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

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

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

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

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

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

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

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

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

How to teach AI?

Morphing neural networks are very fast tools to drive advanced AI-based systems. Those complicated neural networks can involve thousands or even millions of microchips. That allows them to combine data from memory and sensors with extreme accuracy and speed. Teaching AI to operate in a real environment is a complicated process. And the thing is that the morphing neural networks allow the network to drive multiple missions at the same time. 

How to teach AI? Computer memory and microchips are interesting tools. They are very accurate, and that sometimes makes AI training very complicated. If we want to make an AI that recognizes humans, we are in trouble. If we want to make an AI that recognizes certain people like some famous actor, like Tom Cruise, we can make that thing quite easily. We must just have images that are from all angles. Or we must ask that person to put their head into some certain position. Then the system can compile pixels that the CCD camera inputs into the system with images that are in the system memories. In the first case, the neural network can give fast recognition if all the CCD pixels can give an individual data input to the neural network. The system compiles all images that are in the computer's memory and then the system can say, that the person is Tom Cruise.

 If the system can compile all images that are taken from around the faces from different angles. That system makes recognition very fast. But then we face the problem: we know that all people are not Tom Cruises. We must start to globalize face and body images to computers so that they can tell that they see humans. So we must take one step back when we want to recognize that an object is human. 

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When the computer turns a certain person's image to match with species. Or globalize that image with humans as a species the system must remove accuracy.  That means it must remove pixels or replace them with grey pixels and then it can compile that silhouette with a silhouette that is stored in its memory. 

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Normally we recognize persons in certain series. At first, we see characters and then we recognize that character is human, and then after a couple of steps, we recognize that person. But then we must make the AI that recognizes humans and their gender. That means we take a couple of steps back from the individual to global things. We must realize that there must be some common things, the lowest common denominator that we must find in people, is that it recognizes humans as a species. That thing is called fuzzy logic. In precise logic, we must put every person's image on this planet to AI. 

That system gives the personal data of every person that it sees. But that kind of thing makes the system heavy and slow. Precise logic is sometimes easy to cheat. Simply changing glasses is sometimes enough to cheat the systems that use precise logic. There are systems. That must not completely see the match to make an alarm. In those systems certain percentage of the matching pixels causes alarm. There is the possibility that when the computer recognizes only humans it takes images of humans, and then it removes details. When it removes pixels the system combines the image with silhouettes. That is stored in its memories. 

https://www.quantamagazine.org/how-can-ai-id-a-cat-an-illustrated-guide-20250430/

The documented life of professors and creative persons

http://kimmontaidearvioita.blogspot.com/p/one-good-day-to-think-about-professors.html

Kimmo Huosionmaa

One good day to think about professors and their backgrounds. The life of the professors and creative persons is always very well documented, and one of the most interesting thing about scientists and professors is, what kind of numbers would that person get from the schools, where professor have been before entering to University. That could help authorities to track new Einsteins and Julius Caesars from the nation, and then turn their way of think to the right way. Of course, sometimes professors made so-called "back office" work in some secret societies, and that was also marked in their homes. When I write about professors I sometimes use the person as an example of this kind of professionals, and if you think something else, you can visit that museum, and look about those things.


 When we are looking the pictures of the museum of the Johan Ludwig Runeberg at Porvoo, we might see some strange things with those pictures, and one is that over the heads of the statues are placed some portraits, where seems like be some strange landscapes, what seems to be from some other land, and maybe they are portraited by the stories, what that man told in old ages. Or maybe the person, who made interviews used hypnosis to get those statements from Finnish national poet. There is also the combination of the portrayed thunderstorm and the head sculpture of the poet.

The way to make that sculpture is very energic, and the sculpture seems like very powerful and frenzy man, who yells to the thunderstorm. When I was looking at that combination, what might be made by accident, I remember, that there must be very strong will to become the great artist, and the thunderstorm might mean, that this person has the very great power to create new things. Creation of the new and the portrait of the thunderstorm would also mean that this kind of powerful person might have epilepsy. The epilepsy is always connected with the productive way of think, and individualism.


Epilepsy is actually like an electric storm in the head of the human brains, and this syndrome has been connected to geniuses like Julius Caesar and many other very productive persons. And do you know that Caesar was also the poet, who wrote many texts? When we are thinking about the paintings in the living room, could their strange combination or selections mean, that some kind of writer or poet must have interests about the things, what are found in that place? The persons like Runeberg were very intelligent, and also they were made productive work as the poets.

So those persons were very good examined by other people because the state wants to get more of that kind of persons, and when they chose poets, those men thought about Caesar. They wanted something greater, than just normal man. When we are thinking about the situation, that some professor would be coming from another land, there would be some connections for Runeberg. The six children would make that person more convicting, and they would tell to that person if somebody has asked some interesting questions.


When I'm writing this text, I, of course, use Runeberg's personality, because that makes writing easier than using some phrases like "professor" in every sequence of the text. The origin of professors was the very interesting thing because the professors family members were also intelligent. And if those persons would be grown in the environment, where the nationalism would be in the prime role, could those persons transformed as the military, or civil industry leader, inventor, or somebody, who would control the state and the things, what are done in the nation.

Picture sources:

All pictures in this text are taken in Runeberg's museum Porvoo

http://kimmontaidearvioita.blogspot.com/p/one-good-day-to-think-about-professors.html

The dance of robots

Kimmo Huosionmaa

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


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


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


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


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


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


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

https://crisisofdemocracticstates.blogspot.fi/

 http://crisisofdemocracticstates.blogspot.fi/p/the-dance-of-robots-modern-way-to-teach.html

Steiner and theosophical methodology

http://crisisofdemocracticstates.blogspot.fi/p/steiner-and-theosophical-methodology.html

http://crisisofdemocracticstates.blogspot.fi/p/steiner-and-theosophical-methodology.html