What should we do with the liability of the AI?

Should we be concerned because the product liability directive, PLD doesn't include immaterial damages like violating privacy or reputation? Those things were not mentioned as problems when the EU made the PLD directive. But today we have new tools that collect information from our behavior. AI-based systems can make realistic-looking people, who can make things. That, those real people don't ever make. And that can cause at least embarrassing situations. 

Who takes responsibility if somebody makes a film tape where some prime minister robs a bank, etc? The big question with AI is should the recognizable images that portray certain humans be prohibited or otherwise denied from the AI? The problem is that the AI makes images by following the orders that the user gives. And those things mean that some people can simply give the details of the neighbor for the AI. And then the AI makes the image, there is the neighbor's face. 

When we think about the PLD directive and other directives that should protect us against product malfunctions, those directives do not include things like normal blogs. There is the possibility that if some people travel to China, somebody writes the manifest in the name of that person, where that writer justifies the Tiananmen case and human rights violations in China. That blog can cause very big problems at the border zone. 

The thing is that the AI is the new tool that can make many things that ordinary systems cannot make and the main problem with the AI is what is not told about that thing. AI is the tool that allows people to show their creativity. But the problem is that AI can be misused for cheating people. When we think about newspaper articles, where people made pedophilia porn using AI, we must ask ourselves, what is the limit between privacy and security? When the AI should track the person who uses it, and then report the action to officials. 

There are lots of things. That people should know when they use some products. Those things involve privacy and other kinds of stuff, but another argument is this: what if somebody creates sick stuff using AI? Another thing is that there is a race between East and West. Who makes the best AI? The AI is the tool that connects different software under one dome. In the same way, it connects many other things like satellites and airborne, underwater, and ground systems to work as one large macro-scale system. 

The thing is that the Eastern governments are interested in the AI's military, intelligence, and surveillance abilities. The biggest problem is that the AI is that. There are no limits in the East for development work with AI. The Eastern authorities allow unlimited data use in that process. They don't care about copyrights or other things that slow the R&D work. AI is the next generation weapon. 

It can generate malware faster than any programmer can do. The AI can use it to collect data from social media, and then connect that data from other data sources like names that intelligence catches. The AI can search the entire social media to find the people with the same names. And then it can search photos if there are some things like uniforms. That marks the person as an interesting target for intelligence. 

Reporters and social media influencers are also people, who can serve Eastern intelligence and propaganda. We must have the tools to fight back. The AI can steal people's identities. So we can try to give rules for those systems. Laws are weak protection if the attacker operates outside the AU area from China or Russia. The Eastern nations and authorities don't care about laws in the same way as we used to care for and follow them. We can slow down or stop AI development by giving regulations. And then we can remember the Great Wall of China. That wall stopped the technical development and advance in China. 

That caused a situation where European countries just marched to China in the late 19th. Century. In that situation, those armies faced a feodal army. That army couldn't resist the modern European armies. And if we don't think about regulations carefully, those things can do the same thing to Europe that the Great Wall of China did to China. We know that we need regulations. But if we do not think about those regulations carefully, we face the situation that we cannot respond to AI espionage. 

Things like data systems' remote use allow users to run large language models LLMs from a great distance. Wrong regulations cause dangers. And if we just believe people and what they say, we can let the largest Troyan horse in our systems. The regulation is always a problem. The remote use of the systems allows the R&D to work for the customers over the Atlantic. The VPN-protected cloud-based systems allow. To operate laboratories remotely. That allows developers to make computer software development tools for the customer from their homes. Regulations are ineffective if nobody follows them. 

The customer can expect something from the data security. The problem is that many customers don't know anything about the programming or data leaks. And other kinds of things. Sometimes they expect the deliverer or some authorities to make the data security work for them. There is always one big question about data systems. That is what the system maker doesn't tell people. The "open source" means that the customer can check the source code of the program. But checking that thing requires knowledge of programming. The customer might not have the skills to ask.

Questions what they should ask. Computer programs, including AI, are always connected with the environment where they are made. The state where the programmer works can order or force that person to put malware in the code. In the West, we used to think that authorities arrested hackers. We cannot even think that some governments support hackers, and give them expensive tools to make their mission. Hacking that happens under state control was unknown to us until some hackers stole defense secrets from the USA. Those hackers were tracked to China. They are still free because they worked under the control of China intelligence. 

Large language models and fuzzy logic.

Large language models (LLMs) are problematic for programmers. They require a new way of thinking about programming. The key element in those systems is the input mode or input port. That understands spoken language. The system requires a model that transforms spoken language into text and then drives that text to the computer. And the text must be in the form that the computer can understand and turn it into commands that it can use. The system must also turn dialects into literal language that it can use for commands.  This is the first thing that requires work. The programmer must teach every single word to the system. 

The practical solution is to turn the word into numbers. In regular computing. Every letter has a numeric code called the ASCII code. The capital A (big A) has the decimal code 65. The programmer must realize that the small "a" has a different numeric code than the capital A. The little "a"'s ASCII decimal code is 141. That's why things like passwords require precise letters and if there is a capital letter in the wrong place the password is wrong. 

So, if we want to make the system more effective. We can give a numeric value for every single word that we find in the dictionary book. We can simply take the dictionary book and then give serial numbers for those words. The word "aback" can get the number code 1 (one). That thing makes it easier to refer to those words. Every word must be programmed separately into the system. And that makes programming hard. The other thing is. If we want to use dialects we must also program those words into the LLM, 's input gate. That programming is not very complicated, but it requires a lot of work. 

Diagram: Neural network

In human brains, neurons are the event handlers. In artificial, non-organic, non-biological computer networks, or computer neural networks computers or microprocessors are those event handlers. In human brains, thousands or even millions of neurons participate in the data-handling process. Those neurons make fuzzy logic to the brain. 

The idea of fuzzy logic is that many precise logical cases can make the system mimic the fuzzy logic. Fuzzy logic is a collection of precise logical answers. 

Another thing is that we must make a system that uses fuzzy logic. Making fuzzy logic is not possible itself. But we can create a series of event handlers that make the system seem like fuzzy logic. The idea is taken from the human nervous system. When a large number of neurons participate in the thinking process that makes the system virtually fuzzy. Every single neuron uses the precise (YES/NO) logic but every single neuron has a little bit different point of view to the problem. 

So the system uses a model that looks like the grey scale. There is the white that means YES and black that means NO. And then there are "maybe cases" between those YES and NO cases. Those "maybes" are the absolute logical event handlers like neurons. When that group of event handlers gets its mission, every single event handler selects YES or NO. Then the system calculates how many YES, and how many NO solutions it has. So those event handlers give votes to the solution. 

The model is taken from quantum computers. In quantum computers, data, or information travels in strings and finally, every string has values 0 (zero) and 1 (one). You might wonder how much power that kind of system requires if every event handler must process information. Before it answers. But then we face a situation where the system must answer "maybe". Another way to say "maybe" is XNOT (or X-NOT). Or if the answer is closer to "yes" another way to say that thing is XYES (or X-YES). X means that the system waits for more data.  

The system might say. That it does not have enough information in the data matrix. That is a large group of databases or datasets. And that is the major problem with AI. If the votes on the scale of "YES to NO" are equal that means the system has a problem. If the AI controls the robot that is in the middle of the road and votes are equal that robot can just stand in the middle of the road. Another thing that we must realize is that these kinds of systems are the input gates. Data handling begins after the system gets information into it. 

https://en.wikipedia.org/wiki/ASCII 

The first biological computer is real.

Cortical Labs introduced the first quantum computer that uses human neurons for data processing. The cloned human neurons are tools that can offer new ways to create new quantum- and neural systems with powerful calculation capacity and low energy use. In those systems, microchips give electric impulses for training those neurons. They live on special nutrients. The system outsourced the computing to the living neurons. Microchips download data to those neurons and then upload that data to the output devices like screens. 

Those neurons live about nine months because they don't get precisely the right nutrients. And the immune system doesn't support them by removing their metabolism structures and destroying things like viruses. The lab-growing cloned neurons are the new tools for the hybrid systems that can change our way of thinking about life. 

This new application is the "brain in a vat" that can control many things from sensors to robots. And there are always dangers if we create things like robots, that the human brains control. In this case, I mean a robot that the cloned brains control through the microchips. The microchip can connect the cloned brains with computers that can control the robot body. The system requires the human stomach and digestive system, with bacteria that can handle the right food. 

The robot body must also have a tank with bone marrow that creates immune and other blood cells that transport nutrients for those neurons that control the robot. The main problem with biological neural computers is the right nutrients. And another main problem is that those systems are dangerous. If we think about the neuron-controlled robot that eats the same food as we do, that kind of system can be more than a robot. Artificial mini-brains with cloned neurons are made in laboratories. 

Those neurons are normally used in medical tests, especially in Alzheimer's research. But those neurons are been empty. There should not be data in those brains. Microchip technology allows the system to create mini-brains with trained neurons. Those systems can make it possible to create medical treatments for brain damage. Cloned neurons allow medical specialists to fix the damaged brain tissues. However, the problem is that the neurons require their memories. The answer can be in the human memory cells. 

Researchers found star-shaped neurons in human brains. Those neurons can be the key to the human memory and why it's so effective. The biological neural network with quantum-network safety can use those neurons for data transportation. The system might look like a pressure post where pressurized air transports the message capsules. The data system just transports information to those neurons. 

And then that pressure tube transports it to the receiver. There the computer downloads data from that neuron. That is one way to transport important information safely across the distance. Biotechnology with neuron-fungus-electric conducting bacteria can make the biological computer neural network real. Those biological networks can offer new and secure ways to communicate at least in short distances. 

Another interesting thing is to connect microchips with the electric eel's cells. That creates electricity. Those cells can make electricity from nutrients for regular microchips and other systems. The problem is that those cells are vulnerable to viruses. Those electric-producing cells can also raise the transmission power. And they offer the possibility to create a long-distance biological neural network. The system downloads data from the neuron to the microchip. 

The system can transmit electrical signals through the biological neural channel in the form of electricity. Those electric cells can offer power to electronic systems. The regular version of the artificial axon is the ion accelerator where the qubit can travel in the form of ions

https://corticallabs.com/cl1.html

https://newatlas.com/brain/cortical-bioengineered-intelligence/

https://scitechdaily.com/mit-breakthrough-star-shaped-brain-cells-could-be-the-secret-behind-human-memory/

https://www.techradar.com/pro/a-breakthrough-in-computing-cortical-labs-cl1-is-the-first-living-biocomputer-and-costs-almost-the-same-as-apples-best-failure

https://www.tomshardware.com/tech-industry/worlds-first-body-in-a-box-biological-computer-uses-human-brain-cells-with-silicon-based-computing

https://www.ppvak.fi/ensimmainen-ihmisen-hermosoluista-ja-piista-valmistettu-tietokone-on-julkaistu/

Image: Ppvak

How hard is it to prove quantum gravity?

"In a dramatic twist on classical physics, scientists have cooled a mirror to near absolute zero with lasers to see if gravity might be quantum. This breakthrough could reshape how we understand the universe. Credit: SciTechDaily.com" (ScitechDaily, MIT’s Chilling Experiment That Could Prove Gravity Is Quantum)

Quantum gravity: mass, density, and weight form gravity. And every single particle has a quantum field. The gravity is the interaction with quantum dots and the gravity center is the collection of those quantum dots. The quantum dot forms when a spinning particle binds quantum fields from around it into the particle's structure. The outcoming field denies the destruction of the particle by pressing it together. 

The spin of the particle is normally 1/2. Which means. When the particle turns its direction, it stops and releases energy. When the spin direction turns, the particle simply pushes quantum fields away from it. In that case, a particle binds energy, but that time is so short that energy cannot turn a particle into a black hole. 

If we want to turn particles into black holes. We must impact energy in it. When a particle binds energy from around it, it forms a gravity pothole. When that pothole turns deeper that pothole-particle combination pulls energy from larger and larger areas. 

The quantum gravity theory can be proven or disproven. But the idea in the quantum gravitational model is that. Every single particle in the universe has a gravity field. Quantum gravity means that all nuclear fundamental interactions have the "domination limit". There is a certain mass, size, or density of the objects. The object's size determines which of the fundamental interactions turn dominating. 

Dominating interaction between quarks and gluons is strong interaction or strong force. Dominating interaction between hadrons is a weak nuclear interaction. The dominating interaction between an atom's nucleus and electrons is the electromagnetic interaction. That makes the quantum gravity model hard to prove. The gravity wave is so weak at the quantum level that it's almost impossible to detect. Other interactions cover that effect below them. 

Dominating interaction makes atoms stay in the form. And it determines the position where subatomic particles are. Gravitational interaction affects long distances and only between large objects. Or, if we follow the recent text, we can say that gravitation forms in the entirety there are multiple gravitational centers. Or, every gravitational center involves multiple gravitational centers. 

Dark matter and quantum gravity model. 

And then we can introduce an interesting model of dark matter. Dark matter can be material that spins too fast. That spin makes them bind quantum fields inside their structures faster than they should. So, when quantum fields travel in those particles those fields pull them closer together. 

That explains why compact dwarf galaxies' stars are too close to each other. When some outside effect pulls dark matter halo out from the dwarf galaxies that causes the effect that the outside energy tries to fill those points. And that pulls stars closer to each other. When some outside gravity field pulls dark matter halo from away from the dwarf galaxy. That can turn those quantum shadows stretch. That thing makes quantum fields move to those positions that that movement releases. 

Another interesting model is that the WIMP (Weakly interacting massive particle) can be the situation that the other particle will go in some particle. That means we cannot see that other particle because the other particle covers it. So, if we think that the hypothetical graviton is that particle that gives mass to all other particles the graviton curves the quantum field or superstrings that form the whisk-shaped structure or bubble around that graviton. In some models, the graviton is the small, quantum-size black hole. 

The standard model is very functional until we face gravitation. Gravitation has no repelling effect and that makes it interesting. There are theoretical models about things like antigravity but they are not proven. 

There are models that gravity can be a mixture of other three fundamental forces, strong. And weak nuclear forces and electromagnetism. There is also a model that the spinning movement of the particles binds quantum fields to them. So when particles turn wave movement into kinetic energy. They just harness energy from around them and bind that energy to their structure. 

And then to the quantum gravity model. The idea is that all particles are quantum spots (or balls) that bind quantum fields around them. That means all gravity centers are collections of quantum dots. So, those quantum dots form all gravitational centers in the universe. The thing that forms the black holes are the internal quantum dots. 

The quantum field is like a canvas that travels through and between those quantum dots. The size of the holes, or the distance of those quantum dots determines how strong those quantum fields can be. The thing is that quantum gravity means that mass, weight, and density are things that determine the particle's gravity field. 

https://scitechdaily.com/mits-chilling-experiment-that-could-prove-gravity-is-quantum/

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

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

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

https://en.wikipedia.org/wiki/Spin_(physics)

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

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

Dark matter might not be what we thought. (again)

Dark matter and dwarf galaxies are areas that are not extensively researched.  Dark matter is the gravitational effect without an unknown source. The dark matter halo that makes galaxies form inside them. Forms the idea that the dark matter simply binds quantum fields inside it. That halo makes the structure that makes conditions in it more stable that the star formation can begin. 

Dwarf galaxies don't behave as they should. So, there are some kinds of problems. With that model. In galaxy formation models galaxies are formed in the dark matter halos. There are problems with fitting the dark matter halo, Lambda CDM (Lambda Cold Dark Matter), and the gravitational models together. In old dwarf galaxies, the CDM model stands. But in young dwarf galaxies, those models have no match or they are hard to match. 

The thing that is interesting is are those dwarf galaxies near other galaxies? And if those dwarf galaxies are in the other larger galaxies like the Milky Way that pulls gas and dust away from those galaxies. There is a possibility that the bigger galaxies can scatter and pull the dark matter halo while dwarf galaxies are forming. In compact dwarf galaxies, stars are closer than they should be. That means there is less dark matter in a galaxy than in normal dwarf galaxies. If there is no dark matter that means stars can be closer to each other.

When galaxies form in the dark matter halo that thing causes an interesting model in the mind. Does the dark matter halo from the pool, where that energy travels a certain way forming the turbulence? Or is there some kind of channel between those stars and particles that they can start to accumulate in the dark matter halo? 

There is the possibility that some dark matter particles are connected with ordinary or visible material. In those cases, the dark matter particles can stay near or between electrons and quarks. But it's hard to detect. If the WIMP is a very high energy, small particle. That particle can cause wave movement to act like solar storms when they impact Earth's magnetic field. 

"A strange clustering pattern in dwarf galaxies hints that dark matter may be far more complex—and interactive—than we thought." (ScitechDaily, Are We Wrong About Dark Matter? Dwarf Galaxies Suggest So)

There is also a possibility that the WIMP has such a complicated structure. That it pulls energy inside it. And if the WIMP releases that energy very slowly that thing explains why those particles are so hard to detect. 

If radiation slides over the particle without causing reflection it can make the particle turn like a stealth aircraft. The other version is that the dark matter sends such weak reflection that energy flow pulls that reflection with it. That is possible if the particle has the fuzz ball effect. The large energy field with layers surrounding particles that have a certain form can have a model that the quantum field that surrounds that particle can go in the whisk-shaped structure. The outcoming energy flow can push that energy field in the particle. That makes it possible for the particle to send reflection radiation. But that radiation is released so slowly that it's hard to detect. 

There is a possibility that the dark matter can have weak, non-gravitational interaction. There is also the possibility that the dark matter can form black holes. The gravitational interaction between material and dark matter supports that model. So if the dark matter halo collapses because of its gravity, that means there can be a black hole that formed from the dark matter. 

The black hole has its origin as dark matter. Is a similar black hole. As other black holes. The dark matter halo reacts to gravitation and the gravity field can pull that thing away. The quantum gravitational model introduces that all particles have gravitational fields. This field turns more dominant when the object's mass is growing. There is the possibility that gravitational waves can also have the wave-particle duality. That means crossing gravitational waves can turn into particles. 

https://scitechdaily.com/are-we-wrong-about-dark-matter-dwarf-galaxies-suggest-so/

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

How to save energy in programming? The answer is reverse programming.

AI boosts programming most in the environment. AI requires lots of energy, and that is one of the things. That limits the use of the AI. AI requires a very large-scale data infrastructure. The AI server means data-center-size server systems. Programming also requires energy, and the problem with AI is its so effective. AI with qualified programmers is the ultimate combination. 

But there must be something that limits the use of electricity. AI is used in programming because it's a very agile method. The AI makes lots of code in a short time. But AI requires data. That it can make valuable software. Programmers train the programming AI like all other AIs are trained. The AI is a tool that can make things better but it requires intensive training. The programmer interacts or discusses with the AI about things. That it must do. The programmer must put things like paths into the right form. 

1) The intelligent solutions that mean the expanding data-handling structure. Neural network-based solutions take their job from the programmer. The system makes the drive, where the information travels around the system. And if the solution is not good, the AI calls more microchips to operate with that structure. That means the neural network gives only the necessary power to the system. 

2) The system can use speculative solutions. The AI learns what kind of software the developer makes. And then that thing makes the trunk for the program using things. That is stored in its hard disk. Then it starts to cooperate with the programmer. The programmer can see the demos of the programs that the system made before. 

Then the programmer can order the system to make an example of similar menus, as there were in "Project 6".  The programmer can recycle code from other projects, if the system has access to them. That means the system must not generate all the codes that it requires. The system must store those codes in the hard disk so that they are easy to use for other projects. 

Reversive computing means that the system can make everything in the opposite order. Sometimes people say that reversible computing means that the system can make calculations backward. This means that the system follows the mathematical principle that checking the formula happens by calculating its calculations backward. So we all know that the 4+1=5. 

The computer can check the calculation by calculating that formula backward 4=5-1. This is one version of how to make the code error-free. This kind of thing might look like very effective. But there are always problems with more complicated formulas. The system can also search for matching details from other tasks that fit that solution and share it with those things. 

Another way that decreases the computer's work is speculative computing. Speculative computing means that the computer makes tasks that repeat often before it gets the data handling mission. Speculative computing is like a secretary, who makes the work. That repeat is often ready before the boss brings it to the meeting. The speculative systems ask the computer. 

That takes a mission in questions like: Are there calculations like 4+1, and if the port computer says "yes" that marks the answer to the right modules. The system can handle every line in the program code as the module. The gate computer just says that if there are some formulas in the certain module. For this: "Is there 4+1 the gate computer says that "yes in line (or module) 3). Gate computer means the computer. That logs information in the neural network. That means a speculative system can make that point ready and wait for the new commands. 

Researchers are trying to create new alternatives for rockets.

"Illustration of an imaginative artwork depicting satellites piled up like a stack of pancakes. Image generated by AI." (Sustainability-times, Unstoppable Satellite Barrage: US Firm’s Hypersonic Cannon Fires Pancake Probes Into Space, Prompting Urgent Chinese Response)

Space rockets are tested and good systems for launching satellites into orbiters. But there is one problem with rockets. They are so noisy. And they need lots of space around them. There are a couple of alternatives. Researchers are trying to develop to replace those noisy systems. The easiest to make are the blimps, high-altitude unmanned airships that rise small Pegasus-type rockets to the edge of space. 

Those rockets will launch into orbit from the 40-kilometer altitude. The unmanned robot airships can use hydrogen for hovering, and explosions will not be dangerous to humans on board. Those systems are quieter than the regular rockets. 

The other version is the hypersonic aircraft that makes the ballistic jump. The satellite will release at the top point of the trajectory. Or the aircraft can pull that satellite behind it in the hypersonic glider. And when that aircraft makes the ballistic movement the satellite follows it. And then the ballistic movement acts like a sling to that satellite. 

Centrifugal launchers or spin launchers are tools that can replace rockets.  In those systems, the spinning plate accelerates the satellite at a very high speed. The spinning plate should be very large so that it stands for the spinning speed. If there is some kind of error the centripetal force destroys the plate. There is a possibility of connecting the spin launchers with magnetic accelerators, which can be connected to the stratospheric tube. 

The orbital centrifuge cannon can look like this. The image was made with AI. 

The tube itself can be connected with quadcopters. And it can have a telescopic structure. That means the system can push the stratospheric together. When the system is in use those quadcopters and electric motors pull that tower in full length. When the spin plate accelerates at full speed the system will open a hatch. 

At the top of that thing. Then it opens a ventilator that brings air behind the satellite. The magnetic track and pressure along with the magnetic system pull the satellite through the tube. Those satellites will not be very large. They can be about 1-2 kg microsatellites. Those satellites are packed in the aerodynamic shield. 

There are many variants of spin launchers. The laser system. That shoots below the satellite. Can give extra thrust. The system needs four lasers around the exit tube that give a stable push for the satellite capsule.  

There are models where the system throws the satellite to the ballistic, or suborbital trajectory. Then the small space shuttle, or space hook. That is connected to the space station or heavier satellite pulls that satellite upward. The orbital system catches the satellite. And pulls it upwards.  

In some models, the robot airships can carry extremely long whips. The spin launcher is like the Kevlar or spider silk rope. The rope will spin at a high speed. The length of that whip can be even kilometers. In some plans, there is a space station between Earth and the moon. There the whip length is enormous. 100 or even 1000 kilometers throw the small probes around the solar system. 

The thing is that the centrifugal canons that are connected with magnetic accelerators can be used in long-range artillery. Those systems can launch ammunition over long distances. When we think about the orbital spin launchers those systems can look like helicopter rotors. They can pull themselves into a small size. Then the origami-style system puts its structure into the full size. The magnetic accelerators can be put in the tubes. That is in the middle of solar panels. Those "space windmills" can send metal ammunition against targets at a very high speed. 

https://www.sustainability-times.com/energy/unstoppable-satellite-barrage-us-firms-hypersonic-cannon-fires-pancake-probes-into-space-prompting-urgent-chinese-response/