/

https://en.wikipedia.org/wiki/Lambda-CDM_model

https://en.wikipedia.org/wiki/Sagittarius_A*

AI forces us to rethink what “thinking " means.

Sometimes people say. That we cannot ever make an artificial general intelligence, AGI. The argument is this. No human can do everything. So, because there is no general human. There is no AGI. But the problem is this: the AI learns from modules. We can take the tiny artificial intelligence, or limited artificial intelligence, and connect new entirety from them. But. Can be lots of limited AIs. Be the AGI? The tiny AI means. A language model is created for compact devices. Like automated translators. Those systems. They can act as loops. For larger and more versatile AIs.  More about that later in this text. 

When AI handles information with certain rules like mathematical problems, it’s. An ultimate system. When AI handles fuzzy things. If the rules are not so strict. Those systems are in trouble. The AI doesn’t have opinions. They cannot simply tell which color seems better, red or blue.  The AI gives very interesting answers. But as  we might know. It doesn’t have an opinion. It forms answers from statistics. 

Mathematical formulas. And many other things. But it doesn’t have opinions like humans. AI can mimic feelings, but it doesn’t have them. The AI can control robots that  “defend themselves”. Or AI can say “ouch” if we step on those robots’ feet. But. Those things activate only because we pressed some sensors. We could make some of those reactions by using switches that activate tape recorders. 

AI prompts us to rethink intelligence. We might think that knowledge is intelligence. We might think that the ability to connect information from multiple sources is intelligence. And then we face the thing. That even if we can connect all the information in the world, we might not be intelligent. This means that we can process information that we can see. Machines make that thing faster than we. But then, when machines must jump out of the box. And try to make something abstract. 

The AI has imagination. It can use simulations. In simulations. The user. It can use the spacecraft’s digital twins as an example. For docking and other maneuver training. Then the AI knows when and how to use the control rockets. In the automatic model, two AIs can operate in the virtual universe. That virtual universe can be in the third server's memory. This means that two AIs form a loop. And the third AI controls that process. 

The machine will make things better. Or they simply fail. The AI can give wrong answers for many reasons. The first is this. The orders. The user's input can be too fuzzy. The AI can also use the wrong dataset. And this means that the user must be careful about orders. 

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It’s possible that feelings have one purpose. To make our reactions unpredictable. If we did not have feelings. We would make our reactions predictable. If we were to attack like wolves. We would do everything in the same way. Without feelings, we would always attack or escape in similar situations. But feelings make it harder to predict how we react in certain cases. There. Some hunter who is just a puppy. 

Or when hunters threaten our family. The AI always follows logical ways. Of thinking. The AI will never suspect that anybody will tell lies. The trained AI can search the net. Are there things like funerals? For the name of the person who tells about the death of a family member? Or are there some other public ceremonies for that person? But the AI can always say. “Oh my goodness”. And that means the AI can mimic feelings. But it doesn’t have them. 

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The problem with that paradox is that the AI requires well-articulated orders. To make things the user wants. Well-articulated orders are not possible without knowledge of the topics. For using AI. The user must have some kind of knowledge of the topics. Without that knowledge. It's possible that the AI makes mistakes. The mistakes can happen because of the databases that the system uses. Are corrupted. 

Otherwise, we can ask one question. If we give orders to some. Other.A person to make things like painting?  Does that painter ever make a painting that matches our imagination? In the same way, are there mistakes that AI makes sometimes between our ears? Do we just search for mistakes because we want to be better than our creation?

The AI helps us to understand how we think. The human brain involves millions of neurons. Almost all neurons have a pair. And those neurons form the loop. The information travels in the loop. The purpose of pairless neurons is to stop that loop if those neurons are stuck in that process without the ability to stop. The third neuron orders them to stop the process. And give space to another process. 

There is a possibility. That tiny AIs can form those loops in the larger language models. 

Theoretically, those processes. In the level of two or three neurons. They are easy to mimic. But the problem is that. Our brains have so many loops. That they are hard for programmers. We can say that thinking means connecting memories to information. That. The senses transmit to the brain. The fact is that. Something is missing in our puzzle. The thing that is missing makes us intelligent. Computers can connect and process information. But sometimes those things make mistakes. That even a child will not do. 

When we train AI, we require a lot of data. The AI can train another AI. But normally, operators train the machine. But as we know. Normally, it changes all the time. This means that two AI’s can form the loop that allows them to train each other. But in that case. The AI needs information. And information that AI uses can be wrong. 

The AI can teach or train other AI. The system can use the algorithms for the same purposes as human programmers. The algorithms. They are not hard to program. The problem is that the AI requires so many algorithms. And the infrastructure that it needs. Requires many microchips. Of course, engineers can make microchips. Or microchip groups. Those that operate in the three processor groups. The requirement for three processors. Or. Pairless processor core architecture is this. The pairless core releases the endless loops from the  processor pairs. 

But that is one depth of thinking. When we ask something about the AI, we must ask something. From a person who looks at us in the mirror. We must ask: what can we expect from the AI? Should we always expect feelings when we ask something? If we ask something. From the AI. We cannot always expect. That AI connects feelings into its answers. There is not. Feelings that we can naturally connect with all things. That we can ask. If we ask AI to solve a mathematical problem. There are no feelings that we can connect with that thing. 

And AI has no feelings. It can connect some phrases. Like “how horrible” to things that we say. Or tell it. The AI can mimic feelings. But it doesn’t have them. We don’t know what role feelings play in our thinking. There is a theory that feelings are created. To give a surprise element to our reactions. If we have no feelings, that means we would act the same way in all situations. Feelings make our reactions harder to predict. If we were to attack like wolves. We would do everything in the same way. Our prey or other people will predict our reactions. But feelings make those reactions harder to predict. 

https://bigthink.com/philosophy/how-ai-is-quietly-changing-what-we-think-the-human-mind-is/

https://scitechdaily.com/even-gpt-5-failed-this-human-attention-test/

https://www.unite.ai/the-small-model-uprising-why-tiny-ai-is-outperforming-giant-language-models/

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

The key element of drones: flexibility.

As we know the drone. It can transport pizza to the right balcony. But the thing that we don’t normally think about is this. The same drone that delivers pizza at a certain point in the city. It can also transport grenades or bombs to the same point. This is the reason. For why this AI-controlled Skyways drone is so interesting. The drone can transport equipment and food. Into the precisely right position. The drone can make the same thing. With grenades and depth charges. Or it’s pizza can be replaced by using the radar antenna. So those drones can operate as miniature AWACS platforms. 

Those drones with radar systems can see at least larger drones. That flies below them. The radar observation drone can map underground tunnels. A swarm of those drones can communicate. With command, control, and communication platforms. This means that the AWACS platform outsourced its radar to other aircraft.  And that helps the crew to survive if the anti-radiation missile locks on those radars. 

So that makes those drones very versatile. Even if they are standard versions. The bigger drone. It can also operate with other drones. The bigger drone can carry a smaller drone. 

This means that the quadcopter strike drone can be dropped near the target. And those drones. They can use those drones. As aerospace relay satellites. The drone that flies over the area can also send a signal to eavesdropping systems.  To deliver the data that they recorded. Those “sleeping systems”. They can be extremely dangerous. The scanner doesn´t see those passive systems that record speech or data. When the system sends a radio signal, it sends recorded data to that drone. 

Or. It can pull another drone or missile behind it. That kind of system can launch the drone from the right position. And that increases their ability to operate. The pizza drone can also drop a quadcopter into the right position. And those copters can operate as reconnaissance or kamikaze missions. In pure reconnaissance missions, drones can be equipped with microphones or seismic sensors. That can deliver information to the command centers. 

This ability makes drones more versatile than traditional systems. The kamikaze drone can also send reconnaissance data for its operators. The thing about the quadcopters is that. Those drones. They can also transport guns like automatic pistols or small submachine guns. So they can be the most dangerous things in the wrong hands. 

 

New microchips are necessary to boost the AI.

"Scientists have demonstrated a breakthrough method. For building true 3D silicon chips. By stacking multiple layers of circuits. Without damaging existing electronics. The advance could help extend Moore’s law and deliver faster, more efficient computing as traditional chip scaling reaches its limits. Credit: Shutterstock." (ScitechDaily, The Next Computing Revolution May Come From Stacking Chips Like Skyscrapers)

The paradox of computing is this. There is never enough power for those systems. The new high-power computing that revolutionizes mathematics. Forces to develop new systems. That can resist attacks from new systems. The ability to create new protective algorithms. That can resist new complicated malware. Requires high-power computing. The problem is that. The microchips that run those AI-based algorithms. Running with full power. And that means. When new applications come. 

The entire physical system must be changed. The new types of quantum computers are tools. That can break any code. The role of those systems is simple. They can create new, ultra-long quantum prime numbers. The attacking systems require those prime numbers to crack the codes. And when the quantum computers launch an attack. Or assist the attack by generating quantum prime numbers. The only thing that can resist is another quantum computer. 

New AI-driven control systems require new and powerful physical systems. And the thing that can help to solve problems is new quantum computers. The paradox is this. A fully functional quantum computer requires highly effective AI to control it. The operating systems of those quantum computers are very complicated. They require powerful microprocessors. But in mobile systems, the space is limited. 

“Researchers have shown that elusive magnetic excitations can survive far longer than previously thought, opening new possibilities for ultra-compact quantum devices. Credit: Shutterstock” (SvitechDaily,Magnon Breakthrough Could Shrink Quantum Computers to the Size of a Penny)

The answer to that problem is technology, known as vertical integration. In those systems, the microcircuits are like towers. And that makes it possible to increase the number of transistors in those new 3D circuits. The system can keep. The temperature is lowered by using the small air channels. Or a nanotube-based architecture. There, the nanotubes are connected to a heat exchanger. That exhanger. It can be the element. 

Connected to those nanotubes. And the cold gas or liquid travels through those elements. That pumps the temperature. Out of those microchips. These kinds of microchips can control quantum computers in the future. And those heat exchangers can be connected to the cooler systems of the quantum chips. 

The quantum computer. It can be the size of a penny. If those quantum processors turn operational. They can miniaturize. The size of computer centers. The 250-qubit quantum computer matches the 250 regular microchips. That is one of the most effective systems. These kinds of systems can use the superconducting qubits. 

Another version is to use magnons. Anyway, those qubits require. A very stable and low temperature. The small quantum computer requires high-power coolers. So, theoretically. It's possible to keep its temperature low. By putting it into the  thermos box. There, the system keeps the temperature at a low level. The system can also use pressure. To raise the temperature that the superconductor requires. 

Magnon it is. “A magnon is a quasiparticle, a collective excitation of the spin structure of an electron in a crystal lattice. In the equivalent wave picture of quantum mechanics, a magnon can be viewed as a quantized spin wave. Magnons carry a fixed amount of energy and lattice momentum, and are spin-1, indicating they obey boson behavior.” (Wikipedia, Magnon)

“A lifetime of 18 microseconds could turn magnons from weak intermediate links into strong quantum memories and efficient communication channels on a chip. They may be able to connect hundreds of qubits through a shared pathway, serving as a long-awaited quantum bus for scalable quantum computers.” (ScitechDaily, Magnon Breakthrough Could Shrink Quantum Computers to the Size of a Penny)

“The new MultiQ-IT prototype can cool, trap, filter, and redirect over a billion ions simultaneously, dramatically improving dynamic range and signal-to-noise. Credit: Lori Chertoff/The Rockefeller University” (ScitechDaily, Magnon Breakthrough Could Shrink Quantum Computers to the Size of a Penny)

In some very exciting models. The quantum chip can involve a laser network. The points where the laser beams interact can act as quantum dots. And superpositions could be made through or between those quantum dots. Those systems are exciting. But they are a little bit. Too complicated. For the existing modern technology. The ability to miniaturize the quantum computers is exciting. 

When the system requires the ability to control qubits. It must see them.  This is the reason. For why the new sensor is interesting. The ability to see the points of atoms and molecules makes it possible to control them. The ability to see atoms and particles. And the ability to inject energy into them. Makes it possible to use atoms as qubits. 

In the same way. Those sensors. They can observe the qubits. These are created between quantum dots. The mass spectrometers. They can see which. Of those atom groups are stressed. Mass spectrometers can also transform the atoms into information. They can observe atoms. And a certain atom group. It can be a certain qubit or a state of the qubit. 

https://scitechdaily.com/magnon-breakthrough-could-shrink-quantum-computers-to-the-size-of-a-penny/

https://scitechdaily.com/mass-spectrometry-breakthrough-detects-billions-of-molecules-at-once/

https://scitechdaily.com/the-next-computing-revolution-may-come-from-stacking-chips-like-skyscrapers/

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