Why is turbulence so important?

"The sky depicted in Vincent van Gogh's 1889 painting, The Starry Night has been studied for its turbulent flow"(Wikipedia, Turbulence)

“In fluid dynamics, turbulence or turbulent flow is fluid motion characterized by chaotic changes in pressure and flow velocity. It is in contrast to laminar flow, which occurs when a fluid flows in parallel layers with no disruption between those layers”. (Wikipedia, Turbulence)

When aircraft or laser beams travel through the gas it pushes particles away from it. Turbulence forms in the border between energy or two materials that have different densities. Turbulence forms on the aircraft wings when some air molecules touch the wing. That forms the whirl and when the aircraft moves ahead it pumps kinetic energy to those whirls. There are vertical and horizontal whirls and when they cross that causes oscillation. When crossing whirls pump energy into each other they start to oscillate the aircraft’s material. 

The whirl can form only in cases where there is a higher energy wall between low-energy or low-pressure fields. The idea is that the outcoming gas cannot break the whirl. The whirl can start to grow until the outcoming gas or energy breaks it. If things like laser beams travel through the gas that forms two whirls around that beam. The whirl also delivers energy inside it. And it can remain until incoming energy breaks it. Or outcoming energy destroys that structure. We call that thing turbulence. If some algorithms that can calculate turbulence are possible that thing can make a big advance for hypersonic aviation and plasma research. 

Turbulence is one of the most important things that we must control and predict. Turbulence, or chaotic whirling is the thing that makes it hard to create hypersonic jet engines. Turbulence is the thing that breaks plasma entirely in fusion systems. When We think about things like ramjet and scramjet engines the whirl that comes behind the dam makes the pressure wave that makes the pressure and heat barrier that ignites fuel breaks the fuel-air mixture shape. The dam is the cone-shaped structure in the ramjet engine. 

And that limits the use of the ramjet engine between Mach 1 and Mach 6. It is not possible to create the structure that connects the ramjet and scramjet engines using conventional methods. (Maybe) There is a test of thin wires at the front of the scramjet. The problem is that those things make the low-pressure area behind them. They look a little bit like fish. And when those pressure waves impact that causes whirls in the engine. 

Ramjet

Scramjet

The thing that limits ramjet speed is the turbulence behind the ramjet’s compression cone. The scramjet engine uses a different shape in the cone. Researchers tried to connect those shapes. But that is very hard to make. So in some ideas, the aircraft uses different turbojets, ramjets, and scramjets. The aircraft will accelerate to Mach 1 using regular turbojets. Then the ramjet system ignites. When the aircraft’s speed is high enough the scramjet engine starts. That allows the plane to operate from regular runways. And it gives it an extremely high speed. 

There is a possibility to create a cone that pulls itself away from the front of the engine. The system can share that cone structure into four bites that the system pulls away from the ramjet, and then the scramjet acts as the afterburner for that system. If the system can remove ramjet's cone it makes it possible to connect the scramjet behind the ramjet. That system can make it possible to create aircraft that can travel with speed Mach 12 and higher. The rocket ramjet means the rocket engine that uses atmospheric air while the aircraft travels in the air, and when it jumps out from the atmosphere that system can close the iris at the front of the ramjet. Then the oxygen-fuel mixture will burn in the engine. That turns to using the internal oxygen source. 

However, we can also consider turbulence and nuclear fusion. The problem in the plasma is that there is forming empty space or a difference between energy levels. The problem is that the lasers cannot push plasma using the same force at all points. If somebody creates a laser that inputs energy to the plasma by using a beam that covers the entire plasma ring that would be a big advance. It is also possible to create anion plasma in the fusion system. And then the ions will inject that plasma ring homogeneously from the entire length of the plasma. 

If the ignition happens homogeneously in the entire plasma that could make the system more stable. The problem with that system is with magnets. The same system that presses anions to the ring pulls ions away. If the system uses laser points, it creates energy hills within the system, which form a situation where the laser beams make whirls inside the plasma. The problem is in the one or mono-polar plasma. 

Particles in ion plasma repel each other. And there are small points where the magnetic system cannot press that plasma with the same power. That point is the connection point of magnets. Those points cause waves in the plasma where those particles will jump away from each other because of electromagnetic repel. That makes space in the plasma ring. And that space allows the form of the whirls. 

https://www.quantamagazine.org/new-superdiffusion-proof-probes-the-mysterious-math-of-turbulence-20250516/

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

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

The AI can improve our business. Or it can turn it into a total mess.

AI can improve our business in many ways. But AI requires training. It requires that people will get to know AI. If we think that the large language model, LLM is the same as humans we are wrong. The LLM might look like a human, but the AI requires well-made orders. If our orders are not well-made or too imprecise the AI will not be effective. We must also realize that AI doesn't think. 

And that means that the AI doesn't know what the data involves. It might search for information from the wrong sources. There must be parameters about the sources that the AI uses to qualify data. That can be the same information in two or three trusted sources, that are independent of each other.

But we must realize that the AI has limits. The AI is as good and trusted as its users and makers are. That is the problem with the AI. There is one component that we must realize. The AI requires trusted information to be trusted. Without that trusted information, AI is not a good tool. When we use AI to create things like texts, computer programs, and other things, we must give precise and well understandable commands. The AI is like some programmer. It needs orders to collect data. If those orders are not well made the AI doesn't know what to do.

If we want to use AI for things like programming we must not say make me a program.  The AI requires information about what programming language it must use, then it requires things like the database connections or path where the databases are. The AI can make things like SQL injectors that transport data to the database faster than any human programmers. 

But when we want to ask the AI something we must realize that the AI is good at things that are common. If there are lots of sources that the AI can use it's a good and effective tool. But if there is not so much data or the data is behind some kind of barriers the AI is not so trusted. The AI is a language model in the middle of algorithms. Every algorithm is a skill that the AI has. And when the AI learns something it connects new algorithms to it. 

How good is AI? Or how can it improve business? Well, the fact is this: AI is an ultimate tool only if it gets valuable information. This makes it similar to humans. AI is neutral it doesn't have feelings, and it makes many things faster. The lack of feelings means that nobody cheats on AI using touchy-feely stories. The AI will check things that it must check anyway. But the other thing is that: the AI makes decisions by following certain parameters. 

Those parameters mean that the AI has a certain order for making a selection by using certain norms. Those norms can be training, experience, and maybe skills. So how to prove those skills? That is the problem. We can claim that we know some kind of programming language even if we don't ever see it. Things like blogs where people introduce some skills are things that can tell if the person knows something. And the AI can make analyzes from those texts. 

But they can also be made by using some AI. AI is a tool that can make our lives better. Or it can turn our days into catastrophes. The AI can search data for us. It's very good for that thing. But it requires the right and confirmed data. And that is a very complicated thing. When AI searches for things like common data or universal data that are very well known and we can check that data that is the ultimate tool. 

But when we search for things that are not so well-known or are otherwise hard to check, that makes the AI less effective. The AI is the tool that requires information. And without information the AI is nothing. AI is a tool that can make many services more effective. But then the same tool can turn everything into a dystopia, that makes some Stasi or Gestapo look like a child's play. 

The easy things in AI are gone.

"THE LOW-HANGING FRUIT IS GONE. THE HILL IS STEEPER."

CEO of Google.  Sundar Pichai is saying sayonara to the era of easy AI advancements.

The new AI advances require better hardware and better programming. With better education and studies. The thing that we should make is that. We should consider that the users of the LLM know where they should not use the AI.

 If people believe that the Chat-GPT is the tool that can act as a therapist or make everything. That they want. They are on the wrong route. We must call also other people to discuss with engineers to make decisions. Where do we want to go with the AI? The engineer who makes AI can use it. But does the normal person even know where they can use those tools? 

Normally we see either, positive or negative news about AI. But there are both sides in the AI. 

We can find many positive and many negative things in AI. The AI is like all other tools. There is the possibility to use them for positive and negative things. 

The human user makes decisions about where to use AI. AI is here to stay. It makes life easier. But AI is a fundamental tool. All tools need training and exercise so that they work like they should. 

Maybe the new campaign for citizens should be the AI driver's license that is similar to a computer's driver's license. However, the focus should be on the reasonable use of AI. The AI that draws bears who use computers is not the AI that searches tumors from X-ray images. And the AI that controls aircraft and drone swarms is far away from those two things. 

The Chairman of Google said that Easy solutions for AI are over. And we need new tools to make the new types of AI. But before we create any new types of AI. We must ask ourselves, what do we want? Do we want the AI that makes everything for us? The AI is not ready to think like humans. 

Another thing is that the AI is not qualified to work as a therapist. That means people should be informed of the limits of the AI. And the second thing is that the AI is not the tool that can make everything. If we want to use AI as a therapist that system requires lots of work. The AI requires certified parameters to act as a therapist. And do we want that thing? Do we want to transform everything into algorithms? 

Don't overestimate the AI, but don't underestimate it. More you know about it. More safer you are. 

Before we create new AI-based tools like algorithms or LLMs we must ask what we need, what we want. And what we can do. If we can make an AI that can work as a salesman or secretary for some boss there is a long way that the AI can operate as a therapist. 

It is the tool that should help humans. But it cannot make independent decisions. The AI has multiple limits. There are also dangers in that system. Sometimes the AI or intelligent chatbot causes situations where the people who have some problems harm themselves. 

If they use chatbots for therapeutic purposes. When we protect people's privacy we forget to ask, what the LLM should do if somebody seems self-destructive. 

The problem is that the chatbots like Chat-GPT and its competitors are not made for therapeutic purposes. There are people. Who use those creative chatbots for therapeutic purposes. And the fact is that those intelligent chatbots are not tested for those things. That means those systems can give answers that are not tolerated. And those cases mean. That we, or some other person expect too much about the LLM's and other AI solutions. 

The AI can make many impressive things like paintings or art by following text commands that the user gives. The newest tool is that the AI can make computer-game-type virtual worlds by following orders. That thing makes it possible to create a virtual model of cities and other places using spoken or written commands. 

AI can be a new tool for police work. The victim of a crime can use the AI to make the image of the person who made the crime. That makes the police artists' work easier. The same systems can be used to collect data from opponents' military systems or some other classified platforms. But those AI systems don't think. They just follow algorithms. 

The AI can change our way of thinking because it doesn't think. If the AI follows the page ranking the AI selects the popular homepages for making the solution. The AI just combines the text from different homepages. And if it selects the most popular homepages that can cause massive problems. The biggest problem is that toxic homepages are popular if we want to calculate only the clicks. And the clicks are the easiest way to measure how popular some texts are. The thing is that this can polarize information that the AI gives very much. 

And finally. Do we want the AI that thinks for us? Do we want to make a doctoral thesis using AI? That is the big thing in the AI. We can make many new things using AI. But are we going too far? When we go to some school. Maybe we want to learn something. 

But if some AI makes everything to us. That means we don't learn anything. So if we want to go to a situation where the AI makes a doctoral thesis and even thinks for us, we go to a situation where we raise the AI over us. When we cheat in cases like schools the only thing that we cheat is ourselves. The AI is the thing that requires training. The worst thing is to accept the thing, that AI cannot make everything. And it's dangerous to misuse it. 

https://futurism.com/the-byte/google-ceo-easy-ai-over

Networks make AI real.

Linux enthusiast Linus Thorwalds said that 80% of AI is marketing, and 20% is true. That is the normal relation in computer applications. The news about "doomsday AI": or, AI that destroys the world, makes AI look alike more fascinating than it is. But the fact is that the AI and its ability to generate the code should cause re-estimation in data security. 

The neural network can attack systems with powerful methods. In neural networks. The AI can change the attacking computers. And their IP's all the time. The AI-based systems also make it possible to use dynamic IPs that make it harder to track those computers. The large-scale neural networks can also operate as the artificial general intelligence, AGI. 

The news about the AI's ability to make code and even essays brings new users to those systems. Also, things like weapons and other kinds of stuff make the AI news interesting. Networks are things that make the AI real. They allow the AI to search data from the net.  Without them, the developers must program everything to the computer's memories. 

Or it makes it possible to generate a language model that can search data from the network. The data that the AI can use limits its abilities. Another thing that determines AI and its skills is the ability to operate in the physical world. The robot is the tool that turns the real actor who can clean the house and make food. 

Above:) Neural network. 

The fact is this. If we talk about things like AI doctors or AIs that make doctoral theses, we are far away from those things. There have always been people who cheat in universities. And the AI is only one tool for that thing. AI is the tool that makes many things more effective. But it requires that the user knows about the topics. The AI is an ultimate tool for people, who know what they do. 

The AI is a tool that can make many things easier. It can handle well-sorted and well-presented information like numeric calculations with very high accuracy. In a neural network, the AI can swap the calculation to another computer if that computer is required for some other mission. When a user takes the workstation in use, the system can swap the ghost or background process to another workstation. 

In that kind of network-based system, the system is based on the network of workstations. In the network-based solution, the single workstation can try to solve problems in a certain time. If the problem is not solved the system calls other computers to help. In this case, the system can scale the job theoretically unlimitedly. The only thing that limits the number of computers that the AI can use is the number of workstations and supercomputers in that network. 

But the AI cannot make things spontaneously. It always requires humans to begin the operation. 

Social media makes the AI more well-known. But AI is also a tool that requires social media. The AI companies use data from internet services like X, and Facebook to develop and train the AI. When R&D people make some new solutions for AI they require words that activate the process. In cases of the AI the word that people say or write causes a process where the AI searches for match. The word that the AI gets is the trigger that activates the process. 

The AI is still a regular computer program. Or, actually, it's a group of computer programs and applications. The marketing people call a language model that can connect itself to datasets as AI. Each dataset gives some "skill" to AI. The artificial general intelligence AGI is a large number of datasets that it can use to control things like robot taxis and microwave ovens. Otherwise, we can see a large group of dataset modules as one entirety. 

Mesh protocol. 

That means when the LLM calls the robot taxi, it just makes a "phone call" to the LLM that operates the taxi. Then the customer's LLM fills the form. And then it delivers responsibility to the AI that controls the taxi. The appearance of the AGI is formed when the cab transforms the voice it uses with the customer similar to the customer's LLM. There are two independently-operating AI-based systems run on two servers. The network-based structure that swaps missions between systems makes it possible to create a system that seems to be one entirety. 

In this case, the AGI is the network of independent operating systems that humans can control through the LLM. The network-based systems that use open architecture, or partially connected mesh protocols allow to connection unlimited number of modules to that entirety. And that brings AGI closer than we think. 

These kinds of network-based systems can have billions of full mesh networks and in the middle of those systems is the LLM. So each ball in the neural network involves LLM and the mesh network. Those systems can also have independent operating supercomputers. 

The thing is that the network of AI-based systems that connect robots with AI and LLM are impressive tools. The fact is that those robots are good marketing tools. People want to see robots in action. And that brings a new audience to the AI.