AI and artificial life.

 

AI can modify life itself, and artificial cells can also modify AI. When we think about the DNA as data storage, we must realize that the problems are how to create synthetic DNA that involves only the things that researchers want. The other thing is how to transport. That data is sent to the microchips so that the computer can read that data. The answer can be electric impulses that the computer can read. AI is the tool that can read DNA better than play chess. The DNA is a linear data storage. And that makes the AI able to read it using microscopes and spectrometers. 

The AI can search data that is stored in CRISPR datasets. And search for similarities in the DNA that is taken from people or animals that have certain abilities. The AI can make virtual cells and simulations. About what kinds of things certain base pairs at certain points in DNA make. The AI can also connect and search data across species borders. And that allows developers to connect chlorophyll genomes to skin cells. That forms the green man. 

The same nanoparticles that can transport medicines to certain receptors can transfer the mRNA molecule in the same way in targeted cells. 

The AI can connect that data with datasets about the advancement of the fetus. Nanotechnology, along with advanced tools, makes it possible for the AI-controlled system to cut DNA and connect the new bits of DNA into those holes. The other way is to create artificial mRNA whose mission is to control the cell organelles. The system must just create the mRNA molecule that controls the cell organelle, and then that cell can create anything that the mRNA encodes it to create. 

The pathogen that can basically transform species into new ones can be based on the mRNA viruses or packages. Those mRNA molecules order the cell to create copies of itself. And then finally transform the cell into another. The mRNA is the tool that can order the mitochondria to decay in the muscle cells and that increases those cells' power. The artificial cells can also create neurotransmitters and electric impulses that allow the system to transfer memories into the nervous system. And basically, all our skills are based on memories. That is one of the things that we should know when we create new artificial species: are they bacteria or more complicated species? 

Natural bacteria cannot communicate or transmit data to the nervous system. But artificial cells can do that. And when we think about the ability to live forever or fix large-scale injuries, we must create neurons and then transmit lost data to those neurons. The single neuron involves only 1-5 bits of data. Thoughts and memories are formed in connections and states of those neurons, which humans have over 86 million, but our brains can connect those neurons into virtual neurons. 

And the connections between neurons are as important as the number of physical neurons. When a neuron is lost in an accident, the data that is involved is lost. The artificial cell can transport memories back into those cells. But that requires that the lost data is stored somewhere. And the second thing is that the system must have the ability to create artificial DNA that involves data that is normally stored in the brain. 

Things like intelligent tattoos that involve small-sized nanotechnical microchips or neurological microchips that are implanted into people’s brains can make it possible to return those memories. If the neuroimplanted microchips can transport data from memory centers to hard disks, it makes it possible to return those memories using artificial cells. That thing can restore the abilities of the badly damaged people. The brain-implanted microchips can also make it possible to read people’s minds. And that thing can turn humans more than we are today. 

https://www.freethink.com/artificial-intelligence/virtual-cells

https://pmc.ncbi.nlm.nih.gov/articles/PMC8539479/

https://www.quantamagazine.org/rna-is-the-cells-emergency-alert-system-20250714/

https://www.quantamagazine.org/what-can-a-cell-remember-20250730/

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

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

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

The new method, known as distillation, enhances the effectiveness and reduces the cost of running AI.

In chemistry, distillation means a technique that purifies a material. The same way chemists distillate liquids, AI researchers can distillate AI. Distillation in the human body means that when we move our hands, we don’t need to move our feet at the same time. Or when we order pizza, we don’t want the entire list. We want that certain pizza. In AI, that means that the AI can create a student model that it trains for customers' needs. 

The large language model (LLM) can create a small language model (SML) and customize it. That means the LLM removes all unnecessary things from the SML to make it compact and more secure. The SML is easier to test and it requires less powerful servers than the LLM. There are always mistakes and errors in the LLM algorithms. The problem is that the corrupted AI is not a good tool for detecting errors in its internal code. The human coder must recognize the suspected errors and then fix them. But the problem is that the code can be right, but its target is the wrong object. 

The idea is that the system cleans information in the system. That means that the AI has only responses and actions that it needs for complete missions. The system takes all unnecessary parts away. And that sometimes causes questions about the information that the AI will not need. Humans make decisions about information that the AI needs. And that is seen in things like Chinese AI. Those things don’t discuss things like Tiananmen Square. 

That is one version of distilled information. The system will not give answers that are against the state policy. AI is a tool that can make many things better than humans. But those things will happen in well-limited sectors. The AI can observe things like nuclear reactor functionality. The fact is that the nuclear reactor is not like a chess game. The AI must only keep values at a certain level. The thing in the AI is that it can generate code, but it can use only existing datasets. The difference between a nuclear reactor and a chess game is that the nuclear reactor will always follow certain rules. 

The nuclear reactor will not make anything unpredictable. If the AI knows all its values, the nuclear reactor is safe. But unpredictable values like leaks in the cooling system can destroy a reactor. The programmer who creates the nuclear reactor control systems. That creator must be very professional and collect all data so that the system can respond to all situations. The system must collect information from many sources, such as surveillance cameras and other tools. The system must recognize if some light doesn’t shine as it should. 

That kind of system requires very high-level skills and the ability to train the system for new things. There is always a possibility that the programmer, or the engineer who advises programmers, doesn't always remember everything, such as details of some kinds of damage. That means the AI requires training for that mission. And like always, this kind of thing means that all mistakes that AI makes are actually made by humans. Humans should test and accept that kind of system. And that causes dangerous situations. The training is the final touch in the AI R&D process. 

When we think about things like the North Korean government, they want to use AI in the same missions as Western actors. But do those actors have the skills and abilities to make the final training for their language models? If those language models are made by using some kind of pirated copies that are transported using USB sticks, it can make it possible that the AI and its complicated algorithms don’t work as they should. And that makes those systems dangerous. 

https://www.quantamagazine.org/how-distillation-makes-ai-models-smaller-and-cheaper-20250718/

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. 

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/

The new Python profiler increases the power of Python very much.

 The new Python profiler increases the power of Python very much. 

The Python language is an effective but slow programming tool. Python is a very popular tool in network programming. But because it's slow it is problematic in complicated solutions that run on network-based decentralized platforms. Things like AI-based Bing and Chat GPT are making programming very easy, and they can create very complicated solutions in any public and open programming language. However, those programming tools do not remove the limits of the programming language itself. 

In complex and complicated solutions the program is divided into cells. Each of those cells involves different types of information. And each of those cells is responsible for certain reactions. Another reason for cell-based programming is simple. If one of those cells is corrupt that minimizes damage. 

The problem with AI is how it can select the right cell. If AI selects a cell from an interface that controls the physical machines that thing can cause catastrophe if the selected cell is wrong. That means there must be something. That simulates the reaction before the AI makes anything. This means the AI should have layers written with different programming languages. That it can pre-process information before reactions. 

"Researchers from the University of Massachusetts Amherst introduced Scalene, a cutting-edge Python profiler. Unlike traditional profilers, Scalene uses AI to both identify and suggest fixes for code inefficiencies. This development gains significance as the future leans towards better programming for speed improvements". (ScitechDaily.com/Turbocharged Python: AI Accelerates Computing Speed by Thousands of Times)

All of those cells handle one type of situation. And if the system must react fast, that means it needs multi-level programming architecture. The AI-based applications involve extremely complicated and large code structures with even millions or billions of databases and database connections. 

If the AI must go through all that code it takes too long. But if the AI has some kind of profiler that profiles the situations that the AI sees. That helps it to select the right cells from its structure. The faster structure below the Python level makes it possible to pre-select cells. The difference between AI solutions and regular computer programs is that AI is a non-linear tool. It requires the possibility to jump back and forth in the code. 

The faster C++ core under Python makes profiles about situations. That sensor gives it. Then that C++ layer selects the right Python cell. The C++ layer simulates the sensorimotor cortex. And Python layer is like the cerebral cortex. 

So there is a possibility that Python requires some other interface that runs below Python code. That other interface might written by using some other programming language like C++. That is faster than Python. The idea is that the inner code analyzes reactions that the Python interface makes in certain situations. The C++ interface under Python can jump into Python code and pre-select the program modules suitable for a certain situation. 

That kind of faster structure below the Python interface makes the structure look like a brain. The Python structure is like a cerebral cortex, and the C++ layer is like sub-consciousness. The faster C++ can also act as a reflex layer that reacts to fast situations. The Python layer can give observations into that C++ layer, that profiles the situation. And then that faster layer will select the right cell from the Python layer. 

AI and quantum computers will revolutionize ICT.

MIT researchers made a new programming language for quantum computers. And that thing is a big step. For bringing those systems out from laboratories to the supermarkets. The new programming language opens fundamental possibilities for quantum computers. And that thing allows collecting of more programmers around those quantum computer processes. 

There is the possibility to make translator programs for well-known programming languages like Java, C++, Python, and many others. But translator programs along with highly advanced artificial intelligence. Makes it possible to use spoken language for making computer programs. In those cases, the programming tool follows commands like "draw circle which diameter is xx" or "make the form where are following fields and connect it to the database which name is XX". 

That thing makes the programmer's work easier than ever before. The thing is that artificial intelligence can be the next-generation tool for making web services. In that case, the programmer must just give the values that the web service must contain. And there is the possibility to use some other web service as a model. The AI can search the data from the Internet. And then it can make the necessary things like needed connections with the existing database. 

Or the system can ask things like flow chart as the model of the structures what the database should involve. The thing is that the AI can ask the feedback. And in that feedback, the users can tell what they want more. What they feel good, and especially what must be changed. That thing means. That interactive AI can make many things that were difficult before. 

There are already tools on the Internet like Google forms that are making it possible that every person can make things that required highly trained programmers before. That means everybody can make their polls on the Internet without any special training. And that causes the programmers are turning unnecessary. 

Quantum computers and web-based programming applications are making the internet safer. It is impossible to make malicious code by using web applications. If the user makes malicious code. That thing means that the AI detects that attempt. And then, the AI tracks the IP address where that traffic comes. 

The thing is that the quantum computer is the ultimate tool in data security. There is the possibility that the quantum system marks every single mark written on the platform that runs on it. By using a certain code. That code is the thing that identifies that the code is made by using authorized systems. That means the firewalls could block every mark. That is not checked by using web-based applications. 

That thing denies writing computer viruses. But as I am written before. There are new threats like social hacking where is the virtual actors. Or there is the possibility that the person whose log-in rights wanted to use would just inject by using sodium Amytal or tetrodotoxin. That thing makes those people tell their access codes. And that kind of thing can be a risk for national security. 

https://scitechdaily.com/twist-mits-new-programming-language-for-quantum-computing/

https://thoughtsaboutsuperpositions.blogspot.com/

Do you trust AI?

   

At the beginning of this text, I must say that AI is a computer program. Computer programs are like machines. They cannot handle every problem on Earth. They meant to use for some certain purpose. And if we want to use some AI algorithm outside its operational sector, that thing causes catastrophe. The world is full of algorithms. 

Some algorithms are meant to use for things like collecting marketing information from limited systems. The other AI systems are meant to control physical robots. So if we want to use AI for something. We must make sure that the program is meant for that purpose. 

We must realize that if we want to use the marketing analysis programs for controlling robots that thing would cause disaster. If we want to improve the skills of AI. That requires more complicated code than the AI that has only one skill. Every single skill that the AI has must be programmed to that thing. Machine learning is making independently learning machines possible. 

There are three types of learning machines. 

1) Semi-automatic learning systems. 

Whenever the system faces a new problem it calls the operator.  The operator makes the solution and stores that thing in the memory of computers. 

X) Independently learning machines. 

Those machines can create the databases automatically. And then those systems can automatically connect the database to a certain action series. 

2) Hybride systems

Those systems can make the solution or connections between databases automatically. But if that system cannot find the database that fits the problem it can ask for assistance from the operators. That kind of system can respond to multiple problems. 

Hybrid systems are close to the human way to learn things. If the system would not find a match for the case. It would not know how to respond to the case that it faces. In that case. The system will ask for help in solving the problem from the human operators. 

Whenever the system gets the new answer for problems. That thing increases the data mass that the system can create more connections. And it turns more independent. 

When the system is creating the solution. Or the controller solves a problem that solution stored in the memory of the artificial intelligence. for similar cases. That thing increases the number of skills of the AI. 

There are two types of AI

1) Passive AI. 

That system just collects data and analyzes it. 

2) Active AI.

That system interacts with the real world. The system collects data from the sensors. Then it analyzes that data. And then it sends signals to the communication tool. That tool might be the traffic lights if the AI controls traffic. 

The thing is that AI is not a stand-alone operating tool. The system requires tools like an internet connection or a physical robot for making things. If the AI is interacting with the real world. 

It requires sensors and is connected to the sensors that it can get the data mass that it processes. But the AI needs the tool how to interact with the real world. If it controls things like traffic it needs a connection to the traffic lights. Without that connection, AI does not affect the real world. 

All artificial intelligence programs or algorithms are meant to operate in certain sectors. 

The thing in artificial intelligence is that it doesn't make mistakes. If we are saying that the AI makes mistakes. We can same way say that some regular programs like text-handling tools make mistakes. Every mistake that the AI makes is encoded in its code. 

Another way that causes mistakes for the AI is that the data that the AI handles is somehow disturbed. If the sensor that sends data to AI is corrupted accidentally or in purpose. That means the data flow to the system is not relevant. 

The corruption of the sensor means as an example,  that the camera might be dirty.  So that means the system would not get real information. When we are thinking about the trust of AI, we must realize that we must check every single part of the system. The code itself must be completed and tested. But cable connections and the function of the sensors are the same way the important things. 

https://scitechdaily.com/measuring-trust-in-artificial-intelligence-ai/

Image: https://scitechdaily.com/measuring-trust-in-artificial-intelligence-ai/

https://thoughtsaboutsuperpositions.blogspot.com/

Deep-learning networks are the ultimate tools.

"Most applications of deep learning use “convolutional” neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes (orange and green) of the next layer. Credit: Jose-Luis Olivares/MIT"(1)

The deep-learning network is one of the most interesting things in machine learning. When we are talking about deep learning, we should describe what that term means. Deep learning means that the creature knows how to make something. But also the reason why that thing is done, and when it must do that thing. 

In the case of deep learning, the creature knows the background for some regulations. But the thing is that machine learning allows that the AI would collect data about everything from the internet. The system can simply copy-paste the texts from the homepages and then turn that text into speech. Learning new skills means that the system connects the databases to new entireties. 

So the deep learning means that the creature knows what to do when to do and where to do something. When we are thinking about deep learning from the point of view of artificial intelligence. We know that AI can make impressive things. Artificial intelligence can make things like movement series but the question is, does it know what it does? 

We all can make things like a series of movements and talk about many things. And we might know nothing about the things that we are talking about or doing. In the case of machine intelligence, deep learning means that the system records the actions like macros. 

For robots, the movement series are the macros that are turning to the movement series. When the robot sees the person who is injured that thing activates a series of movements. Whose mission is to save the life of that person. The thing that activates the movement series is the details like blood. Or something else that is stored in the memory of the computer. 

So the cameras of the robot are sending images to the computer. And if there is a match with the accidents that thing activates the help mode. The robot might ask "are you ok"? And if the person answers "hurts" or is quiet the robot would give first aid. If the person can talk the robot would ask what happened? If the injury is caused by violence. 

The robot would activate the protective mode. When the robot is searching criminals some details are activating the attack mode. Even if the robot seems like a human. It can use infrared and radar scanners. For searching things like firearms and knives. When the system detects a weapon. That thing activates the attack. The idea is that when the system detects some image. 

That image activates the workspace. The operation is quite similar to operating systems in which locking can open by using an image. In that kind of system, the image activates the movement series. And the thing is that those movement series are programmed similar way. Without depending. If those movement series meant for opening the door.

Or if they meant for the use of weapons. The programming of the movement series is always similar. If the robot uses a drill machine the image of the drill machine activates the program how to use that thing. And the same thing is useful in the cases that the robot must use any other tools or weapons. 

The robot can take the gun, but it can also have ultrasound or microwave systems or internal weapons like shogun in the hand bones of the artificial skeletons. The image of the gun is acting like a trigger that activates the attack mode. The thing is that the robot itself is a multipurpose machine. And the programming determines what it can do and what it cannot do. 

(1)https://scitechdaily.com/deep-learning-ai-explained-neural-networks/

https://interestandinnovation.blogspot.com/

Could somebody activate "ARTICHOKE"-programming by accident?

https://kirjabloggaus.blogspot.com/

Kimmo Huosionmaa

In the movie "Manchurian candidate" the psychological specialists program the soldier to make assassinations by using simultaneous repeating infrasound, the medicines, and hypnosis. In the elder version, the trigger was the heart queen, and that launched the effect, what caused the assassination. Or it causes the "dance effect", what makes the human, who is targeted for the brainwashing take the gun and shoot the targeted person. And that process is called as "brainwashing". The "dance effect" causes because the trainer teaches the victim the "katas", series of movements, what can be very dangerous. That kind of things are learned in the martial arts like karate, and that makes those self-defense skills so effective.


In the most advanced method would the brainwasher can use also Virtual Reality for making anger in the person's mind. The anger reaction is called as "trigger", and it would make the person act violently against some other person. In the worst scenarios, those psychological operators might play some kind of police or even play the victims own family. In the VR-method the consequence would be lowered by using chemical stimulants, that the victim would not even realize about that suggestion. Also, the feet or arms can be numbed during that thing.


That suggestion method was tested in the "PROJECT ARTICHOKE", and even if those experiments were ended in 1970's somebody might have thought those methods to some private military corporation. In the real world of science, the scientist, who makes the invention can teach that methodology to other persons, who are so-called the field team. In this case, somebody can play some kind of newspaperman or some activist and then ask about the methodology, what was used in ARTICHOKE-program.


The suggestion is the most attractive method when somebody would be wanted to give money to some other person. In this case, the methodology, what have been used in those mind control programs are using in the normal criminal activities. The trigger, what is used to make person murder, might use to suggest other people get the money to somebody else. But in fact, somebody has been targeted for that kind of brainwashing in "PROJECT ARTICHOKE", and if the trigger is the number 666 in the receipt, would the result be very devastating.

Difference between killer- and an industrial robot is only in their programming.

(Picture I)

Kimmo Huosionmaa

In the real world, we all want to say "no" to autonomous combat robots, what might be the greatest threat for peace today. The artificial intelligence makes possible to make robots, what are looking innocent, and they can be used in some normal work, but when they would see the targeted object, what have ordered to exterminate those robots would attack that target immediately. In this case let's say as an example the gardener robot would noticed something treating, that robot can be transforming as the combat robot, and maybe in that robot's arms is hired machine guns or laser weapons, and when something, what is ordered to exterminate would come in sight, would the wrist turn down, and uncover the nasty surprise.


This example, what I just gave, is one of the most feared things in the world of robotics. It is known as the "ghost protocol", and that means that normally the peaceful working robot would turn to combat robot, when it notices the target, what is dangerous for it or it masters. When some nation would say "no" to killer robots, they have forgotten that only programming would make difference between peaceful and the killer robot.  And in the real situation, the operators of the robots would just upload combat programs in those working robots by using the Internet. And this means that there is a change, to make the full army of robots in seconds by using industrial robots, what are equipped with those combat programs.


The Russians are always against this kind of programs because they have no experience in building the sophisticated robots, but their way to make new nuclear missiles authorizes Western world to make autonomous killer robots. And this is the problem with robotics, the same machine can do many missions, like working in the mining sites and other dirty and dangerous places. If the robots are made looking like humans, they could use same tools as humans, but in this case "one model fits all" can get the notorious shape because those human-shaped robots can also be used as the warriors because they can operate with normal infantry weapons. And the use of those weapons is needs only the program update, that those robots could transfer as the combat models in one second, by sending those programs by using the Internet.

Sources:

https://futurism.com/germany-pledges-not-killer-robots/

https://www.defenseone.com/threats/2014/08/top-six-strategic-threats-worry-about-todays-global-headlines/92000/

https://www.digitaltrends.com/cool-tech/un-told-to-ban-killer-robots-before-they-become-a-reality/

Picture I

https://cdn.defenseone.com/media/img/upload/2014/08/20/shutterstock_190737419/medium.jpg

https://crisisofdemocracticstates.blogspot.fi/

The speed of the supercomputer is not alone made ultimate computing.

(Picture 1)

Kimmo Huosionmaa

Problem with the quantum computer is not only in building the new kind of microchip. Another problem is to create the program or operating system, what can use all power of the supercomputer. And one solution would be used multi-layer kernel, where the top layer is normal Windows or Linux layer, and that layer communicates with processor thru the lower layer, what is specially designed for those ultimate processors.


The speed of microprocessors is not the only thing, what makes the ultimate computing and ability to make the sharp and powerful simulation. Another important thing is that the programs can communicate and control that electric equipment. In the production of microchips might use nanotechnology, what allows to make the three-atom size of transistors. In those transistors, the foot, collector, and emitter are made of the single atom.


And the super fast computers can also cool to the temperature where it turns to superconducting. It would give it ultimate clock frequency because that superconductor would eliminate the heating of the processor. But then the temperature must be near absolute zero-point or zero Kelvin temperature. Those ultra small and fast components need also new kind of kernels.


This means that if the computer programs and kernel, what makes the layer, where computer microchips are communicating with computer software have no ability to drive computer with full speed, or is not otherwise capable to exploit the full power of those computers, the superfast computers are useless. This is one of the greatest problems with the creating the quantum computer.


There are no programs, what can exploit the capacity of this new kind of supercomputer. One version to solve that problem is to make multilayer kernel, where the top layer is normal Windows or Linux kernel, what would control the lower layer of the kernel. This lower layer of the kernel would be made for communicating with that quantum processor. That would be one solution for that ultra-fast computer.


The ultra-fast computers are needed also in everyday computing because mobile solutions use so-called pre-processed computing, where all calculations happen in the server. This allows making the mobile systems, where are the long-term batteries. The mobile computers like tablets are usually quite weak processors. The calculations must do in somewhere, and those process would happen in the memory of central computers. But the servers must have powerful processors. And this would raise the need to develop the new kind of extreme fast computers, what can be used also for code-breaking.

Sources:

http://www.wired.co.uk/article/quantum-computing-explained

https://thestack.com/data-centre/2017/12/15/australian-engineers-design-silicon-based-quantum-chip/

Picture 1:

http://www.newequipment.com/sites/newequipment.com/files/styles/article_featured_standard/public/brain-quantum-computing-1620.jpg?itok=DQ7rP_xZ

https://crisisofdemocracticstates.blogspot.fi/


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The dance of robots

Kimmo Huosionmaa

The modern way to teach robots is not similar than making programs. It could be like teaching children. In this scenario, the programmer would take the robot by hand, and then make the movement of robot’s hand with it. After this, the system will record the movements, what are needed to take the example the glass from the table. But how to teach the robot to dance. This is the question of the week.


 And there is needed the dancer, whose character would be digitized by using laser-scanners. Then the movements of this character would be transmitted in the robot’s digital mind. After this, the robot’s computers would control it’s hydraulics like the way, that the robot makes dance movements. This is the very effective way to teach the things to robots, and the same way we can teach the language to the computer. There would be a dictionary book in the memory of this machine, and then we are starting to talk to the system.


First, we must write the answers to the computer, but when we are asked many questions, and wrote enough answers, the computer starts to work more and more independently. In this program might be three layers of answers. The first answer could be the text, what is written by the user, but when the computer learns more words, that would ask only if the answer is correct. This computer would record the questions and compile it with answers, and then compile those answers in many other things, what are involved with similar actions.


Then the robot is asked to bring the milk to the table, would the action be first, that the programmer would take the robot to the freezer, and then the robot would take the milk bottle. But when the programmer asks to bring the screwdriver, would the computer or robot uses same actions when it used in the case of the milk bottle. But now it must find that thing from somewhere else. So it starts to open the doors of lockers, and look for the things, what looks like the screwdriver.


For this action, the robot needs the picture of the screwdriver. After that robot compiles the picture in its memory to the real situation. The system, what is used in Digital Scene Matching Area Correlation can also compile the shape of the other things, and that would make those machines more independently than ever before. The bases of learning are the ability to make networks with words and actions.


And the first things in this process are the most difficult. When the first world and the action for it are made, the robot, what is controlled by speech comes near to us, by step by step. This network would give the opportunity to make the robot, what learns things itself. And this kind of robots might be dangerous in wrong hands. In real life, robots might have access to Internet dictionaries, and they can compile the orders to the databases. In this case, the robot would get orders to get the spoon.


Then robot would find what to do if it is given the order of getting something and then look for the spoon from the Internet. This makes robot easier to learn everything. And in real case robot could find the orders to use some equipment from the Internet, and in this case that could make robot very dangerous, because the programmers don’t know, what robot have been learned. In the case of robotics, the extremely important thing is to control what robot can do. There would be robot what have learned city warfare tactics and knowledge of using weapons without that the programmers even know about this, will the situation became very dangerous. Self-learning brings the uncontrollable element to tests, and this might even cost human lives.

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