Can Sci-fi weapons: nanomachines and sophons be a reality someday?

 

The grey fog is one of the superweapons that are so horrifying that we cannot even imagine them. That grey fog can erase entire planets. Nanomachines are the new tools. They can be the ultimate Swiss blade for everything. Theoretically, nanomachines can erase any molecule that they face. Those small molecular machines must only create the wave movement and resonance that cut the chemical bonds between atoms. The miniature machine can simply send an electromagnetic impulse to the chemical bond in a targeted molecule. And that energy can push atoms away from each other. This kind of system can have multiple civil and military applications. 

The nanomachine that can terminate forever molecules can be the most wanted thing in the world. But that same technology is also capable of creating the terrifying “grey fog” that terminates everything that we know. The main problem with nanomachines is their movement. The surface active agents, or surfactants are the things that can solve the nanomachine movement problem. If the nanomachine has two surfactant molecules that the system can turn on when it gets a command. That makes it possible to move the nanomachine. Surfactants have two heads, one is hydrophobic and one is hydrophilic.

If the hydrophobic head is in the direction where nanomachines should move and the hydrophilic head is at the tail of the nanomachine. That makes the nanomachine move in the desired direction underwater. The hydrophobic head that can be connected with water droplets can also make the nanomachine hover and travel to wanted direction in the air. When the water droplet surrounds the nanomachine and then the hydrophobic- or water-repelling heads are turned to that water. That thing can cause an explosion. And the pressure wave can help to raise the machine up. The small nanomachine that can control that thing can make it possible to use that thing for controlled flight. 

The other version is that they use some. more exotic propulsion systems, like theoretical systems that can change the shape of the quantum fields near the nanomachines. Those systems can make the machine hover and travel at very high speeds. 

Those nanomachines can be connected with the von Neumann probes. The term Von Neumann probe means self-replicating machines.  Those systems can include miniature factories that create copies of those machines. The nanofactory can be very small. And they can create copies of themselves and create those molecular machines. Those machines and factories can be DNA-controlled. 

The sophon is introduced in the sci-fi novel 3-Body Problem. The sophon is a proton-sized quantum computer that can control humans and steal their imaginations and thoughts. The model of sophon is in the real quantum models where the proton, or quarks that form this hadron will be put into the superposition and entanglement. This kind of quantum computer is very unstable. There are models made of what those sophons can be. And one of them is that the sophon could be the group of photons that are trapped around the quantum-size black hole. The other version could be the quantum-size grey hole. 

The system creates those things by pressing some particles like protons with antimatter implosion. The ball-shaped antimatter-matter ball will be exploded around the proton. That thing can be the fullerene that acts like an implosion bomb. And then photons will be put around that extremely dense object. And the system will transport data into them. 

But there is another way to make the theoretical sophons. That is the DNA-based quantum computer. The system can be an artificial bacteria or an artificial amoeba that is injected into the target’s blood. There, genetically engineered amoebae can travel to the human brain. Then that thing will steal the electric impulses or make copies of the neurotransmitters in that thing. 

When the artificial amoeba or biorobot is ready it calls the genetically engineered mosquito to pull it out from the blood vessels. 

The artificial mosquito can use certain chemical marks, antibodies to call the artificial amoeba to it. And that amoeba can also send neurotransmitters to neurons around it. The system mimics the natural parasites. But their purpose is different. Their mission is to paralyze and steal information from the targeted person's nervous system and even control that person. 

Then that mosquito travels to the laboratory. And there are many ways that that thing can transmit data to the computer. The mosquito can split that amoeba on the research table. Then the amoeba starts to blink the bioluminescence light and using that light the biorobot can transmit information that it got to a photovoltaic cell. The amoeba can also reprogram the mosquito and make it communicate with computers. The artificial amoeba-mosquito couple can be the ultimate tool for intelligence and other systems. 

The AI that beats humans is at the door.

Mark Zuckerberg says that he wants to create an AI that is more intelligent than humans. The AI can have better cognitive skills than humans because they learn differently. Every skill that the AI has is like a macro in its memory. There is no limit for the number of those macros, or automatized actions that the computer stores into its memories. The limit is the memory storage. The AI will not forget humans. That makes it possible for the same robot can cook. 

Clean and make almost limitless numbers of operations without errors. If we want to make the AI that makes food for us we must create a huge number of variables for that thing. But there can be a shortcut to that problem. The AI can involve certain modules. So, if the user wants meatballs that AI downloads the meatball algorithm and databases to the robot. That makes it possible to make the system operations lighter. The databases or datasets can be created separately. 

Cognitive AI means that it can create a dataset independently. And for computers, each dataset is a certain skill that it has. 

The AI is the man-created alien. Are aliens already here? The fact is that if Mark Zuckerberg wants to build AI that is more intelligent than humans that thing is an alien. Human-made aliens are things like genetically engineered species and artificial intelligence. And then we can ask is artificial intelligence really intelligent? Can it think? The AI can do many things. It can advance its skills and it can learn from other AIs and from films. Turing’s test is the thing that measures the AI’s ability to think. 

The AI can mimic humans. It can transfer all movements that humans make to the human-shaped robot. That thing is the thing that makes the system seem intelligent. The cognitive skills that AI has made it possible to create learning systems that can control robots on the ground following certain parameters. When a robot fails in its mission the system also knows what it should not do next time. The physical robots are good subjects for modeling the cognitive systems. 

The AI can learn autonomously by using the same methods as humans. If it fails some mission that means there is an error. The cognitive system learns by using a method there failure means that the system must not try that thing again. Learning by mistakes is easy to explain by using a model where the AI controls a robot group. There are let’s say 5 paths that the robots can use for traveling from point A to point B. That AI sends a robot to make its mission. When a robot fails like falling into a canyon the system learns what it should not do with the next robot. 

The system creates the model of the landscape and then it creates the model of the path that the AI selects for the robot. When a robot succeeds in its mission the AI stores the data about the environment for the next time use. The system can also store the data about failures so that it knows what it should not do. Failures are also important for developers. The robot makers need knowledge about what caused their product failure. 

The robot should know how steep the slope the robot can rise. When we talk about robot success and things that the robot should not do, we must realize that the robots cooperate. The human-shaped robots can cooperate with flying quadcopters that send data about the landscape and other things that those robots require. 

But then we can think about AI as a mathematician. The system must also recognize the mission that it has. When the AI recognizes the mathematical formula, it can connect the data that it collected to that formula. The problem is this. If the mission is not well-explained AI will not simply understand that work. The AI must dare to say that thing. If the mission is not clear the AI must not try to make anything. The main problem with learning systems is this. They simply connect a new subprogram or macro in them. And that makes them look very intelligent. But the main question is: can that system think? 

For computers, every skill is a database or dataset. A learning system is described as a system that can get new skills and then link those skills with other skills. Or, otherwise, we can say that the self-learning system can create new datasets and link those datasets with other datasets. 

It can connect data and data frames into one entirety. But the fact is this. The AI simply mimics subjects. It seems that the subject makes something, and then the AI makes the same thing if it faces a situation that matches that case. But we humans also learn from mimicry. When we see that the teacher makes something at the front of the classroom we can mimic that thing. 

When we learn something new with teachers we simply mimic things that the teacher makes. And then we store that data model in our memory for the next time use it. That is the rigid model. The rigid model includes basics for some computer skills. And then we must simply connect that model with other things. This ability to interconnect that new model with other things makes it flexible. The model turns into a thing that is like an amoeba. 

The system can connect that new model to many other skills. When we talk about things like image processing programs, we can also connect skills that this program requires with things like writing skills. The fact is this: the AI must not do everything that the user wants. It must have the possibility to refuse to follow orders if the user wants to use it for criminal activities. The other thing is that the AI must have certain orders for what it must do. The AI must have the ability to use virtual models on the screens that it really makes when somebody gives certain orders. 

When we think about cases in which the robot acts as a mover there are some human-shaped mannequin statues that can cause a bad situation. If the mannequin statues are not well described to robots, that system can also transport humans to the lorry. In those cases, the AI must know all the details about their subjects. They must know that the mannequin statues are plastic and other details. 

The new form of living is the “new village”.

In medieval times the city walls separated people who lived in the city from people, who lived outside the wall. There lived people who ever stepped out of the city. In that time people told stories that in the forests lived monsters who ate people. Sometimes mentally ill people are banished to the forests. But those walls created a feeling that the world outside the walls was hostile. That increased the city leader’s authority. When people believed that there were evil spirits in the forests around them, that thing made them easier to control. And the question is: are we returning to those kinds of cities? 

What if our future is that we would live our entire life in the same building? Things like artificial intelligence and virtual reality make it possible to make virtual trips to lands that are far away from the place where we live. We can simply open a solarium, take a VR system to our eyes, and then the AI-controlled system connects winds, sounds, and other things that we need to the space. The AI observes that we will not take too much radiation. 

The Saudi-Arabian mega project called “Neom”. That thing will be the most incredible megastructure in the world. That kind of thing brings the new types of village societies into the front of our eyes. The idea is that the system brings homes, all services, and workplaces under one dome. And in some visions, cities like New York will be covered with giant domes that should make the air comfortable every month. That thing brings the route to the ultimate segregation into the front of our eyes. That thing can look like a village society with idyllic things, like the ability to keep the T-shirt on every day. 

What happens if we ever leave that dome? Can that dome feel good? The fact is this: the dome turns into an entirety where we can live. We will never feel fresh air. All physical works are made with robots. And maybe we see the future as a thing where there are giant forests and there are giant domes there and here. That future is the thing that takes us to heaven and hellfire. The AI-controlled structure allows us to control each other. The place where we would live is safe. 

But there is also another side in that idyllic structure. That structure can turn into a prison. What if our leaders will use that thing against us? The dome allows people to control people simply by using the chemicals that are in the air. Or the leaders can use the air pumps to control air pressure. And as always: there is a chance to use that kind of system to steal people's lives and entirety. This dome can turn into the thing that brings the highest walls between people that have ever been made in history. But that kind of thing offers solutions that can also save nature. The city can use green energy as an example of energy production. 

The new open-source robot is a tool for everyone.

The open source opens a path to open applications. In open applications, the physical tool is the platform that can do “everything and more” as humans. The open application means that the robot itself is a platform that can be equipped with the tools and programs that determine its purpose and work. The man-shaped robot is the tool that can make “all things” that humans can do. The robot body can be remotely controlled, or an independently operating system. 

Macro learning where a robot learns through modules is the tool that makes the robot’s limited computer capacity more effective. The operator uses a system that records the things. That the robot must do in certain situations. When the robot makes something for the first time, the operator creates a macro. And then if there are similar situations the robot can launch the macro independently or ask the controller to make that thing. 

The idea is taken from the text editors and spreadsheets. There is a possibility to record some actions that are used commonly. The macro programming for robots follows the same principles. The thing that makes man-shaped robots very good tools is that they can act as builders, cab, and bus drivers, fighter pilots, firemen and make all dangerous missions. The same robot can change its role in less than a second. The things that separate fireman-robots from bus-driving robots and military operating robots are skills or datasets that the system can use. The operator must only change the dataset for the robot. 

And then that system finds a new role. The datasets or skills are collections of the macros. Those macros are activated when there is a thing that matches with descriptions. This means that when the fighter pilot robot operates things like alarm signals activate certain macros. The open source robots that act as cleaners are a good idea. But people don’t always remember that changing the program makes those robots the tools that can operate as commandos. 

When researchers create robots that they can teach, we sometimes forget one thing. That is, those robots can operate as networks. When somebody teaches or creates a macro for one robot, that robot can spread that macro over the entire network. And here is the problem with the “machine rebellion”. Machines will not rebel. This is the key element in robotics. 

But should we somehow transform that argument? We should say that machines will not rebel autonomously. So, we must not worry about the machine rebellion, but we must be worried about human-controlled machine rebellion. We can imagine a situation where somebody simply buys let’s say million housekeeping robots. Then that person will simply change those robot’s programs. And then that system is ready for combat. 

Robots can be dangerous to humans for two reasons: 

1) They are made to be dangerous. That means that things like combat and security robots can be dangerous. 

2) Robots can turn dangerous if there are some errors in programming. 

All errors that machines and especially computers make are made by programmers. The computer will not be automatically dangerous. Same way robots might not be dangerous if they operate as they should. The problem is that when robots are not programmed with certain accuracy, that makes them dangerous. In the cases where robots refuse to stop their actions, they might turn dangerous. 

There is a possibility that in the case of fire, the robot who works as a house guard denies the firemen's operation. The reason for that can be that these kinds of emergency situations are not determined in their program. So, when firemen come in, the robot can think that they are intruders. The other case can be that the law-enforcement robot has no descriptions of things like umbrellas. That robot can think that those things are weapons. 

In another scenario. Programmers forget to determine green T-shirts. or green balloons for the car’s autopilot programs. That thing can cause an error if the autopilot determines a green balloon as a green traffic light. And that causes a destructive situation. 

In some models, the other civilization can cause the end of some other civilization by accident. The system encoders simply forget to make the breaking protocol to the computer. And then that probe comes to the star system. The AI simply forgets to slow down and then the probe will impact the planet with a speed of about 20% of the speed of light. 

That causes the model that the most dangerous thing in the universe is the type of early Kardashev 2. Or late Kardashev class 1 civilization that sends first probes to another solar system. 

That civilization will not handle that technology yet. Without wormholes, it takes years or centuries to get information from that spacecraft. And if there are some errors in programming that spacecraft can impact the planet. The theoretical minimum weight of that probe is about 10000 tons and if it impacts the planet there is not much left. 

https://www.rudebaguette.com/en/2025/06/humanoid-bots-for-everyone-new-open-source-robot-unveiled-in-the-u-s-makes-advanced-robotics-affordable-for-total-beginners/

Why would AI kill humans rather than let them shut down the server?

 

Why would AI kill humans rather than let them shut down the server?

These kinds of situations are very bad. But the problem is in the program code. When we talk about AI and its ability to kill humans we must realize something. We must realize that the AI will not understand what those things actually mean. If we think about those things like a programmer, we might understand that situation better. When we write programs we must determine a variable in the code. The “human” is one of those variables. In traditional programming when something matches a variable, that thing runs the subprogram or macro. There are descriptions of things that launch a certain macro. The variable actually activates the pointer that begins the sub-program. 

Or, otherwise, it calls the sub-program. In traditional programming the thing goes like this: When the user writes the word “Goofy” there is a code that activates the Goofy. In programming that orders the program to jump to a point, where is the macro where the pointer “Goofy” points. In AI programming those variables and pointers are more complicated to describe. 

That means that if the “human” is not well described to a system or algorithm that system can even kill a human. When computer operators work with servers and other things in computer halls. They must sometimes shut the server down. In those cases, the data will be copied to the swap system that guarantees the service without stops. The problem is this: if the system lets anybody shut it down that allows vandalism. 

The system might have orders to deny or stop the malicious action. The system requires precise orders about things. Like when it must or should stop the action. 

If the system has an order to stop that kind of action there is a possibility that the system simply kills the actor. When we think about cases like machine rebellions or the situations where computers turn against humans like in 2001 Space Odyssey those situations can happen. Because of the programming error. In that movie, the HAL-9000 computer kills almost the entire crew of the spaceship. Can this happen in real life? The answer is in programming. If the computer has no description of the humans and it has the order to remove malfunctioned systems from the spacecraft, that thing can cause destructive cases. 

There is also the possibility that the AI recognizes humans using cameras and IR systems. When humans put space suits on, it causes a situation where the AI will not see human faces because of the black mask. And the other thing is that the space suit does not let infrared radiation go through it. That means the system can “think” that the astronaut, who uses a space suit, is a robot. If an astronaut makes some mistake that causes a situation where the system translates the space-suited human as a robot. Then the system tries to remove those malfunctioning robots. 

When the astronaut cannot catch the tool the system will try to remove the astronaut that it thinks of as a robot. If the robot cannot catch the tool and the computer removes it seems like an overreaction. The reason for that overreaction is that there are no descriptions of the cases where the robot makes such big mistakes that it must be removed. If those things are not described every mistake that robot makes causes the removement. If a robot drops one screw to the floor and the programmer describes that “mistakes cause removement” that means the system translates even the smallest mistakes to cases, and their robot must be removed. If those cases are not described every mistake causes the removement. The system will not automatically make a difference between small and big mistakes. If the only thing is a mistake, the system removes the robot even if it drops the cup from the table. 

Mathematics, geometry, and quantum.

In the image in this text is an image of lupine and image of a quantum experiment there is tested Landrauer's principle. “Landauer's principle is a physical principle pertaining to a lower theoretical limit of energy consumption of computation. It holds that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat to its surroundings. It is hypothesized that energy consumption below this lower bound would require the development of reversible computing. The principle was first proposed by Rolf Landauer in 1961.” (Wikipedia, Landauer's principle)

Both the flower and those quantum fields form the tower. And the remarkable thing is that those quantum fields form a similar structure as a series of coils that send radiation from their sides. That kind of quantum tower can send information to the receiving coils or layers if they are against each other. Same way a radio antenna transmits information from the points where the Hall effect forms the plate-shaped expansion into the electromagnetic (quantum) field between atoms. 

"Quantum magnetometers are breaking barriers in magnetic sensing — but are they really quantum? A new study digs into how far these devices can go and what defines their quantum nature. Credit: SciTechDaily.com"(ScitechDaily, Quantum Sensors That Hear Magnetic Whispers – And Push Physics to Its Limit). Those sensors could form a tower that can scan quite a large area. 

Quantum magnetometers can detect incredibly small changes in magnetic fields by tapping into the strange and powerful features of quantum physics. These devices rely on the discrete nature and coherence of quantum particles—behaviors that give them a major edge over classical sensors. But how far can their sensitivity go? And what actually makes a magnetometer “quantum?” (ScitechDaily, Quantum Sensors That Hear Magnetic Whispers – And Push Physics to Its Limit)

Those fields form when an electromagnetic wave travels between atoms.  When that wave hits an atom's quantum field it causes a wave. That wave or resistance makes it possible that the system can press information into the sides of the antenna.  When we think about those Hall fields and those flowers, we can imagine a situation where those flowers could send chemical signals from their flowers to another flower. That thing is not proven. But the lupine flowers can act as models for directed radio transmitters that send coherent radio signals to the receiver. 

The second image introduces the model of the quantum fields around quantum sensors. Those quantum fields allow those sensors to sense things that were unable to detect before. When we think about things like quantum computers, erasing information is also important. If we can trap wave movement into the bubble, we can erase that information by pressing wave movement into a straight position. 

We can see that the same forms repeat in nature. The image from the Landrauer’s principle has a similar form with flowering plants. And that causes an interesting question. Can we someday calculate things like quantum fields' form in situations where some high-power energy impulse hits them. If we think that the quantum tower is similar in all sizes of quantum systems, we can make the new types of quantum systems that are more sensitive than ever before.  

There is a possibility that the quantum sensor looks like the quantum tower where the electrons or photons hover between objects and those quantum fields. When we make superposition and entanglement we must know everything from the system. We must predict things like FRBs and other changes in the power of electromagnetic fields. 

https://phys.org/news/2025-06-approach-probing-landauer-principle-quantum.html

https://scitechdaily.com/quantum-sensors-that-hear-magnetic-whispers-and-push-physics-to-its-limit/

https://scitechdaily.com/the-quantum-price-of-forgetting-scientists-finally-measure-the-energy-cost-of-deleting-information/

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

The AI learns like a child.

Why did the old-timer ATARI chess console beat Chat-GPT? Or why that old-fashioned Chess console could beat humans in chess? The reason for that is the same as in cases where our robot reapers will always get stuck when it works. If we ever play against those antique game consoles we don’t win them. The old-fashioned ATARI involves a couple of mechanic games. But we cannot predict how it moves its buttons if we don’t play against those consoles. That means we win those consoles because we learn how that console plays its game. Those consoles use traditional linear computer programs. If some button is hit the system removes code lines that were meant for that button. 

The old-fashioned ATARI shows that AI requires similar learning methods as humans. So why are our robot reapers unable to do their job? When we program those systems we must stop thinking like programmers who use linear, symbolic programming languages. We should take control of that reaper, and drive the area by pushing that system through the grass area. The system must have navigation tools that help the system to determine its place in the yard. Those navigation tools can be three or four radio lighthouses that help the robot determine its position without the GPS. 

The system can also have a GPS that helps to locate the robot if somebody steals it. When the owner pushes the first mows that helps the robot’s system to determine how much energy it needs. That helps it to plan the battery reload position. The system also needs information about the escarpments and potholes. Those things might be easy for humans or big robots. But for small robots, those things can cause trouble. When we teach AI, we must remember that there are many variables that don’t mean anything to us. But those things are very important for robots who must make complicated things. 

Many complicated things like working in cramped places are automatized in our bodies. But if we want to make a robot plumber we must program every movement that plumbers make doing jobs. That thing requires new programming tools like AI-based systems that can follow the plumber while working. Then that system must copy those movements to the robot’s body. This is one thing that requires advancements. Traditional programming tools are not suitable if we want to describe multiple actions to robots. 

https://www.rudebaguette.com/en/2025/06/chatgpt-just-got-wrecked-by-a-1977-atari-vintage-console-destroys-modern-ai-in-the-most-ridiculous-chess-match-ever/