Hacking as the new and old threats.

 Hacking as the new and old threats. 

There are always suspicions that organized crime uses hackers to steal psychiatric papers to get policemen, prosecutors, and judges fired. Same way. Fake papers can also be a tool that allows people in foreign intelligence to make military forces. To kick off their best commanders. Sensitive information can be used to blackmail even top-level politicians. 

Hackers are people who steal data. Some of that data is harmful, or it contains personal, sensitive information. That kind of data can be a very effective tool if someone wants to destroy someone’s reputation. Hackers can steal data from psychiatric services and try to blackmail politicians. One of those cases was the Vastaamo case, where a hacker stole client information from a psychotherapy company. That hacker also sent a SWAT team to a person’s home and marked one aircraft passenger as a bomb carrier. Hackers can also make things like deactivate the payment cards of their victims. 

Or, in some cases, hackers simply steal money from accounts. If a hacker steals 1 euro. From 10,000 bank accounts. Those victims might not even notice that thing. Or do you follow every euro that is lost from accounts? If there is a loss, let’s say 1 euro, do you call your bank? You should tell that thing. Because in those cases, hackers steal a small sum from many accounts. But are hackers evil people? Some of them are. Some of them enjoy their crimes, and they want to hurt people. 

Being an effective hacker. You don't have to be a computer genius. You must only have access to passwords. One unprotected telephone on the desk gives a hacker access to the entire system. If a telephone is left open on a table, the hacker can call the IT support. And ask for access to the system. If that is some very high-level boss's telephone, that makes it possible to create a super user’s access to the system. That allows an intruder to make new users.  And that allows hackers to expand that operation. 

We can say that all criminals are marginal people. But what makes them marginal people? When some ex-neonazi or MC-gang member wants to get back into society, those people carry the criminal stamp. The rest of their lives. Some people ask, can ex-Nazis or other ex-criminals really regret their actions? If we think like that. Criminals are individuals. That means some of them regret, and some of them don’t. 

We will put those people. Into a marginal position in society.  The rest of their lives. And that raises the risk that those people will commit other crimes. If a person is surrounded by other criminals, they cannot re-integrate into normal life. 

Only jobs. What the ex-inmates can get is some kind of cleaning work. They carry a criminal stamp. The rest of their lives. 

So, are hackers criminals that society created? We can say that some of them have criminal behavior. Because of other people. didn’t accept them for who they are. The media introduces computer hobbyists as some stereotype who is not social. Their place in a mental hospital is the message. Those people face demeaning treatment; they have no girlfriends, because some elder guy wants to show that they are alpha members of some school party. What would you feel if somebody yelled at you, “Get out”? Would that be non-respectful behavior?

What if your workplace treats you as necessary furniture, whose mission is to do jobs? But whose mission is to be otherwise invisible. There are people who just wait to fire those ICT support persons. They show their authority to those people all the time. So, what if your workmates treat you without respect? What if you are always an outsider? Would you want to take revenge? Those kinds of outsiders are excellent targets for a criminal gang recruiter. The hyride threat is that those criminals can cooperate with foreign intelligence services. And they can give a tip for those hostile agents. Maybe they get guns or drugs as payment for cooperation. 

And then we must ask why hackers are what they are. In this case, we must ask why computer hobbyists are what they are. Those people are boring nerds who are not in any way interesting.  Do you know those people? Some of them are people who don’t find any social hobbies. They are people who are lonely because nobody wants to play with them. And the computer is their only friend. Then some criminals come and offer money or women to those people. Some hackers are young, and they don’t understand why some criminals want policemen's home addresses. But then. We must realize. People grow into criminal behavior. 

Maybe the first case that. Those people did it for revenge. It’s easy to steal somebody's passwords. To some system, and then send a message to customs or airport security. When we think of things like professional hackers. Those people work for some criminal organizations or governments. 

Some hackers are made by governments. They can be forced to make those things for the military intelligence. Or some criminal organizations can blackmail them. The fact is, this hacker must not be a qualified programmer. That person must not be an extreme computer genius. If the person gets access to the system is enough. One of the oldest tricks is to play some cleaner and then step into the office. And search for those passwords in the places. Like under keyboards or from computer briefcases. 

The question is always who made the initiative.  Who gave the idea for the hacking operation? Or was it the hacker self, or somebody else? That means some sensitive information can be used as a booster in political games. Psychiatric papers are tools that can offer a possibility to clear the competitor out of the way. That means some people can steal those papers. And some other people are willing to pay for that kind of information. 

AI-controlled drone swarms are entering battlefields.

 AI-controlled drone swarms are entering battlefields. 

The next step in drone technology is the development of AI-controlled drone swarms that utilize a LEGO-based system architecture. That cloud-based modular technology offers them flexibility and a multi-mission ability. Those drones can search enemy targets, support fire control, and make kamikaze attacks.  They can carry various sensors. Like Geiger meters, gas detectors, microphones, and other systems. 

That means when those systems require more calculating capacity, they unite their processors' capacity to work as a cloud-based morphing neural network. When the solution is made. Those drones, or their computers, can be separated, and then those systems, or each drone, can operate independently or as part of the entirety. 

Researchers took this idea. From a hypothetical alien model, where aliens can be like insects. When those insect-aliens require intelligence. Those things turn together. And then they share missions with each other. 

That idea is transformed into the drone swarms. Those drone swarms can call other swarms to solve complex problems, and then they share their missions or roles with the drones that participate in that swarm. Those drones can operate as one entity. Some drones can attack air defense. In that case, even small drones can be a more dangerous tool than anyone expected. They can hunt enemy commanders from the streets. The drone can slip into buildings through windows or ventilation. And even through sewer systems. If they are able to operate underwater. 

A large drone swarm can create a layer over the battlefield. Those drones can interconnect their sensors, which can transmit enemy moves to the commanders. Drones can also attack targets. That they recognize. The system uses images stored in the drone’s memory. 

One drone can drill a hole into the wall using explosives, and other drones can drive themselves into that hole. The drone can wait until its AI recognizes the target, and then attack it. The drone swarm can land on the roofs of trains. And then make their attacks. Large-sized cruise missiles or unmanned boats can carry drone swarms to target areas. Those drones can perform those missions without communicating with the command center. That makes them immune to normal jammers. 

The EMP system uses  high enough power EM impulses that it can destroy physical electronics. It can also destroy AI-controlled drones. The drone swarm can be released to the operational area just before manned aircraft comes. Drones can search for anti-aircraft artillery,  missiles, radar systems, and radio transmitters. They can attack ammunition storage. 

They can also make a radar and IR shield between aircrafts and ground-based systems. In those cases, small drones can search ground-based systems. And attack them. They can also disturb defense. Using jammer systems, aluminium bags, or IR lights that cover stealth planes behind them. Drone swarms can also close airfields, and they can fly into the aircraft’s jet engines. If drone swarms hover above runways, they can deny aircraft takeoff and landing. The small damage to the aircraft’s window or structure destroys the stealth-fighter’s stealth capacity. The drone swarm can communicate with aircraft. And stratospheric and orbital  satellites using laser systems. And those systems can also deliver those drones against the targets. 

https://www.msn.com/en-us/lifestyle/shopping/ai-powered-drone-swarms-have-now-entered-the-battlefield/ar-AA1LHwJg

The new computers are morphing neural network systems that mimic quantum computers.

 The new computers are morphing neural network systems that mimic quantum computers. 

"By linking smaller superconducting modules like building blocks, researchers at the University of Illinois Urbana-Champaign achieved near-perfect qubit performance. Their modular approach could open the door to scalable, flexible quantum computers of the future. Credit: Shutterstock" (ScitechDaily, Scientists Build Quantum Computer That Snaps Together Like LEGOs)

The fact is this. The regular binary computers can also operate like LEGOs. When a problem becomes too complicated for one computer. That computer can call more calculation units or computers. To operate on the problem. The system can call for assistance over the internet. That means when the computer doesn’t get an acceptable answer, it calls more computers to work with that thing. 

The new innovations in quantum computing represent a significant step toward a more efficient and effective way to calculate things. The reason why quantum computers cannot be stuck is this. They are like a tower of binary computers. Every layer or state in a qubit operates as an individual quantum computer, and if one of those states is stuck. 

Another state or layer comes and releases that state. When we think about the power of quantum computers.  We must remember that they can drive multiple programs. At the same time. Or they can cut and share complicated problems over those layers, and the AI-controlled quantum computer operational systems can act like LEGOs. 

Those systems can operate and run multiple different programs at the same time, but if that system sees something very complicated. That system will collect more and more quantum states and quantum units together to solve those problems. If the quantum system does not find an acceptable answer. That system connects more and more quantum units and quantum states to operate with complicated questions. 

So, in the case when the system doesn’t need very much power. That can allow all its units to work separately. With different problems. But when the system requires more power. The central system orders those systems to save their duties. And then start to work as a whole on that complicated problem. Things like drone swarms can use similar technology. That system can call all units to work on things. Like routes that those drones can choose. And then the system breaks entirely and shares those solutions to individual drones. 

The second big advance will be a room-temperature quantum computer. 

"Figure: (upper panels) Scanning-electron-microscope image showing a charge-density-wave device channel in the coupled oscillator circuit. Pseudo-coloring is used for clarity. Circuit schematic of the coupled oscillator circuit. (lower panels) Illustration of solving the max-cut optimization problem, showing the 6 × 6 connected graph, circuit representation of the six coupled oscillators using the weights described in the connectivity matrix, and values of the phase-sensitivity function. Credit: Alexander Balandin" (ScitechDaily, UCLA Engineers Build Room-Temperature Quantum-Inspired Computer)

The UCLA engineers built a quantum-inspired computer. The system will use a morphing neural network technology that mimics the quantum computer. That can change the world. The room-temperature quantum computers are tools that will revolutionize computing. When we think about things like quantum dots in virtual quantum systems, those quantum dots are the binary computers that operate like states operate in quantum computers. That makes those computers very powerful tools. Because those systems are immune to errors and stucks. 

"Scientists have built a physics-inspired computing system that uses oscillators, rather than digital processing, to solve complex optimization problems. Their prototype runs at room temperature and promises faster, low-power performance. Credit: Shutterstock" (ScitechDaily, UCLA Engineers Build Room-Temperature Quantum-Inspired Computer)

If some of those computers are stuck, some other computer releases that system. Because that system is morphing. That means all its participants can operate independently with different problems. But when a problem reaches a certain state of complexity. The system sends a message that all computers must unite their force to work on that problem.  

If a researcher makes a quantum computer that operates at room temperature, that system can be superior to the regular binary systems. There are so-called virtual quantum computers that operate in data centers. In those special neural computing systems. Each physical binary computer works as an individual quantum state in a quantum computer. The system operates entirely. It tries to mimic a real quantum computer. 

The system can operate like a quantum computer, but the qubit states are replaced. By using a physical binary computer. Those systems act like a quantum computer. That system’s Achilles heel is that it needs. A lot of power. If we want to make a virtual quantum computer. That qubit has 129 states, which requires a system with 129 binary computers. And that causes very big electric bills. Those systems release heat. This means those systems require powerful coolers and other things that protect those machines. 

https://scitechdaily.com/scientists-build-quantum-computer-that-snaps-together-like-legos/

https://scitechdaily.com/ucla-engineers-build-room-temperature-quantum-inspired-computer/

A company is always as good as its workers.

 A company is always as good as its workers. 

We can react to change only if we recognize what changes. We don’t have to make a comprehensive response. That goes through the entire company. in every case. We must find the way. How to respond to that new situation effectively and economically. In the changes of data directives, the change will touch information management. That means people who work on assembly lines don’t have to react to those kinds of things. If the reaction is wrong, the damage can be huge. 

The reaction to change must be. 

1) Effective

2) Economical

3) Sustainability. 

4) Legal. 

5) Respect nature. 

6) Respect values. 

7) Follow the environmental needs. 

The company cannot make better products than its employees can. 

Workers are the company. Their skills make the company’s products. Without products. The company faces bankruptcy. The worker must develop their skills so that they and the company can respond to challenges that a changing environment and business ecosystem form. The business ecosystem can face changes. That form when things like building materials change. When bricks turn into concrete elements. That means the brick factory must change its products. It must start to create other ceramic products. 

Another way is to search for and expand a new marketing environment. The third way is to close the factory or reduce production and kick out workers. Or, the company faces bankruptcy. The problem is that products are as good as the worker’s skills. If the company leaders kick workers out. The factory’s ability to respond to new orders decreases. The answer is that robotics is a thing that can give answers. The company must find out. Does it have the skills to make these types of projects? 

The company can get those skills in three ways. 

1) The company can hire people who have those skills. 

2) The company can order a course to get those skills. 

3) The company can give orders that every worker must go to the library, and then find the information about the new thing. 

There is a famous so-called 10-20-70 model of company learning method. 

10% is official courses

20% is learning in teams

70% is self-directed learning. 

In all those cases, some kind of test can be a useful tool to make sure. That people really have the skills. That they need, or they claim. That they have. 

There are some problems with the last two cases. The problem is how to make sure. People who use self-directed learning really know those things. That they should know. Another problem is. How to make people share their knowledge in the team. People think that their skills are capital, which guarantees their workplace. That means people can hide their knowledge. They might think. If they hide something. That is important. That raises their value. This is one of the reasons why things like AI projects can fail. 

The failure can be caused. Because people don’t share their information. They don’t want to help their competitors. And that is one of the biggest problems with the working environment. Workers must have certain skills to make products that the company sells. Without those skills, the work is undone. 

When we face the need for change, we face the effect. That comes from the outside environment. Those changes can be legislative. Or they can be technical, or some other things. Like war causes situations. That companies must change. So that they can adapt to the changing environment and its challenges. The change can reshape a customer’s relationship. Or it can reshape the manufacturing lines. Or it can be reshaped into information management. The control team must find a way. To solve or respond to the problem. The fact is that outsourcing some solutions is a good choice. The company must not keep or recruit all the needed personnel itself. But outsourcing requires money. And it can cause a situation where all people who worked with that thing go to other companies. 

When a company makes a list of needed changes. The list must not be too long. That the people who work with those things have time for deep analysis. Of things. That requires changes. If the list is too long, that means the analysis turns too superficial. The central business is the focus of that operation. But then. We must understand that if we outsource something. We must have money. For that thing. Outsourcing the need doesn’t mean that the need is gone. 

Why does AI fall into the infinitely continuing loop?

 Why does AI fall into the infinitely continuing loop? 

Infinite loops, or infinitely continuing loops, mean that the system is stuck operating with the same problem without a reasonable solution. 

All our neurons operate as pairs. When the first neuron sends a message to the receiver. The receiver acknowledges the message. Those receiver neurons send the message. That the message is received. Sometimes something causes a situation. The transmitting neuron sends an acknowledgment back to the receiver. And then. Those neurons start to play a ping-pong, using those neurotransmitters. That means those neurons can fall into a situation where they just surround the same dataset in the form. Called infinity loop. 

But no problem, the outside neurons come and remove the loop. That releases those neurons to operate on a new problem. The outside system must only recognize the infinite loop, and that is quite an easy thing to do. The control system, “judge,” must just see. The system  under the “judge’s” supervision gives the same answer repeatedly. If the answer is the same multiple times, the supervisor sees that those data processor units, like neurons, can be released to operate on another problem. 

The reason why our brains would not fall into a thinking loop is this. We have so many neurons. Our neurons watch each other. And if there is a situation in which some neuron group starts to operate on the same problem repeatedly, and data starts to surround that neuron group, the outside neuron comes and releases those neurons. That means. Outside neuron destroys neurotransmitters that carry the surrounding information. And releases those neurons. 

The upper image introduces the algorithm. As you see, data travels in a circle. But sometimes the algorithm makes mistakes. The mistake can happen when the algorithm uses the wrong dataset. Or sometimes the algorithm simply returns the last mission solution to its beginning point. The system should only send a mark that it's not busy. But sometimes the router will send something else to the return point of the algorithm. When data travels in a computer. 

One wrong value causes a scandal. And in binary computers. It is not accepted. If the value is more than 1. Binary processors can operate only in states one and zero.  The stuck gate causes value 2. The system is stuck. The problem is that the system cannot null itself. And in infinite loops form when data surrounds the system. And it cannot null itself. Without stopping, those algorithms cannot take on the new mission. 

Have you ever tried to make an infinite loop in your mind? The infinite loop, or infinitely continuing loop, is the case where thoughts surround in a circle. The infinite continuum is the case where we think, “I had a dream, that I had a dream...”. This means the list of those internal spaces can continue forever. But the fact is this. Our brains cannot make an infinite loop. Or an infinite circle. Things like pi (3,14...the ratio of a circle's circumference to its diameter) are not infinite loops. They are infinite continuums. 

Outside systems can deny the infinite loops. When the system operates to solve a problem. The outsider judge system. Checks the answers. If the main system always gives the same answers, the system has fallen into an infinite loop. And the outside system orders stuck systems to dismantle the loop. And reboot the system for the next mission.

Brains can create situations that we might think of as a “virtual infinite circle”, but we are never stuck in that thing. And the reason why the AI can be stuck in those processes is this. The algorithms are like circles. But the second thing is that. The AI operates over the binary computer platforms. The AI is an algorithm group that requires the giant computer centers. There are billions of microchips in that system. But there is one weakness. When the AI or large language model, LLM, starts to solve the problem, it has a certain data handling capacity in use. 

Or, the system reserved a certain number of microprocessors for use in that problem. But if the system cannot solve the problem, the AI calls more data handling units to operate on the process. If there are no limits for that process, the system can use its entire capacity. For one problem. That is the thing. That causes the infinite loop. The computer makes its calculations. And then. It makes an error detection. Calculating the same calculations backwards. Another way is to make the error detection. Using two different lines or computers. 

If both computers have the same solutions, that means (probably) there are no errors. There is a possibility. There is a common error that causes a false answer in both computers. But the last one is faster. Than the case. Where the system detects errors. By calculating all calculations backward.  After that, the system can introduce a solution. But sometimes, Something causes situations that the system cannot detect the errors as it should. 

The infinite loop forms in the case that there is no outside actor. Or the system uses its entire processor capacity to solve some problem. In that case, the system has no resources to end the task if the processors are starting to play pin-pong with the solution. That keeps those processors busy, and they have no time to null that process.  

There is a need for an outside microprocessor. That gives an order to stop the action. If the entire system is not reserved. There is a system. That denies the main system from falling into infinite loops. 

The problem with AI is this: It’s not intelligent.

 The problem with AI is this: It’s not intelligent. 

"Opaque AI systems risk undermining human rights and dignity. Global cooperation is needed to ensure protection." (ScitechDaily, “AI Is Not Intelligent at All” – Expert Warns of Worldwide Threat to Human Dignity)

The AI is not as intelligent as we know intelligence. The AI utilizes specific parameters to gather information. If AI collects information about a person, it might use things like photographs. And it recognizes that object. Then it searches texts that are written or films that are connected to that image. Then it creates a data matrix about that person. The problem with that is this. The AI doesn’t actually know. Do those texts and other data have any connections with a real person? 

The AI is a tool that can connect data from multiple sources. And it can make many impressive things. But AI doesn’t know. Does the person really have a connection with the data that AI uses? This is the problem with the AI. The AI connects data and makes medians, and then it creates a summary about that text. An interesting thing about AI is that. Every single data unit can be introduced as a numeric value. We can give as an example a ca value of 6. Or something like that. 

This makes the system easier for programmers because numeric data is easier to handle. But the problem is that. AI doesn't have a natural ability to be suspicious of data. That it uses. This means the AI is easy to cheat. The AI can have prohibitions that prevent users from using the AI for certain types of purposes. But the problem is that the AI programmers and engineers are not always experts in law. Another problem is this: what if  the AI software order comes outside the EU region? 

The programmer can operate using remote connections. That means the person who orders software might be different from the person whose name is in the paper. This means the real customers can sit far away from the person who is marked as a customer. The problem with algorithms is this. Things like malware are easy to make using the AI. And there are lots of actors like China and North Korea who want to create AI to control their society. 

Those actors have no limits for recruiting programmers to create AI that can generate spy programs. People forget this aspect too often. They think that the entire world is like the EU, where people follow the same rules. We forget that the Internet allows spies to operate from thousands of kilometers away from their target. The spy who operates for Chinese intelligence can sit in an office and use the internet and virtual spying tools to steal secrets. 

https://scitechdaily.com/ai-is-not-intelligent-at-all-expert-warns-of-worldwide-threat-to-human-dignity/

Ukrainian new Flamingo missile can change, something.

 Ukrainian new Flamingo missile can change, something. 

Ukrainian new FP-5 Flamingo missile has a range of approximately 3000 kilometers. That means it's capable of long-range strikes against Russian targets. The Flamingo missile made its tribute and destroyed the FSB station in the Crimean area. The missile itself uses a GPS/GNSS navigation. With inertial, INS backup. That means the system is immune to the jammers. The ability to make long-range strikes means that Ukraine can cause damage and pose a threat to large areas in Russia. And that means the Flamingo is the thing that can cause very bad damage to the Russian military industry. 

The missile range is long enough to reach Moscow and many other important targets. The thing that makes Flamingo important is that the system decreases Ukraine's addiction to Western weapons. This missile can fly without the GPS if the system knows the position. Where the missile is launched. The operation can be made using the missile’s INS navigation. The system can also use AI-based target recognition. And that means the system is highly independent. The fact is that the large-sized target drones are easy to transform. Into missiles. 

Geran 3 drone. 

"The center of the circle is not on the frontline and its not either very far west in Ukraine either. Even some larger cities and or factories east of the Urals Mountains are in range." (Reddit)

The new tools, AI-controlled, like visual image or Lidar-scanner-based terrain contour matching TERCOM systems, connected to inertia, make drones independent of GPS. Those systems can also make it possible. To create new types of precisely attacking drones. That can even ambush jet fighters and helicopters from the air.  When a jet fighter turns against a drone swarm, some of those drones turn to attack that fighter. 

Russian jet-engined Geran 3 Kamikaze drones are causing trouble for Ukrainian air defense. Those drones are too fast for helicopters but “too slow” to manned jet fighters. The main problem with drone defense is that. Those drones can fly in large swarms. That means those jet fighters might face a situation where they have enough ammunition to destroy all those drones. 

Another problem is that those drones could change their targets. The advanced AI makes those drones more lethal, and the next-generation drones can have an AI-based imaging infrared systems that allow them to attack jet fighters and helicopters. Drone swarms can attack as one entity. The problem is that AI-controlled drones can already be a reality. The AI-controlled drones can make ambushes against defending fighters and helicopters. The AI itself is cheap when the algorithm is ready. 

https://www.aerotime.aero/articles/flamingo-missiles-strike-fsb-base-crimea

https://www.defensemirror.com/news/39493/Russia_s_Geran_3_Jet_Powered_Kamikaze_Drone_Ready_for_Operations

https://www.ir-ia.com/Ukraine-Flamingo-FP5-Cruise-Missile.html

https://sfg.media/en/a/ukraine-flamingo-missile-claims-doubts/

https://en.wikipedia.org/wiki/Flamingo_(missile)

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

https://en.wikipedia.org/wiki/HESA_Shahed_136

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

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

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