The brain uses memory Lego to create new behavioral models.

“Princeton researchers found that a primate’s prefrontal cortex reuses modular “cognitive Legos” to solve related tasks, giving biological brains a flexibility that AI still lacks. The insight could help improve AI systems so they retain old skills while learning new ones. Credit: Adapted by Dan Vahaba (Princeton University), from “Brain Silhouette 2” (Littleolred, CC0 1.0, freesvg.org) and “Lego bricks” (Benjamin D. Esham, CC BY-SA 4.0, Wikimedia Commons).” (ScitechDaily, Your Brain Has a Learning Shortcut AI Can’t Copy)

Behavior is the reaction that we see from the outside. And. When an actor notices something, that something acts as a trigger. The trigger launches. A certain behavioral reaction. When we see our friends, we can say “hi”. Or, when we slip on the ice, we put our hands in a certain position, that protects our head from impact with the ground. This means that. When we see something. Hear something, or feel something that causes a reaction. Does something cause a reaction, and what type of reaction is? That depends on the memory blocks connected to that sense. If. There is no memory connection with sense. That doesn’t launch a reaction that we see as behavior. 

The ability to use complete building blocks to create new behavioral models makes human brains more effective than any AI. The AI cannot mimic that ability. Each memory block is like Lego. And. It gives flexibility and effectiveness. To handle memories. And especially the behavior or reflexes. Those are connected with those memories. 

The idea is that there is so-called macro behavior. Behavior is the thing. That. The memory block activates. The modular structure of memories. And actions. What we see from the outside gives humans incredible flexibility. The macro-behavior is like a puzzle, an entirety that includes multiple smaller bits.  Each macro-behavior module can act as a module for larger-scale macro puzzles. 

This means that the human brain handles behavior like a set of modules, which can connect and reconnect with other modules. If we think that. The human brain includes about 100 billion neurons. And each of them has one memory unit. That means there are 100 billion memory units. That gives very high flexibility and morphing ability. For those. Memory structures. The AI cannot mimic those things because that requires the ability to handle so many memory units simultaneously. 

The ability to create new behavioral modules makes human brains effective. The ability to interconnect those modules is unique. One of the reasons. The reason this thing is so unique is that the neuron is not passive. The neuron knows what kind of data it can handle. So it can tell other neurons. That. It's a neuron that processes signals that come from the eye. This means that the neuron that transmits a signal from the retina can ask the routing for data to the neuron that can handle that vision information. 

This saves energy and time because the signal doesn’t disturb other neurons. The transmitter neuron connects a mark. Like a serial number, to the data packet. This information includes data from the transmitter neuron. And if the route that neurons select is wrong. And the receiver neuron cannot handle that information. It can send that information back to the transmitter. Then the routing neurons can ask where the right receiver is. 

https://scitechdaily.com/your-brain-has-a-learning-shortcut-ai-cant-copy/

Does the AI turn us passive?

The answer to that question is simple. The way we use AI determines whether it will make us more effective, smarter, or dumber. The thing that can turn an AI into a tool that destroys our ability to search for information. Write our essays, and make things. Is the way. To use AI. The AI can be like a mother, who always gives answers when the kid asks something. That thing can be effective. The problem is that the mother will not always stand next to the kids. And give answers to their questions. 

But the thing. That teaches a critical way to think and search for information is to give the encyclopedia to the kid, and then order them to search for the answer by themselves. 

The best way to destroy our productivity, thinking, and other things. It is to use the AI as a mother. In that case, we use AI like a child uses their parents. We can ask everything from the AI, and that makes us lazy. If we just ask things. From the AI. like “Write an essay for me, and mention things X,Y,and Z.” 

That thing can make this way of working effective. But it doesn’t make the essay-writing thing. That advances our way of thinking. In this case, we can compile the AI into a mother who writes essays to their kids. Those things might be impressive. If a 40-year-old person writes an essay. That a 10-year-old kid introduces as their own product. That essay can collect many prizes in a 10-year series, but that doesn’t advance the 10-year-old kid’s thinking. 

The thing that can destroy our ability to search for information is the need for effectiveness. When we write our essays, like this kind of text, we make them for ourselves. And of course, they are made for the audience. But those things require time. Time is money. And of course, we must write our essays in our free time. Companies pay for work. They pay for effectiveness. And that means we can turn the AI into a mother, who we can ask to make things like write essays and reports for our boss. 

We can use the AI as a mother who always gives answers to us. If we ask something. Or we can turn that tool into the melody mother, who always pleases the user, and tells things that the user wants to hear. We can give the AI the ability to think. But should we follow the instructions that the AI gives? Or should we just push the red button when the AI says that: “Press the red button”?

We say that we can use the AI as a mother. We can ask everything. From the AI. And that makes us effective. But that kind of mother. Kills our ability to search for information. Or, is it the AI’s fault if our boss gives an order to use the AI assistant in every situation? When we use the AI as some kind of mother. Who always gives answers all the time. When we ask something, we make a decision. We are people who use AI. We give orders to the algorithm. 

The algorithm is the system that searches, sorts, and outputs information. The decision on how to use AI is made by humans. If we give AI the authority to give orders to us, and we follow those orders, that decision is made by a human. We decide if we want to take orders from the AI. And if we don’t even check who owns some newspapers or TV channels, the decision is ours. 

But same way. Humans own the AI companies. They also make decisions about what the AI should tell and what it should not tell. Those people decide what they allow people to get, and what they are not allowed to get. 

The biggest failure that the user can make with the AI is not to use AI as some kind of mother, who tells everything that we want. We can turn the AI into a melody mother, who tells us what we want to hear. The problem with commercial AI is that it must be user-friendly. In some cases, the user-friendly can mean that the AI’s purpose is to please the user. That thing can tell. What the user wants to hear.  But, in this case. The people who make products. Have responsibility for their product. The AI tells only things. That it’s allowed to tell. The person who makes the algorithms determines those rules. 

Unpredictable actions make human behavior hard to predict.

  Unpredictable actions make human behavior hard to predict. 

"To be, or not to be" 

98% of generative AI projects fail. The reason for that is that researchers try to replace humans. But is this the only thing that causes failures? Another thing that can cause failure is this: people try to use AI for purposes. There, it cannot operate. When we think. About those cases, there is always a possibility that people misunderstand. Things about what they should do. There is a possibility that people try to use AI as a tool that makes something impossible. And one of those things is. To use the AI. To make predictions about economics. 

Economics, psychology, and sociology are not exact sciences like mathematics and physics. The AI can calculate things like the energy levels of particles quite easily. If it knows all quantum fields. And all other things. That interacts with that thing. A psychological effect. Makes it impossible to make predictions about the economic advances. Human behavior is unpredictable. When we go out and say. We go for a walk. For 30 minutes. But we might not do that thing. 

Feelings and imagination are things. That allows us to make unpredictable things. 

We should always react to similar stress in similar ways. But we use only successful ways to respond to the challenges. If we don’t get successful feedback. We change the method. How to respond to the challenge. Every action that we do is a small challenge. When we cross the road, there is always a possibility that a car will hit us. When we walk to the shop, there is always a possibility that we slip on the icy street. That’s why there must be an alternative route. 

There is always a possibility that a burglar breaks into the shop, and the police keep the shop closed. Or the milk car can have an engine failure. Causing a lack of milk in the shop. Most everyday challenges involve a risk of injury. Or even die. If we cannot act right. If we just run to the road. Without caring about anything, there is a possibility that we run straight under the car. 

We can make a U-turn just outside the door and walk back in. We can change our direction for any visible reason. The reason why we make that U-turn could be that we just want to turn around. When a particle turns its direction, there must be some outside reason. The behavior of the particles and radiation is predictable. If we know the entirety. And the thing that makes us unpredictable is feelings. If we feel that we cannot accept something, we don’t do that thing, even if our sense says something else. 

This makes things like economics hard to predict. We should have knowledge of how certain persons react. In certain situations. But the problem is that we cannot know everything that the person faced during their life. And those missing parts can be the most. important things for that person. This is why it’s hard to predict how people react in certain situations. 

One of the reasons why behavior models are failing is that people hide data. Making predictions of the behavior of large groups of humans is much easier. When the group that AI uses grows. That means that the accuracy in the behavior of those people increases. But when we try to predict the behavior of a single person. We must collect all the data of them. We can see from that data how a person reacts to certain situations. If that person faces a similar situation. We can predict when. That person faces a similar situation again. That person will always act in a similar way in all similar situations if the way the person used was successful. 

The thing that makes us unpredictable gives us a big advantage over our competitors. The ability to make unpredictable moves and solutions makes predators hard. To track. And hunt us down. This is the ultimate ability in nature. Abstract thinking is the tool. That gives us the ability to make plans. That gives us the ability to operate unpredictably. 

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/

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. 

AI doesn’t make U-turns.

   AI doesn’t make U-turns. 

   

We don’t always make our best move. That means we don’t always choose the fastest way to travel between two points. We simply might like another route. We might like things that we see while we travel some other, slower route. And that is inhumane. The AI always predicts that we choose the fastest and most economical route. AI thinks that we don’t look at things like old houses. AI always predicts that we travel to some other place using the fastest route. It believes that we want to make journeys in the shortest time possible. It doesn’t think that somebody chooses a longer route, because that person wants to improve fitness. 

And that is the thing that differs AI from us. 

The difference between the human and AI approach to playing games like chess is this. The AI never plays anything for fun. Or the AI will never “kill time” with games. When humans play chess, sometimes we just move pieces on the chessboard and think of other things. AI always “thinks” that we play games to win. Not just to kill time, when we wait for things like a weekly meeting. This is one of the differences between AI and humans. When AI tries to predict things like interactions in nature. It makes things better than humans. The reason for that is simple. Everything that happens in nature happens in a linear way. When action happens. A reaction follows that action. When snow starts to fall on the slopes, that action continues until the avalanche reaches the bottom of the valley. The avalanche follows the laws of nature. It will not change its mind and start to travel backwards. 

And that is the difference between avalanches and humans. Humans can change minds. When a human starts to walk across the street, that doesn’t mean that the human will go to the other side of that street.  Humans can change their minds and make a U-turn. This is the thing. That makes human nature unpredictable. And that is one thing that evolution is made for: protecting species. And that makes humans difficult prey. When some animals hunted humans. We can run to the thing that seems like a dead end. And then put their spears against attacking animals. This way of making something unpredictable. Makes it hard to predict human behavior. 

AI follows the same marks as human psychologists when they try to read humans’ feelings. The ability to use special sensors to see how blood pressure and adrenaline levels rise. If the AI doesn’t have those sensors, it must follow body language. And the way a person speaks. In those cases, the sensor systems that the AI can use. Mean as much as AI itself. If the AI can use lie detectors. It is not very hard to make an algorithm that uncovers lies.  In that case, sensors mean more than an algorithm. 

When we see cases where somebody cheats people who serve as royal guards, we know that thing makes those people angry. Those people, with ultimate strength, can hide that emotion. We might be very angry. But in some cases. We don’t tell this to other people. Animals never hide their emotions. When human turns angry against a superior force. That means humans might not attack. Humans can flee from the field and wait for a better moment to strike. That is one of the things that makes us the ultimate species. 

Let’s go back to the chess game. The algorithm predicts that we always make the best move. And this is the algorithm’s ultimate strength. And that is the algorithm’s ultimate weakness. We might think of cases where a boxer faces many practice opponents. That boxer can win all of them easily. But then in the title fight, the opponent knocks out our boxer. In that case, we forget that. Some boxers can lose their training matches because they bet on that opponent. This means the training opponent can lose matches that nobody sees. But when there is a kind of meaning. That a person can fight differently. This is the thing that AI doesn’t always understand. Humans can save their best moves for the places where those moves mean something. So, we can think about different things than what we make. 

The ability to control the mind can be an ultimate training method. But it can also open the darkest visions of dictatorship.

 The ability to control the mind can be an ultimate training method. But it can also open the darkest visions of dictatorship. 

Brightest innovations have darkest sides. That is the thing. That we should somehow learn. This is the thing. That people like Fritz Haber are shown to us. The same person who created ammonia synthesis and made fertilized irrigation possible created chemical weapons that killed millions in the First World War. In the same way. Things like artificial intelligence and brain-implanted microchips are tools. That can make many good things. But those systems can also control things like killer robots. And that means we must realize that all of those systems have two sides, bright and dark. 

The ability to control consciousness and decode thoughts is a tool. That has always fascinated people. The CIA and other intelligence agencies studied those things for a very long time. And an idea is to create a spy who doesn’t know being a spy. The person would walk into the office. And read documents and other things. And then that person goes to the safe place. And after the mark, that person can repeat everything that this person saw and heard. The idea is to use humans as walking recorders who don’t know that they are spies. 

The BCI system can make this thing possible. Brain circuits store information in the microchip, and then that system decodes that data in a safe place. 

That makes them pass things like lie detectors and even sodium amytal interrogation. In some other visions, the consciousness control can be used to hide top-secret technology. What if the operator can make missions using highly classified systems? And then that person will not remember anything. And any details about the missions and equipment are lost in their memories. The ability to control memories is a tool that can have many roles in the future. Controlled memories. Or, so-called synthetic memories are tools that can be used to train complicated things very fast. 

And that thing can save human lives. If a person is in trouble in remote areas. And the only person who can give first aid is person who lacks the needed skills, those systems can offer those skils in a very short time. Those systems can teach complicated algorithms to people, and they can make many things that we don’t even think about possible. Brain implants can be used to teach people things like karate. When we think about things like mind manipulation. We must realize one thing. In the wrong hands, those systems are extremely dangerous. 

When Brain-Computer Interfaces, BCI systems operate with people, they interact with the computer directly through the brain lobes. That means the mind cannot separate data, or information that it gets from the BCI, from information that the person’s own senses will send. And that is the danger of the BCI, that it is a very powerful tool. The BCI can cause a singularity where people are connected with the AI and each other. By those kinds of systems. In the worst cases, the system destroys personality and turns people into a homogenous mass of information. The biggest problem with the BCI-controlled computers is that. A person will not separate what is real and what is a cyber dream. That means the person can simply forget to sit on the computer and then die because of a lack of food and drinking. 

The biggest problem is this: the person. Hackers can target the users of wireless BCI connections. This thing makes remote extortion possible. The same system also offers a way to communicate using brain waves. That thing is called technical telepathy. Technical telepathy is thing that allows a person to control robots and other things using brain-implanted microchips. Those robots send information to the controller’s brain that handles that information. Like it comes from the senses. These kinds of systems are created to control bionic prostheses. But they can also control robots and other things. 

https://www.livescience.com/17449-matrix-inception-brain-manipulation.html

https://www.popularmechanics.com/military/research/a63149552/pentagon-psychic-spies/

https://www.sciencedirect.com/science/article/pii/S2589004223007526

AI’s advancement turns slower.

    AI’s advancement turns slower.

Growing accuracy requires more complicated code. That causes a situation where AI’s advancement slows. When developers created some pseudo-AI tool in a couple of hours in the 1980s, those programs required about 10-50 lines of code. Those programs asked “what's your name?” and then they output the name that the user gave. Then they might ask, “Is the sun shining”? And then the user could answer “yes” or “no”. Then the program replied with something that gave good things in the user’s mind. Today, AI algorithms require billions of code lines. And that causes a situation where the advancement slows. 

So, when accuracy grows in program advancement slows. 

AI’s advancement turns slower. When its accuracy grows. And that means the AI follows the line of the limits in mathematics. Term limits mean the equation that’s the curve approaches zero endlessly. But that curve never reaches zero. If we translate this mathematical equation into an AI model, we can say that when AI approaches human level, the advancement slows down. Maybe. The AI will never reach a complete human level for programmers, but it will reach a level that is almost human-level intelligence. So the base elements in the AI are easy to make.

Then, researchers should find something more accurate. At the same time, they must find new, complicated ways to train their AI. And that means there is a need for more complicated algorithms. Those algorithms require more power, more time, and more accuracy. This means that the programmers use more and more time. That developers can  create more complicated code for algorithms. And how they should react. When accuracy grows. The sector of their algorithm can work turn smaller in the same time. When the need for accuracy grows, the speed of programming slows. 

And the next thing is that the error detection must be at a level where the system can be trusted. Another thing. What slows AI’s advancement is the calculation power. The system needs the entire data center for every query. That means the system needs so much calculation power that developers have no money to buy the systems that they need. Complicated code requires high-power, very high-accuracy systems. And when the system requires lots of capacity for the smallest duties. 

That causes a situation where the developers don’t have time to use AI as they need. When some details keep the system busy, it has no time to drive new code. Complicated code requires a complicated- and long-term error-detection process. The human coder cannot check even billions of lines of computer code. The entire human lifetime is not enough for that process if the human coder wants to make that thing without automated tools. 

AI and cartels.

     AI and cartels. 

Increasingly, more actions are being outsourced to AI. The stock market is a vast arena where individuals can utilize more or less sophisticated algorithms. The algorithm can see things. Like, changes in courses, like raising the value faster than a human. Then the AI-trainer must have the point where to sell and where to buy. The system must follow the small losses of the values and then decide to buy or sell. 

The AI must buy cheap shares and sell them when their value is high enough. The AI can follow more targets than a human. And it can see how the share values advance. If those shares are losing their value fast. And a person doesn't watch that curve, which causes losses. The AI will never get boring. And it ever sleeps. The case of the investor finds something else interesting: the screen on the table can cost millions in seconds. 

The AI can be a tool that solves those problems. But algorithms can exchange information illegally. The cartel means a situation in which a person uses an inner circle or non-public information to make profits from the stock market. 

And one version of cartels is a case. When traders make a contract to drive investments to certain targets. When somebody buys shares. That raises their values. So the group just dumps money into some target. The AI can also accidentally ask for advice from another AI. 

There can be a leading algorithm. That leader algorithm makes investments, and then other algorithms follow that thing. The fact is that,  even if some algorithms or AI chatbots look different, they can operate on the same platform. The AI chatbots can be the same, even if they seem different. And that thing means the AI can start to drive investments to certain objects. One AI-based solution can operate millions of investors' investments. 

This kind of AI can theoretically aim for even billions of dollars to the same target. And that thing makes it possible to raise the value of the shares. The AI is the tool that can react faster than humans. Criminals can create the AI-based investment cartels using quite simple algorithms. The AI that can control millions of objects in the bors can guarantee that a person cannot lose money in that game. The AI can invest small sums of money in a large group of targets. The system can buy and sell shares far faster than a human. So the person will always get profits. 

https://fortune.com/2025/08/01/artificial-stupidity-ai-trading-stock-market-behaviors-price-fixing-collusion-wharton-study/

https://finance.yahoo.com/news/artificial-stupidity-made-ai-trading-110500308.html

Can we trust AI?

    Can we trust AI? 

Artificial intelligence is a tool that doesn't create information from emptiness. That means the AI uses data and information that already exists. If something or somebody changes information from the data sources that the AI uses, the AI gives wrong answers. The AI can use old-fashioned information. In the same way, a human can take an old book of facts from the shelf. 

That is one thing that we must remember. The second thing is that most AI chatbots are owned by companies whose purpose is to bring profits for their owners. That causes a situation where some of the answers that the AI gives are customer-friendly. That means they are made to please customers, or people who pay for those chatbot services. 

However, one of the most important aspects of using AI is that it is actually utilized incorrectly. But the question is, why is it used? Because it makes work more effective. That means people who work with the AI assistants have no time to think and check the answers that the AI gives. The prime focus in AI use is that it makes people do more work. 

The modern business ecosystem has evolved over time, when all products were physical. That means the business ecosystem measures the effectiveness of the worker by simply calculating the number of work performances in a time unit. And that means the number of code lines that a programmer makes determines how effective that person is. And that means there is no time to analyze and think about the product that the AI makes. 

This is the problem with AI. It makes people more effective, and it should leave time to make analyses about the product. But the problem is that if the AI allows the person to work at double speed, that gives the company leaders the opportunity to fire half the workers. That decreases costs. 

When we talk about how AI makes things less effectively than a top-level programmer, we can find one very interesting question. If we analyze that argument closely, we face the claim that all programmers are top-level specialists. All programmers are top-level specialists. And another thing is that some workplaces are so hurried that they have no time to advise junior programmers. Sometimes old workers think that the young comrade is a competitor, and that makes the workplace atmosphere poisonous. 

The person might not dare to ask for advice from workmates. And that causes firing. The thing is that the AI is a tool that can help people. But we must remember that this tool allows us to analyze and think about new possibilities, only if we give it that chance. But we must have time and willingness to check the data that the AI gives. And if we see the AI only as a tool that allows us to cut the personnel costs, that is not the way to make our working life easier. 

If we just copy-paste texts that the AI makes. And don't even try to analyze that data, we don't increase trustworthiness in the company. That means we deliver the decisions to the AI. AI can also use old-fashioned datasets. Or it can use fake or falsified data. That means the AI makes mistakes in the same way as humans. It simply takes the wrong book from the shelf, and then that causes destruction or catastrophe. 

The AI is not immune to fake information. The problem with propaganda home pages is that their page rank are raised by drumming it with net queries. That makes those homepages easier to find. The problem with those pages is this: they use .com or some other neutral terminal identifiers. So, those homepages are not easy to connect to China or Russia. They are operated from servers that are in Western countries. The operators use a remote control to control those servers that operate in Western sockets. 

If the AI uses regular web browsing applications, that means it can select a propaganda page. The popular pages are always at the top of the page lists. And that thing means that the AI selects popular homepages. That decreases the AI's trustworthiness. The AI requires training. That training means that the users must set limits on what kind of sources those systems use. 

AI and robots can develop and fix themselves.

Today, AI starts to develop itself. But what if we were to take that ability to the physical systems? An even more interesting aspect is a “cannibal robot” that can collect spare parts and improve itself by utilizing another robot’s components. That means that the new robot can search for spare parts and improve parts from robot wrecks. That kind of thing is fundamental because it allows a physical robot to fix itself. AI-controlled robots that can collect spare parts from garbage and fix themselves will be the new tool for other planet research. The robot that can fix itself can also operate in remote areas on Earth. 

The AI that controls robots can make a checklist of what causes damage to the robot’s body. Then the robot can search for parts that can replace that weakness. If a robot requires a stronger shell, it just searches for plates that it can connect to its body. In the same way, if a robot requires more power, it can search for more servo-engines that can use its manipulators. The ability to search and benefit from raw materials from the environment makes robots more survivable and flexible. The AI develops itself in the same way as a robot that collects spare parts from its environment. 

AI is a computer program. And the computer program is a virtual robot. When AI develops its own code, it actually searches code models from the digital environment. In that case, the AI makes a model or matrix about things that it needs. Then the system will ask if there is some AI that has those abilities. Then the AI asks the code that gives the desired ability to the AI. The AI can also ask another AI to generate the code that it requires. And then the AI connects that code into itself. The problem is how the AI can detect things that it needs. And the second thing is that the AI must create the right form for the query that it makes for other AIs. 

The AI can also search things like internet databases about the code that it needs. And then generate that code in its code generator. In that case, there are two AIs: the first one that connects the code to another AI’s source code. Then the system boots another AI. And after that, the other AI makes the changes for the first AI’s source code and boots the system. The code also requires testing, and in that case, there must be at least two different systems. Because if there is a malfunction in the code, the other part of the system can operate without errors. That system can also remove malicious code from another system. But if there are no errors, the system can scale code changes through the entire system. 

https://www.euronews.com/next/2025/07/24/cannibal-robot-scientists-develop-a-robot-that-can-grow-and-heal-by-eating-others

https://www.livescience.com/technology/robotics/watch-this-robot-cannibal-grow-bigger-and-stronger-by-consuming-smaller-robots

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

Copyrights are not only things that cause criticisms against the AI-made films.

"Illustration of Netflix's use of generative AI in producing scenes for its original series." (RudeBaguette, “This Isn’t Art, It’s Algorithmic Propaganda”: Netflix’s GenAI VFX in New Series Sparks Industry Backlash and Fan Revolt)

Netflix faced criticism when it changed the' featured scenes using AI. That thing fights against copyrights. Another aspect is that the AI can include an actor's character in movies that are against the actor’s values. But this is not the worst thing on this path. AI-generated movies are tools for propaganda. 

The AI can create characters that look like real people, and those characters can even yell nazi propaganda. The AI can create an artificial model of any person that it sees. So the AI can use images from surveillance cameras to make those images and then put them into a film. That kind of ability allows for the misuse of the AI. 

That kind of tool is dangerous if those films are introduced as real life or documents. Normally, we know that at least most of Western film actors and filmmakers have such high morale that they don’t make those kinds of contracts. But in countries like North Korea, the government plays by different rules. That means those people can make films and use them as propaganda. But in the AI-created films, there is one difference between those films and traditional films. The AI will not say “no”. And that means those tools make everything. That's what their operators want. So, that tool can create anything that its users wish. 

The AI doesn't require any crew, and the operator can do everything alone. The AI has no will. Humans determine the limits of those systems. And there is a possibility that somebody includes their neighbors or other competitors in the surveillance tapes where they steal things. Or the AI can include any person in things like child pornography. You might also think about what things like fake police surveillance tapes can do to humans. That kind of thing can destroy any person’s reputation. 

And there are many different motives for that thing. The political motive could be that the competitor's reputation, or the motive for those things, can be that the competitor in working life can be erased by destroying those people’s reputation. 

Is AI a black box?

In the black box model, the only thing that means is the thing that the outside observer sees. That thing was the primary element in early 20th-century psychology. And the most modern observation tools are changing that thing. But in the early 20th century the brains were totally unknown. There was no change to research living brains. When EEG systems developed researchers could investigate the brain shell electricity. 

But things like PET and MEG scanners open new ways to see how brains work. But if we think of brain research as the box model, the early 20th-century models were black box models. The modern scanners brought the glass box or white box model to brain research. And the AI brought the grey box to neurology. That means the brain-computer interfaces, BCI, where researchers use brain electricity to control computers and soon read thoughts. 

When developers use the black box method to test programs they simply test that the program works. When we make the black box model for psychological learning methods, we can think about some 9-year-old child. That child can read almost everything and we can put that person to read even complicated scientific articles. The child can read those words but that person doesn’t understand what those things mean. The black box means that the actor can make many things, but the action is the only thing that the outside observer sees. That means that black box applications are not as safe as they could be. 

And most of the computer applications are black boxes for users. A regular user sees the interface and that’s enough.  In the white box or glass box application test, the tester tests the code. In the grey box, the test unit tests the functionality and code in the case of programming. The fact is that the AIs that we see are black-box applications. We see only the command line and the answer or thing that the AI makes. We don’t actually know even if the images and texts that we order the AI to make are made by some humans. 

In the case of the black box, the only thing that matters is that the system gives the right answer. The way the system makes that answer doesn’t matter. The only thing that the user sees means something. 

Is AI a black box? The idea of AI intelligence is something that is handled very little. We know that AI can solve many problems independently, but AI doesn’t know what it really makes. The AI can know many things, but it cannot have deep knowledge about those things. The model is taken from the black box psychology. In black box psychology, the key element is that the behavior that an outsider observer sees is something that we can accept. That means the AI just mimics human intelligence. The black box model means that the AI can give the right answers for some mathematical problems. 

But the same AI cannot make anything else. That is the idea of the black box. In the black box model, the thing that the AI gives the right answer is enough. The AI must do only things that the operator orders. So when the operator asks about the uranium enrichments and something like that the AI must give the answer. It must not make its own connections to things like weapon development. There can be things that deny the AI to give orders that can cause damage to operators or their environment. But those orders are in the AI’s code. The AI doesn't make those orders itself. 

The AI is sliding to the grey box application. For making autonomous learning the AI must have access to its source code. And the second thing is that the AI must have the ability to test the code. The AI can involve three parts. The system that creates the code. Another system that tests and accepts the code. And the last system that keeps the backup copy of the AI source code. A backup is needed for the errors. The system should have the ability to reject the code that is not functional. 

What separates us from AI?

Today we use lots of time to think, why does AI decrease our IQ? The answer is this: this modern time where we live benefits superficial people. Our working life encourages us to have maximum velocity. There is nothing wrong with the velocity-based working life. But the problem is that the measurement tools for velocity are things like how many executions we make during our working day. The measurement tool can be how many screws we tight. Or how much money we bring to our employer. 

Deep thinking is not encouraged in our working life. When somebody sits in a coding company, that person should use the AI assistant. Can you even seriously imagine that you would go to the library and borrow a book about the problems? And would you have time to stop deeply thinking about things that something really means? How often in the week, do we discuss philosophically and think deeply about things that we really see? We can do many things without deep thinking and logic. 

And when we think about things like some 1970s chess simulators, we must ask ourselves are those things really thinking? Do some ATARI, or Commodore 64 really think? It can drive a car on screen, but does it think? The system can move chess buttons and win chess games or some formula games against humans. But is that a mark of advanced thinking? Or does the horse, who sees three fingers and knocks the floor three times using its hoof, be some doctor of philosophy? Is a chess simulator with 64 kb memory more intelligent than some university professor? Maybe that thing is not as versatile as a professor. 

The IQ is something only if we compare it with something else. If we play chess alone or make something else, we don't need to be intelligent. If we are surrounded by people who have an extremely high IQ, in that case even a high IQ doesn't mean impressive. Same way. If our only opponent in a chess game is Garry Kasparov. That makes all of us seem like very bad chess players. Then we can think how ordinary chess player Kasparov is. Did an ordinary player play 2533 matches at the world champion level and win 1371 of those games? That means 54,13% winning games. But that happened at the highest possible level of the chess game. So does an ordinary player ever reach that level? 

But if the professor wins the AI in chess that is not news. The news is that the professor, some AI chatbot, or quantum computer loses a chess game against that machine. Nobody cares how many things the professor made before that chess game. Nobody even cares if the professor plays that person’s first chess game. And nobody even asked if the AI played chess before, or did the AI even knew how to move buttons. In the same way, a professor might not necessarily play chess at all. The fact is that all geniuses don’t even play chess. And if AI learns like humans, there must be something there that gets things like button movements. The computer can play chess but it might not do anything else. 

Normally we say that AI simply mimics things and then it doesn’t have a deep knowledge of things that it makes. When I read about that kind of thing, I sometimes remember one question from philosophy exams. That question is: “What separates philosophical thinking from the every day, or regular thinking?”. The answer is that philosophical thinking is deeper and more analytic than regular thinking. And that brings new questions into my mind. That is when we last exercise philosophical, deep thinking?”. When we do something, like turn screws in workplaces, do we really think about the purpose of that action? 

How deep out thinking? And how deeply do we think about things that we do in everyday life? The thing that’s enough is that we do what we must and that’s it. We have no time to think deeply about what some screw or other things can do. We simply do our job and that’s it. The thing is that humans learn through mimicking. We know many things. We know how to drive cars and use computers. We know how to fly airplanes and still, we don’t know anything about those things. We can drive cars. But we don’t need to know what happens in the car when we pull the gas pedal. We know that cars accelerate, but we must not know how cars make that thing. 

We could put the car to react with the gas pedal two ways. We can make physical contact between the gas pedal and the engine. Or we can use a camera that registers the gas pedal’s position. Then the AI accelerates or brakes the vehicle. For that system, the AI doesn't need any deep knowledge of things that it does. The system must simply accelerate the electric engines if we use electric cars. And the fact is that the AI must not even know what an electric engine is. When a car accelerates the system can involve code there is the word “engine”. Then some control circuits have a code that makes it react to the signal that is meant for the engine. 

Same way, if we hear the word that is our name, we automatically react to that thing. Our name is the thing that activates our attention. Sometimes we think about our names. But we forget that we learn that thing. Why cannot our name be C3PO? Because our parents didn’t give that name to us. Our names are “Jacks” and “Jills” because our parents gave those names to us. But have we ever wondered why some names are reserved for girls and others for boys?  And then we learn that those words are our names. 

But if our parents would give the name C3PO to us. We would react to that as our name. But do we ever imagine, why cannot our name be C3PO? Because we never thought that before. Then we can go back to begin, and ask how to describe thinking. Does thinking mean that we think how many times we hit some nails? Or does thinking mean how many chess games we learn? 

The thing is this: if we drive about 10000 kilometers without accidents or we win millions of chess games in our life that is not news. The news is that if some robot car drives off the road. Or if some supercomputer loses a chess game to some ATARI chess machine. Those things can be translated into that maybe the ATARI chess machine is more intelligent than humans and supercomputers. 

Training determines the abilities of the AI.

The new remote-controlled robot uses technology that allows it to teleport all human movements to that robot's body. That thing turns so-called external, or exobodies true. That nobody can operate in every mission where humans can. This kind of body can operate as an AI-training tool. The operator controls that robot in different situations. Then that system can share its data with other full-automatic robots that operate independently. In the robot world the word “independent” doesn’t mean necessary the same as in the human world. An independently operating robot is a robot that can do its duties without human assistance. That kind of system can operate under the control of data centers. 

So, those systems use the internet for the remote control of the robot body, and that computing capacity is not high enough to run complicated algorithms that do things like walking in the city and visiting shops to buy things. The fact is that the training of the AI determines its abilities. The human-shaped robot is like a screwdriver. The AI that controls that system is the thing that makes it capable of operating in many situations. The first real robocops are coming to the streets in Indonesia. 

Many armies and civil defense and rescue organizations test robot dogs that can search things like mines. Those systems can carry lasers or even missiles in the back. So that makes them able to destroy tanks and other vehicles. Those robot dogs can have quadcopter propellers in their legs. The system can turn the twin wing rotors in the same line. And then pull them in. In use, those systems can push propellers out. Turn the legs to the side. And then turn those propellers into an X-position. 

Researchers trained the Chat GPT for space pilots. That system did its mission very well, and that means the new drones can get AI-based autopilots. And in the wrong hands, those systems are terrifying. That means basically anybody can train their own AI-based aircraft pilot that can operate drones or, why not, full-scale aircraft. There are risks in those applications. The same autopilot program that controls the drone can control full-size aircraft. 

Some researchers are worried that AI chatbots can start to create biological weapons. Those systems can make almost everything. AI-controlled laboratories are things that can revolutionize material and molecule research. That means that those laboratories can also connect the DNA molecules together. Those systems can also make artificial organisms using AI-controlled nanotechnology. 

The self-replicating or self-amplifying mRNA molecules are created for self-replicating vaccines. Those self-amplifying (m)RNA (sa(M)RNA) can also unlock new tools for controlling nanorobots if a small miniature robot can carry saRNA molecules to the right cells. And that thing can make it possible to create the system that orders those cells to die. That mRNA molecule can order the cell to shut down its protein synthesis. Those self-amplifying molecules can also act like chemical computer programs. And that makes new tools to control nano- and biorobots. 

The ability to produce and self-replicate mRNA molecules in a dish, outside the cells. That opens new paths to genetic engineering. And another thing is that this kind of ability makes it possible to create new mRNA and DNA-based computers and data storages. 

There is a possibility to create the artificial mRNA molecule that self-replicates in the dish. That thing makes it possible to create the mRNA that reprograms the cell. There are two ways to make a large number of artificial cells. The first way is to create artificial DNA or mRNA molecules. The researchers can change the DNA from inside the cell’s nucleus. The other way is to use the mRNA molecule to reprogram the cell organelle and force it to create artificial mRNA viruses. 

The new method is to create artificial mRNA or DNA molecules. Then that molecule can self-replicate. Then researchers can take cells in control and inject those mRNA molecules into cell organelles. Or they can exchange DNA from the cell nucleus. That makes it possible to create artificial cells. The difference is how to produce that genetic material. In the last case, the genetic material is produced separately from the cells. And that makes this genetic material easier to control. That material opens new doors for DNA and mRNA-based computing and controlling miniature robots. 

https://www.birmingham.ac.uk/news/2023/ai-could-be-used-to-develop-bioweapons-if-not-regulated-urgently-says-new-report

https://futurism.com/scientists-chatgpt-controls-spaceship

https://www.rudebaguette.com/en/2025/06/real-life-avatar-tech-arrives-new-capsule-teleports-full-body-human-motion-directly-into-remote-controlled-robots/

https://www.rudebaguette.com/en/2025/07/theyve-gone-full-robocop-indonesia-unleashes-humanoid-police-robots-to-hunt-criminals-and-crush-the-drug-trade/

https://www.science.org/content/blog-post/first-self-amplifying-mrna-vaccine

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

https://en.wikipedia.org/wiki/Self-amplifying_RNA

The AI that beats humans is at the door.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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/

AI is the tool that can change web searches forever.

The new AI will not destroy Google immediately. But those new systems can have a big influence on Google for a longer period. The fact is Google controls so large a data mass that its power on AI development is stunning. However, the new AI-based tools can connect search results from multiple search engines. And then it can refer to those results and make the list of homepages that it used for the solution. Google's dominance ends when those AI-based search solutions can create such large databases that it can turn independent from traditional search engines. 

And that is the main problem with those things. Google is not the only search engine in the world. There are many other search engines that want to drop Google from its position. But some of those search engines are powered by Google. They offer one interface between a search engine and the user. Search engines require lots of computer power as well as AI needs. Companies like Google sell data. 

That means those search engines are operated by private corporations whose business is to sell data. The thing that makes those companies so powerful is that they collect data about the clicks that users give to them. The number of clicks raises the page rank. And that raises the homepage’s position on the search result list. This is one thing that causes critics against that system. It’s hard to get new home pages to the top of that page ranking list. People normally see only a couple of top homepages from that list. And then they select the thing that they think is the best. 

This is the Matthew effect in the web searches. Homepages that already have a massive number of clicks will get much more. And homepages that have no clicks will not even get them. That is the thing in web dominance. AI-based solutions can use many search engines at the same time. The thing that makes those applications interesting is the results or references that depend on the information that it gets if the AI-based web search application can see what type of persons will read those homepages. If some homepage is used by professors who work in a trusted university, that can justify that other people can trust those homepages. But how to confirm those people’s real identity? 

One solution is the quest book where people can say their work. And then we must also realize that confirming those answers is quite difficult. People can write anything they want in those quest books. Confirming those answers requires hard recognition. And that’s against privacy. Privacy protects people on the net. But the same thing offers protection for cheaters, propagandists, and net criminals. There are countries that collect data from all their citizens. 

But people will not need only search lists. The problem with those lists is that they are based on web addresses. There is a possibility that somebody changes the data that those homepages involve. First the page rank will rise using some addictive material. Then the workers change texts, or information from the homepage. That is the tool that is effective for hybrid operations and propaganda work. That is one thing that causes the need to create more advanced tools that can use data deeper than regular search engines. 

The biggest problem with modern networks is disinformation. The other problem is how to describe disinformation? People like V. Putin has a different way to see that thing than regular western actors. That is one of the things that we must realize. The same tool that works against propaganda and disinformation can turn the ultimate tool in their hands. 

https://www.rudebaguette.com/en/2025/06/chatgpt-wont-kill-google-sam-altman-downplays-the-hype-while-quietly-reshaping-the-future-of-search-with-every-new-update/