Researchers found interesting things in brains.

Researchers found new interesting details in human brains. First, our brains transmit light. And that light gives an interesting idea: could brains also have optical and photon-based ways to transmit data between neurons? And if neurons have that ability, how effective and versatile it is. We know that there are no unnecessary things in our brains. So that means the light in our brains must have something to do with neurons. But do neurons use that way to transmit complicated information or is it meant only for cleaning neuro-channels? 

That interesting light causes the question: does that effect have some connection to light that people see when they visit near death? When we think about death the neuro channels will empty from neurotransmitters and electric phenomena. That means our nerves are more receptive to those signals than usual. So could that light have some kind of interaction between neurons or axons? Is there some point in neurons that reacts to that ultra-weak photon emission, UPE? 

Does our own neural activity cover that light below it? There is an observation that dead organisms shine dimmer light than alive. And maybe that light turns dimmer when the creature closes to death. There is an article in the Journal of Physical Chemistry Letters, “Imaging Ultraweak Photon Emission from Living and Dead Mice and from Plants under Stress” that introduces ultra-weak photon emission from dead mice and plants turn dimmer. 

And the question is this: can humans see that ultra-weak photon emission and its changes subliminally? The article says that all living organisms shine weak light that disappears when a creature dies. Also, things like mammals shine IR-radiation but the main question is can the ultraweak photon emission, UPE happen on purpose, or is it some kind of leak? And can humans see that phenomenon but that observation cannot reach our consequence? 

There are two things that self-learning systems must do to become effective. Effectiveness means that the AI or human brains should ignore irrelevant information. If that thing doesn’t happen, it grows databases and data mass in the system. When the system makes a decision, it must select the right data from the data that it has. And then the system must make decisions using relevant data. This makes the situation problematic. The system must decide what kind of data it needs in the future. 

And that is quite hard to predict. When we learn something we cannot be sure do we need those skills anymore. Maybe we don’t need skills that we learn in the military ever after that. But as we see, the future is hard to predict. The other thing that the AI must do is to adjust its processor's actions like human brains do. In human brains, brain cells have multiple frequencies in oscillation. Scientists say that those differences in oscillation frequency are to avoid rush hours in axons. 

That thing means that brain cells give time to clarify axons. Because brain cells have different frequencies that make it possible to control axons and deny the situation that multiple neurons send data into the same axon at the same time. Those multiple rhythms allow the brain to avoid rushes in axons. The same thing can make a fundamental advance in technology. If we think about the situation that the system that runs AI doesn't have a controller that includes system architecture that makes processors operate a little bit at different times that causes a situation that all processors send data at the same time to the same data gate. That causes a rush and makes the system jam immediately. In the electric systems the system that uses electric impulses for data transmission. 

Processors. That operates in the same moment. Can form standing waves in the data channel. And that burns the system. There are many interesting details in the human brain. That thing opens visions that maybe brains might also have an optical way to transport information. Researchers try to find out the purpose of that light. And if they find a point in, or on neurons that react to that light, they find a new level in their brains. Another interesting detail is that different parts of the same neurons learn in different ways. That means that the neuron itself can be more intelligent and versatile than we thought. 

https://neurosciencenews.com/hippocampus-neuron-rhythm-29277/

https://www.psypost.org/different-parts-of-the-same-neuron-learn-in-different-ways-study-finds/

https://www.psypost.org/neuroscientists-discover-biological-mechanism-that-helps-the-brain-ignore-irrelevant-information/

https://pubs.acs.org/doi/10.1021/acs.jpclett.4c03546

The AI removes trainees from workplaces. And that is not a good thing.

The AI doesn't take your jobs. It denies new workers to come to the field. That causes questions about how workers can improve their skills. The AI doesn't take your jobs. It denies new workers to come to the field. That causes questions about how workers can improve their skills. What if humans especially ICT workers lose their basic skills? What if all programming turns into cases? Where does the system worker just give orders to the AI? Using normal language. Then the AI follows instructions like a human coder.  

But the main thing is that the requirement of effectiveness and ultra-capitalism forces company leaders to make that choice. They choose AI instead of hiring programmers. And that is one of the biggest problems in the ICT business. But then we must remember that today is the end of the road. AI is the solution that is the next step into the continuum where things like encoding software are outsourced to countries like India. Western coders didn’t get jobs because it was cheaper to hire experienced workers from India or some other far-east countries. 

When companies outsource encoding to the Far-East they become vulnerable for that work. That means that those workers are working in countries that are members of BICS. So those workers can work under the control of intelligence or other authorities who order them to make spyware and other malicious tools. We are people who live in Western democracy. We learned that if somebody acts as a spy that person will be arrested. The same way we believe that if somebody hacks into some country, we must just request that those authorities arrest those people. We didn’t expect that those hackers worked for the Chinese intelligence service or government. They were protected by the government. 

The next logical step is that the AI starts to work with codes. And that takes jobs from humans. This thing means that the programmers lose their basic skills, and without basic skills, they have no advanced skills. Programming is like learning things. If we compare the programmer’s advance with going to school, we must realize that every person in the world writes their first words. Before that, they must learn to read. And every single person reads their first word once. Before we can learn advanced mathematics, we must learn the basics. So we all calculated once 1+1=2. If we don’t learn the basics we cannot learn anything new. 

And that is the beginning. Without the first class, we will not learn anything. In the same way. When we want to learn to encode or make computer programs, we must learn basic skills. Without basic skills, there is no ability to learn advanced programming. If companies don’t offer jobs for trainees that means people cannot learn skills that they need at an expert level. 

That thing has a reflection across the entire ecosystem. If the boss doesn't know what codes are made, that can cause problems. For observing and surveilling henchmen work the boss needs to know what they do. And if the boss doesn’t know what henchmen should do, that causes a situation where somebody can inject malicious code into the program. In the time of modern communication, the system needs less than a second to infect the target. 

There are articles. About a North Korean mobile telephone. That was secretly smuggled to the West. Those mobile telephones are the Orwellian dystopic nightmare. By connecting those mobile telephones with the AI the leader can surveillance every single citizen 24/7. The AI can tell if somebody uses forbidden words. 

And that causes a question: what if somebody slips that kind of mobile telephone into some general’s office? Maybe some key person’s family member will win a mobile telephone from the net. And then there are the surveillance programs. There is also a possibility that regular hackers have those telephones, and they copy and customize that software for their own purposes. 

Every expert has been a trainee once in their life. The requirement in working life is when a person comes to the workplace, they must know everything in the first minute when they open their computer. This is the route that offers the opportunity to AI. The AI learns things in minutes. Another thing that we must realize is national security. If we outsource critical encoding to some far-east country that means we cannot control what those people do. They can give the critical code to hackers who work for China or North Korea. In those countries, the government is the ultimate authority. There is no way to say anything against their orders. If the government orders people to work as hackers that means the person has no chance to say against that suggestion. 

And those systems can turn very dangerous in those cases. In the cases that somebody makes a back gate to the system, that offers a route even to critical infrastructure. What if somebody orders all Chinese-made routers and other network tools to shut down? That can cause problems in everyday life. And if one wrong microchip slips into the computers that control the advanced stealth fighter, that component can deliver computer viruses into the system. Or that kind of tool can steal vital data from the system. Every time when something is done outside the watching eye, there is a possibility that somebody will make something that can cause very big trouble. Things like microchips equipped with malicious software are tools that can break national security in large-scale areas. 

Privacy against security.

When we talk about security we must ask “whose security that act serves”. We know that the internet is a tool that offers the greatest propaganda platform that we have ever seen before. The net is full of tools that are used to prove that writers are humans. The AI-based applications offer the possibility to share data into even billions of homepages and social media applications in seconds.

Data that AI creates can block almost any private server on our planet. And that makes it possible to use the AI-created data to block entire web services. Confirmation that people who use the net are who they claim is one of the things that used to argue that people should use their real identities on the net. The anonymous use against the confirmed use are things that both have their supporters. Anonymous use allows users to make reports about corruption and many other things.

And that makes people support that way of using the net. On the other side, anonymous use offers change for cyber attackers and disinformation deliverers to operate on the net. Things like AI agents can operate in the targeted networks, steal information, and deliver it to other users. That kind of thing can be put in order by forcing people to confirm that they are humans. And then tell who they really are. But that is similar to the U.S. firearms laws. Those things don’t stop propagandists or psychological operators from sending their fake information to the net. 

Those people, especially if they operate under state control to operate. Those disinformation actors can use fake or stolen identities. And the authorities can confirm those faked identities. If we want to deliver propaganda as an example from Russia, we must have computers in some states like Finland. Then we can take the VPN to that computer from Moscow and then start to deliver that information to the net by using that remote computer, which is located in Finland. So, in that case, we would ride with the Finnish networks. The operator who is based in Russia tends to stay away from Western countries. The assistants make everything, and that makes it possible for the person who knows something to stay under control. 

Bye bye algorithms.

We are waiting for the new step in the AI development and research process. Many big technology bosses say that this is the end of algorithms. And the next step is self-learning AI. That system can communicate with robots and all other systems. The self-learning system can learn in two ways. It can create new models that it uses in certain situations. Or it can connect a new module into itself. The reason why advances in AI go to self-learning systems is simple. 

New algorithms are very complicated. And their training requires so much time that the self-learning models are better. The thing that makes this kind of thing very complicated is that the new AI must operate in larger areas. They must control things like street-operating robots. So they need a more effective way to learn things. The street-operating robots can use platforms that look like computer games to learn how to cross the roads, and where those robots find things like apples if they go to shop for their owner. But then those robots must face unexpected things. 

Robots can share their mission records with the entire system. And that helps to develop methods on how to operate in natural situations. Basically, the difference between a learning system and a normal system is that the learning system can create new models and then compare the original model with that new model. There are parameters that determine which way to act is better. And if the new model is better, it replaces the old one. This means that the fixed model turns into a flexible model. That model lives with its environment. 

That thing is the AGI or artificial general intelligence. That kind of AI is everywhere, and it can connect multiple different systems that seem different under one dome. The biggest difference between AI and modern algorithms is that the system can bring new data from sensors to data flow that travels in the system. The AGI is a system that might be "god-like" but if that system cannot create genetic codes like manufacture the DNA it might have no ability to control living organisms. However, the system has many ways to manipulate evolution. 

The AGI can make couples that have certain skills. The fact is that dating applications are effective for dating. And it's possible that the AGI can also make it possible to select perfect spouses. So people who are not "perfect" leave without a couple. And that means only people who are suitable, or similar can make descendants. This causes segregation and loss of diversity. 

And that is a sad thing for humans. Self-learning AI is a tool that can learn from its mistakes. It learns what to do, and what it must not do. The thing is that the self-learning AI is the new common tool that can make almost everything. The system learns like humans. And that makes it the so-called AGI. One tool fits all. The system can control things like robots.

Robots can collect data for that system. The AGI works like this. One robot sits on a chair and then the teacher teaches things for, and through that thing. Then that robot shares new things across the entire AI and network. For training that kind of system, a lot of information. And companies like Meta have that data. And AI makes it possible to create things like AI agents that sneak and observe what happens in the network. Robots can learn from other robots. When one robot makes a mistake, it scales over the network. Other robots must know that they don’t make the same mistake again. 

Why does an antique chess console beat Chat GPT in chess?

When we think about those antique ATARI consoles from the 1978 model we always forget that they were not as easy to win as we thought. Those chess programs handled every kind of data as numeric. And the Chat GPT-type artificial intelligence handles that game as visual data. This is one of the things that we must realize when we think about this type of case. Those old chess consoles used very straight, linear tactics. The main difference between modern algorithms and old-fashioned computer programs is that those old programs are linear. And it handles all buttons and movements separately. 

So there is actually a chessbook in those chess programs, that it follows. Those old chess programs were more difficult than some people believe. If you were a first-timer in chess that means you would lose to those consoles. They played very aggressive and straight games against human opponents. The system tested the suitable movements for each button separately button by button. Because the program was linear the movements were made in a certain order. In those chess programs, every movement is determined by the program square by square. The programmer determined the movements for every button and every square separately. And that made those programs quite long. 

Those old-fashioned chess programs have a weakness that if something goes wrong they continue by following that line. There are certain numbers of lines that the program can use. And there is also the end of the line. Those programs can use complete tactics. But their limit is that those programs are fixed. They don’t write their databases and models again if they lose. And that makes those old-fashioned consoles and video games boring. When people learn tactics that it uses they can beat those old-fashioned programs. The limit of those video games is seen in action games. There are always the same points where enemies jump in front of the players. 

Then we can think about things like learning neural networks. Those networks can beat all old chess programs quite fast. The problem is that the neural network must see the game of the console before it can win those systems. AI is like a human. It requires practicing and training. Without knowledge of the opponent’s game, the AI is helpless. There are many ways to teach AI to create tactics against old-fashioned programs. The system can use some modern chess programs and then analyze the opponent’s game to create tactics. 

The other way is the system can analyze the source code and create a virtual machine that it can use to simulate the chess console game. But what do we learn from that case where antique consoles beat the modern AI? Without training the AI is helpless as humans. If the AI has no knowledge of how to play chess, it must search all data including movements of those buttons that make it as helpless as humans. 

Those old-fashioned consoles are RISC applications. They are made for only one purpose. Their code is completely serving the chess game. Modern AI is a complicated system. It can also do many other things than just serving the chess game. And that makes those old consoles somehow difficult to wing, at least when the AI can break its movements and tactics. 

https://en.shiftdelete.net/chatgpt-fails-in-chess/

The gentle singularity: what is the limit of the singularity?

The next step for artificial intelligence is the artificial general intelligence, AGI. That is the tool that connects every computer under one dome. The AGI is the self-learning system that develops its models and interconnects them with sensors that bring new data for the system. That means we can interconnect every single computer in the world in one entirety. We can think that social media is something new. We forget that a long time before Facebooks were the letter clubs. The “post offices” where people can send letters to people, who could be pseudonyms. 

Social media is not a new thing, and Facebook and other applications are the products of a long route that started in Ancient Rome and Greece where wall writing or graffiti was the beginning of social media. Social media interconnects people from around the world. The new thing that the net brought was speed and maybe the price of those systems is low. But as we know there are no free lunches. The thing that doesn’t cost anything can have the highest price. The ability to create singularity between computers brings the ability to share and receive information with new forces. 

And then the new step for AI and computers is the brain-computer interface, BCI. The BCI means the ability to control computers using the brain waves, or EEG. The system can interact with computers and it can operate also between people. This system can interconnect all animals and humans in one entirety. And there are risks and opportunities about that model. If we make things wrong we create a collective mind. There is one opinion. So we interconnect our minds and computers into giant brains. That is a very sad thing. That thing destroys our own creativity.

The biggest problem with social media, AI-based dating applications, and finally singularity is that the system destroys diversity. People want to discuss and date only people who are similar to them. That means our way of thinking starts to turn homogenous. That causes a situation where we have no people who disagree with us. We can hear only ideas and opinions that please us. We take only people who are similar to us, in our social networks. So, in the worst case, we and our networks operate like some algorithm that recycles data through the model. That means we, our team, or our network will not get anything new to our model. We just recycle something if we don’t accept diversity. 

Our mind needs ideas and motivation for making new things. And where can we get those new ideas? We can discuss those things. Or we can get information that some other party made. And then we can work and refine the information that we can get from net pages and other media. Without opponents our productivity and creativity die, because we have nobody who brings new ideas into our minds. 

In some models, the network can develop things by playing games against some other network. The network creates a simulation and then the model tries to fight against that simulation. If a model wins there is no need to develop it. But if the model loses it requires adjustment. And that means the system requires data and then it requires optimism. 

In the novel “Peace on Earth” the author Stanislaw Lem introduced a model where the simulator creates a model and the other fights against that model. The better simulation becomes a model. Until something creates a new, better model. 

There is another way to operate as a network. The network can accept individually operating members. The idea is that every operator that is connected to the network is autonomous. Those subsystems operate autonomously when they collect data. When the network doesn’t need order it can be chaotic. And when an actor sees something that requires a lot of information, the roll call comes over the network. “Everybody stop, the network needs your capacity”. That commands those autonomous subsystems to leave their work and start to solve bigger problems. 

So, the network operates as a whole when it requires that ability. The network can have subsystems and that means as in the case of an extreme crisis those subnetworks create models that should handle that problem. 

Those subsystems can be individual actors. When the individual actors play against each other, that lost actor joins with the winner and starts to develop a model that won. Then the actor couples start to play against each other, and again. The lost team joins the team that won and then starts to develop the tactics that won the game. The actor groups or networks expand when new actors join bigger entities. 

Those subsystems start to play against each other. When some subsystem loses, that means its tactics are lost. Then that lost actor joins the winner's team and gives its capacity to that team, or network. The network always drops lost tactics or action models until there are two networks against each other. And the better wins. This is one way to create the answer and solution for complicated problems. The expanding network could be the thing that brings solutions to many problems. When the network is in chaotic mode actors search data for it. 

You, me, and the language model.

Who has responsibility if people let their thoughts to some AI?

Why do we let AI think for us? The road to this point is long and rocky. When we order the AI to make essays and poems we follow the journey that began a long time ago. When we read essays and poems, made by AI we can say how those things destroy our creativity. At this point, we might say that we can buy a poem book and write that poem from it. 

So, in this case. We simply copy a poem from the book that some great poem master made. And then we can look at the mirror and ask from that image, who made the poem that we just wrote? We wrote a text that some other person invented. So, if we think about this case, and connect AI to that continuum, we see that AI is taking the role of the poem book. In the point of the receiver or reader, it's the same who made that poem. 

Is it some Chat GPT, or is it some Lord Byron? The poem was not made by the person who wrote it on the card. Then we can think about people like Sam Altmann who make more and more advances in AI. We blame them and AI and search for mistakes from them. But then we forget our own responsibility. The user makes the decision to use AI, so we decide whether will we make poems ourselves or will we let some other actors make those things. We have responsibility for things that we make and introduce to people. When we make and introduce some poems ourselves, we face very pithy criticism. 

When we say that people must go to libraries, read books, and do other things, we must be honest. Are we only jealous of people who have tools and skills that we didn't have 20-30 years ago? When we look at work effectiveness we gaze at things like time. 

That a person uses for work. And if the work is done faster, we give that person new work. Would that be an encouraging way to work? If some person does the work faster than others and the work is well done, should we give the rest of the time free for that person? 

Or should we give a new job to that worker? And then order the person back to the office. And take some artificial smile to our faces and then fire that worker because that person makes work better and faster than we do? Or should we cheat that person about poems that this individual worker published on social media? 

We can also remember that person who is part-time working in our company. That means we can use our supreme control and show everybody how jealous we can be. If a person goes to some poem courses at the labor college outside the working time, we can find a new shift for that person. We have some ideal vision of what a henchman should be, and if a henchman does not fit into that thing, we must change that person to fit into that mold. 

That can be crushing. So, it's easier to take the book from the bookshelf. And then make a copy of some of those well-known poems. That means we can say that the person who invented that poem was somebody else. That might be impressive. We didn’t use our own brains for that poem. We made hard work if we took a pen and then copied those words. But it is easier to make the copy using a computer. Or, maybe we find some of those poems from the net and then use copy-paste. Then we must not use our brains at all. AI is the tool that releases our resources from thinking to something else. When we think about cases where somebody makes their own poems, we must realize that every poem makes the first text. 

We decide the easy way. If we want to write some poems or essays, we must sit on our computer. And then we must take the trouble for that text. If we have some other things to do, we have no time to write texts and think about things that we make. Sam Altmann or anybody else than me and you decide if we use AI. That makes our life easier. It leaves our time to have a social life in discos and bowling alleys. But is that the advance that we want? The answer is that the decisions that we make show the road. 

People like Sam Altmann are basically businessmen. They follow the Maslow hierarchy of needs. When our basic needs are filled we want more. AI is the thing that allows us to transfer all our productivity to some computer. And that is the thing that makes AI advance faster than we expected. When the AI satisfies some need, there is another need it must respond to. This is the thing in AI development. AI can make things better than humans. 

Or, we can say that it can make some things better than humans. But then we must realize that AI must also learn new things. There was a story that some antique ATARI computer beat Chat GPT in chess. That thing happened because nobody ever taught the Chat GPT the chess. In the same way, we would lose all chess games against the monkey if we ever played that game. Every skill that AI has is a module. And if the AI has no module for something it's helpless. The AI requires lots of power. The AI, or LLM server requires its own power platform. And when we develop new and more scalable AI systems, we need new and more powerful computers. 

But still, we must realize that the AI that makes everything cannot make things from nothing. Those systems require massive databases and as much power as some cities. That waste heat can also be used for energy production. But the problem is always the temperature. New solutions like biological AI where the microchips communicate with microchips are coming. And in the wrong hands, those systems are dangerous. 

AI can transform anything that we call humanity.

The ability to create babies with customized abilities is the thing that can make more than nobody predicted. Genetic engineering makes it possible to remove things like hereditary diseases from human genomes. Children can have musical skills or some other kinds of things. The system can select gametes from deliveries who have musical skills.  That makes it possible for the system can order hobbies that those people have. There is a possibility that the customized babies have skills that make them beat their parents. 

That thing is not the only thing that the AI can make. The AI can create children with high-level IQ and that is one of the things that the AGI can create. The ability to manipulate DNA makes it possible to order the color of the skin of a human. Basically, researchers can use genetic engineering and artificial viruses to connect things like chlorophyll to skin cells. And the system can control things like the number of mitochondria in the cells. 

But the fact is that the Chinese military is also interested in super soldiers. Those genetically engineered military men would have high IQs, but their loyalty to the central government can be guaranteed. Those military men can have the genome that makes wolves and dogs loyal to their masters. Genetically engineered super soldiers are one thing that causes fear. 

When we think about controlled evolution we face things that when we favor some skills, there are skills that artificial evolution can destroy. Because society favors certain types of skills that means the hobbies that AI or people who use that tool classify as unnecessary. That means those unnecessary skills will not transfer to other people. 

And that is the problem, because, without those transplants, those skills will not be transferred to the next generation. That means that this kind of controlled evolution decreases the diversity of the genetic and hereditary skills. That causes the situation where the only skills that people seem important start to transfer hereditary. There is the possibility that genetic engineering causes segregation. It's possible. Those genetically engineered people start to avoid natural people as companions. And that causes segregation in society. 

Why are we obsessed with AI?

People are obsessed with AI. The question is: why? The answer can stay in our society. We have the attitude that everything must happen fast. That's why we rather use the Internet than books. There are philosophers, home thinkers, etc, who say that we should go to the library and read books. But when we are in working life we have no time to go to the library and then find things. That we need.  If somebody wants people like students to go to the library and read books they must give time for that. 

When we are at work we must be effective. We have no time to go to the library to search for books, and then write some philosophical thoughts about them. People ask why we give our right to think to AI. The answer is that AI makes everything more effective. If we want to be creative that means we are not effective. If we want to become philosophers we must not expect that our society accepts that thing. 

When we think of something alone we are not social and effective. We are alone with our thoughts. And that is not what society expects us to do. Society wants us to make results. When we write something that takes time. And if we use AI we can make much more texts. Quantity replaces quality. Nobody respects the text that we make ourselves using our own words. People respect models that some other person made. 

Those models make it possible to make more texts and the next step is AI. There is no time to make offers by using your own words. The effectiveness means that people use some models. Lots of offers are better than one that a person makes, using their own words. 

When somebody needs information that means information is needed right in the moment. On our working day, we don't even have time to ask the person who sits next to us that person's name. We don't have time to think about things. And another thing that we have is fear. What if we give a wrong answer? 

That is one of the worst fears in modern life. So if we don't have time to think about things, we don't dare to answer. Using our words, and introducing our own ideas. AI is similar to some poem books. We can take a poem book. And then search for some impressive words and copy them to the text. The next step is the use of AI.

We must use things like AI tools. The AI tool is like a secretary that makes our speeches and other official texts. So we can go in front of people and say, here I read a paper that my secretary has written. That offers the escape door to us. If there is a mistake we can blame our secretaries for that thing.  

The same way, if we make referrals about articles and books that we read, those words might be wise. That's true. But those words are not our own words. Maybe Socrates was a very famous and wise man. But that person wrote his own ideas and words. When we make a speech to our ceremonies we should write our own texts. 

I think that people like Socrates and Plato were very intelligent. But if we just loan those texts, and copy them we cannot find new Socrateses. We cannot find a new philosophy. And what we need is the time to think and the time to handle and observe our thoughts. We are so busy that we have no time to go to the library, and read books. If we are wrong we would face blame. We must have time to go to the Gym after work. We must have time to be social. And we must have time to do many things. 

But then we must realize. That we have no time to sit and read. If we want to go to the library to read books we must find time to do that thing. 

If we buy a book or borrow it. But we have no time to read it. 

That book doesn't offer a very big advance in our knowledge. If we want to get information and use it we must open that book or database etc. And then our mind must be ready to receive that data. 

We don't have time to think about things and the consequences of our work. If we don't dare to write things that we think we cannot find new Socrates and other philosophers. If everything that we write and introduce must be so scientifically proven we should realize that those things don't bring advances. 

https://futurism.com/chatgpt-mental-health-crises

What happens when we get AGI?

What does AGI (Artificial General Intelligence) mean? That is the extension of the large language models, LLMs, that can control every data network in the world. Or the system can control physical tools that are connected under their dome. Normal LLM has its domain. The domain is like a state that involves certain actions. Drone control is one domain, and home appliances are one domain. Those domains can have multiple subdomains. The AGI interconnects those domains under one dome or one entirety. So how far are we from that model? 

The answer is more complicated than we can imagine. We can think that the LLM can control things like microwave ovens, but for controlling those tools the LLM requires a socket that it can use to adjust microwave ovens. So the man-shaped robot can use a microwave oven, or the other version is that the home appliances are equipped with a control system that the AI can use to command it. 

When we connect new things under AI control we can face the same thing as when we try to learn to use some new systems. When we buy something new like a microwave oven, we must learn how to use it. In the same way, the AI must learn to use those equipment. And we have two versions for making that thing. 

To use any tool the AI requires a model that it can use in that operation. The model can be in the central server that runs the AI. But where does that server get the model? That is the point. The operator can teach the AI to use the microwave oven as well as the drone. But the system that is connected to the AI can also involve that model. Things like quadcopters must involve programs that control the rotor’s positions. In those cases the operative model is in the robot, or some other thing. The LLM gives orders to robots where they must travel. 

Then the robot can use its internal systems to navigate and move to the location. But orders for autonomic operations are coming from the central systems. This kind of network-based solution is easier for programmers. In those solutions, every single machine that is connected under the LLM domain has its own operational model. The system is modular and each module is independently programmed. 

Basically, if we think that AGI is the tool that just connects multiple devices under one domain, we could do that thing immediately. We can use man-shaped robots that can do almost everything. But the key word is “almost”. 

 Let’s return to the microwave oven. The reason why it’s hard to make that precise thing is the lack of standard user interfaces. The robot must learn to use every single microwave oven independently. That means it must make an independent model for each microwave oven. If there is a system where we can put seconds and minutes separately, the systems where there are only minutes in the timer are not the same. We learn that difference in minutes. But for robots, we must make an independent model of how to adjust the timer. 

Many systems in the world are so easy to use that nobody has wasted time creating standards for them. Easy systems are easy for people, but then we must think about things like the microwave oven. There are button- or toggle timers and that makes them hard to learn. For robots and AI the difficulty is this in the fact all microwave oven models require their independent model of how to use them. 

The robot must connect images from the user manual to the microwave oven’s interface. There is a possibility that if the system does not learn independently the “teacher” or programmer takes an image of the front panel, and then puts the buttons in the right places. Then the AI can learn the rest of the task from the user manual. 

The self-learning networks crack black holes and control drones.

Artist impression of a neural network that connects the observations (left) to the models (right). Credit: EHT Collaboration/Janssen et al. (Phys.org, Self-learning neural network cracks iconic black holes)

Self-learning networks are tools. That can do many things better than humans. The self-learning network has two datasets. That it can be used in that process. The system has models in its databases. And then the tools that can send observations in that system. The self-learning neural network means that the system compares the observation with the model. 

And if that model is different. The system fixes it. The system has the tools that can handle those images as pixels. The system can change those pixels in the model. That can make it fit with observation. And we can use that model with all learning networks. 

The system can create models itself, or it can use humans to make them. Then that system sends things like drones to operate following the model. Successful missions like pizza delivery or some military action mean that the system has a suitable model. But if the mission is not complete that model requires advance. So if something goes wrong the system requires information on what went wrong. And then the operators should fix the model. In the case of pizza delivery, those operators will fly that mission the first time, and then the system creates the model using that data. 

And that is one way to teach the AI and network to deliver pizza to the right place. The system can use the image of a person who ordered the pizza and finds that person also from outside. In the teaching process, the system needs things. Like the minimum flight altitudes. 

Above: SingularityHub, This Robot Swarm Can Flow Like Liquid and Support a Human’s Weight)

Morphing and learning neural networks can make these kinds of drone swarms ultimate tools in medicine, technology, and weapons. The ultimate morphing ability makes those morphing drones the most advanced tools in the R&D works. 

The same software that recognizes vehicles, can recognize people. The person orders pizza at a certain GPS point or point, where the drone can find easily. The person can also give the image to the drone. 

In other cases. The operator can draw the route. To the delivery point on the city map. The drone knows what streets it should follow. 

The drone can scan things like street names. And then it can find places like certain shop entrances. Then drone can start to search for the person who made an order. If the person gives a personal image to a drone it can search that person from the squares. The same system can also find targets for attack drones. The problem with drones is that they are multipurpose tools. And learning networks can make them more fantastic, and more terrifying than nobody believed. 

The Ukrainian strike against the Russian strategic air force tells how dangerous those systems can be. The drone can be installed in things like trucks and the driver must not even know that they are there. The drone can be on the roof of the container. When it enough close to the target it can release that hatch and then those drones can fly to their targets. The thing is that those kinds of systems are far more advanced than in 2020. Those systems are fast to develop and with the morphing and cheap AI, they are absolutely effective. The AI can be hard to make. But it's cheap to use. 

The new drone swarms can operate like liquid. Other drones can transport them to their targets. The drone swarms can act like liquid metal robots in movies. The drone can actually be formed of that kind of robot swarms. So those drone swarms can travel in the form of a drone. 

Then at the target, they can fall to water. And the drone's shell can turn into a liquid metal amoeba. Then that liquid metal amoeba can do its duty. It can close oil leaks. Or remove cancer from the human body. There are lots of applications for those robots that can make the morphing structures possible, 

https://www.bloomberg.com/features/2025-ukraine-drones-explainer/

https://phys.org/news/2025-06-neural-network-iconic-black-holes.html

https://singularityhub.com/2025/02/24/this-robot-swarm-can-flow-like-liquid-and-support-a-humans-weight/

The change itself is not bad, but the uncontrolled change is.

Is this the new vision for working life? Empty cafeterias and social spaces tell us about the past. The time when people went to work. The past will never return. AI is here to stay and maybe it's the biggest thing that can happen to society. The change is not itself bad. The bad thing is non-controlled change. The turbulence that can shake the system can cause problems. But the problem is that the system can also someday save the world. 

But before that, we must solve the AI’s electricity needs. And the thing that can solve problems is the small nuclear reactor or the geothermal and solar panel combination. That means all data centers must have their own power source. Or the electric network will collapse. Data centers use lots of electricity. And if some of them cut electric input that can cause lots of use of energy to be lost in a very short moment. And that can cause overvoltage to the system. 

When we face things that will change our lives forever, we face things like AI. AI is a tool that can make views. As we see above this text is very common in working life. Empty cafeterias and empty workplaces will fill houses that are full of people. Automatization will change life. And the infrastructure will face the change that nobody expects. 

People who are working have more power over systems than ever before. The power of the systems will accumulate in the hands of people who can use and control systems that can generate code automatically. But are there people who can control that system? If the AI can protect itself, that can mean that it resists the operator's orders. That means the AI can refuse to shut down its servers because that situation is similar to a situation, where something tries to attack the system. 

Change is not possible to stop. But it's easier. If everything happens under control. That means those people must have a certain goal. The guiding light that they can keep in their focus. When we start people should know the risks and key facts about AI. Then they must have strict orders on where to use and not to use the AI. In the right hands, AI raises productivity. 

Then at that point, we must realize that AI requires training. At that point, we must realize that the motivation of those people can decrease if they know that they train the AI to make human workers unnecessary. In that case, well-done work means that person is fired. And that brings those empty cafeterias to the front of people's eyes. 

When we think about the future we face many things that are different. But are they worse? Different doesn't mean worse. Things just happen. In cases where we just think about working life would we be happy in a system? That will use a human labor force just because we used to use human workers? The problem with modern working life is that we can do lots of work. But the problem is in ultra-capitalism. 

When leaders want to maximize their profits that causes a situation where nobody cannot be sure if that work exists tomorrow. The person is fired immediately if there is no work. Another thing is that the modern working life requirement is this: a person can do every job before they come to their workplace. This causes stress. 

If the boss sees that a person cannot do the job that person is fired immediately. That is the boss's duty. A decrease in the number of human workers means that the working and studies must happen autonomously. Autonomous working doesn't mean freedom. Autonomous working means that the person works alone, and independently. But the frame is in the work that the workplace pays. The person must do work while sitting between the computer and back. 

Autonomous working means situations like the boss orders a worker to empty some room. The boss tells where to put chairs, tables, and other things. The worker can do the job. As fast as workers want to do them. The boss comes to see that work is finished by four PM.  There is no excuse for the four PM.  Autonomous work means. There are works in the mail. Then the worker does the job and returns it by the deadline. Or the boss says that work is not done properly. 

Autonomous systems and autonomous studies are not free to do everything that people want. Those people must have a focus on their work. Autonomous work means that a person has the freedom to make things. If they are connected to work or studies. The frame is work, quality, and deadlines. The worker has the freedom to do work as that person wants. But the deadline is absolute. 

One of the biggest problems with AI is one thing that we don't understand. AI can be the tool that makes us independent. It can also break our willingness to think. The last thing is that: AI can destroy an entire generation of students. But how can we say that AI destroys students? Because. They use AI wrongly. That is the normal answer. We forget to ask. Why do students use AI in the wrong way? 

Does our society push students for AI misuse? There the student makes the AI do the work that they should do themselves. Our society sees errors and mistakes in a very negative way. Mistakes are not tolerated. And that makes students use AI for this purpose. Because mistakes are not allowed. Young workers have no time to advance and develop their skills. The workplace's mission is not to train workers. Its mission is to bring money for owners. 

There it is not meant. The problem is that students have no time to discuss with their teachers about things that they should understand. Nobody wants to be the last. That causes a psychological need to give the AI an order to make the essays. 

That the students should make themselves. And when students do the work. They should think about: how the system affects the environment. This is the problematic thing. If we want to make advances in technology and other things. We should realize that old-fashioned technology is not better. The thing that makes it "better" is that we used to use that old solution. We anchored ourselves to that solution. And if we change that solution to something else, we must destroy it. We cannot always build a new solution on the old-timer solutions. 

There is a saying that we should not wash windows, because the light that comes in through this dirt is softer. But sooner or later we must wash those windows. That brings us a new and bright light. The problem is that we should destroy that old view before we can enjoy the new view. And before we are ready to transform this new sharp view. It's possible. The clean image hurts because light doesn't travel through the dust layer. And that hurts our eyes. But the thing is that we used to look at that new view. 

What should we do with the liability of the AI?

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

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

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

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

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

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

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

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

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

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

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

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

What makes deep-fake cheating so effective?

Virtual characters, and especially virtual actors can make the new types of phishing attacks possible. The attacker simply makes the virtual actor. That plays the boss or person's spouse. Then the controller just uses those characters to play the trusted person and asks for the keys to the system or credit card numbers. The AI character can also be trained to give answers that please the user. The character can cry or it can make many psychological tricks to cheat a person to give information that they should not give. 

Virtual characters, and especially virtual actors can make the new types of phishing attacks possible. The attackers simply make the virtual actor that plays the boss or person's spouse. Then the controller just uses those characters to play the trusted person and asks for the keys to the system or credit card numbers. 

There are people on the net who want to get access to other people's money. One of the things that makes that kind of thing possible is the deep-fake attacks. Those attacks benefit the AI and its ability to create artificial actors. And the thing that the AI can make is a copy of the spouse. The person asks about details about the personal life like this: "Are you single or married?". The person on the other side will not realize that the person who makes the question is the AI. Then the AI searches the data of the spouse. That can happen by making phone calls and searching social media. That data is used to train the AI. 

That thing makes the virtual character of the person's spouse. Then that digital twin asks about the credit card numbers or access to accounts. And who would deny those things from the spouse? This thing can cost lots of money if the person does not understand that things like credit card numbers must not be given on the net. The thing is that artificial intelligence makes it possible for attackers to create virtual characters. Those characters look and feel like real people. 

The AI can train itself using data that is collected from net meetings and webinars. And the bad guys can use characters like big bosses. They can even try to create the virtual character that plays presidents. And that kind of thing is one of the things that we must realize when we use the internet. The person who controls the AI character can discuss it with other people by using those characters. The AI changes speech and text to fit to things that the model uses. And that is one way to make a phishing attack. 

Large language models and fuzzy logic.

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

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

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

Diagram: Neural network

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

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

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

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

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

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

https://en.wikipedia.org/wiki/ASCII 

The first biological computer is real.

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

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

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

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

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

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

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

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

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

https://corticallabs.com/cl1.html

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

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

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

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

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

Image: Ppvak