Cryptocurrencies and quantum technology.

Encrypting information increases security. And security is trust. Did you think what could happen if somebody breaks the code that the central bank uses to ensure the currency? That allows the hacker to start to make lots of currency. In normal trade, people use digital currency. The big difference between digital currency and cryptocurrency is that the last one is not controlled by the state. Cryptocurrency is like stock. Investors can buy those things and then sell them to other people. Or they can exchange their cryptocurrency for state money or real money. The problem is that nobody controls Bitcoin or other cryptocurrencies. 

There is a lot of money in the cryptocurrency companies' accounts. And if that money will be released to the markets that causes hyperinflation. The main problem with U.S. economics is connected to its role as the dominating actor in global marketing. There are lots of dollars stored in the other state’s banks. And if those dollars are released to markets that collapses the dollar’s value. Bitcoin can cause a similar effect. But the main difference is that the central banks don’t even know or control the money that those cryptocurrency companies handle. 

The bitcoin countdown has begun. Quantum computers can crack the Bitcoin security algorithm in less than a week. But there is a possibility that some actors have already cracked that code. The AI-driven learning neural network can do many of the same things as quantum computers. And in those systems, the AI-driven network architecture drives autonomous workstations to run the code-breaking algorithms. In those kinds of systems, the AI can share the number line that the defending system creates using the Riemann Zeta function between those workstations. Then those systems can try to crack the security key. 

If the system can use the botnet with thousands of computers that thing can make the de-encryption very fast. And that can endanger Bitcoin. The main problem with Bitcoin, and other cryptocurrencies is that they offer a place where actors can dump their money. There is no bottom in cryptocurrencies. The thing that makes cryptocurrencies dangerous is that a considerable part of the state currency can be dumbed into cryptocurrency. There is a lot of currency out of marketing. When the state sees that there is not enough money, it can deliver more currency.  

The contract that the cryptocurrency can be bought from investors means that if the cryptocurrency company delivers very much state currency to the market it collapses the value of the currency. The inflation mechanism is so simple. Only a lot of money is needed. And when the number of currency compared to state ownerships and state merchandise rises that drops the value of currency. There is lots of money invested and locked in cryptocurrencies. There is a possibility that somebody starts to create their own cryptocurrency using stolen or cracked security code. Suppose this is not noticed in cryptocurrency companies. It can cause a dramatic collapse in marketing. 

https://www.rudebaguette.com/en/2025/06/bitcoins-countdown-has-begun-experts-reveal-when-quantum-computers-will-finally-shatter-its-legendary-encryption/

Have you heard Evo, the Chat GPT for genomes?

The Evo AI is the tool that allows genome research using AI. Access to CRISPR gives a large language model. The ability to analyze viruses. And bacteria, and other cell groups. Including human cells. The Evo can find many things that were not possible before. The AI that runs on the morphing neural network is the tool that can make DNA analysis very fast. The DNA involves linear data structure. The DNA involves data that is stored in four bases. (A, T, C, and G, for adenine, thymine, cytosine, and guanine). 

The system can cut the genetic code into pieces. Every part of the morphing neural network can take a small part of the DNA. And that the non-centralized processing model makes it possible to find differences and similarities in the DNA very effectively.  That is one of the most interesting structures in the world. 

The DNA can be used to analyze and find genetic structures that cause hereditary diseases. The DNA analyzer can also analyze the bacteria and viruses' DNA. That can help to find the weak points in their genomes. It can also help to find how some viruses and bacteria can cheat immune defense at the beginning of infection. 

The DNA can turn viruses into the next-generation data storage and transporter. The AI-based operating systems with access to nanotechnology make it possible to transform the DNA into bar codes. Bar codes that are in extremely long tapes can store very large-scale information. 

"Evo, a 7-billion-parameter genomic foundation model, learns biological complexity from individual nucleotides to whole genomes."(Science.org, Sequence modeling and design from molecular to genome scale with Evo)

Trained on 2.7 million raw prokaryotic and phage genome sequences, Evo is naturally multimodal, enabling the codesign of DNA, RNA, and protein molecules that form higher-order functional systems. Evo is also inherently multiscale, enabling prediction and generation tasks at the level of molecules, systems, and genomes." (Science.org,Sequence modeling and design from molecular to genome scale with Evo)

The bar codes that written on paper tape are a very safe way to store information. The system can read that data to the computer using the optical camera. And when that bar code is read the system can burn it. That kind of optically read data storage can offer secured data transportation. By using some acid that destroys the paper. If wrong people will handle that USB. 

However, the AI cannot read the DNA itself. It requires access to some automatized laboratories. The ability to see the base pairs makes the DNA an excellent data storage. The simplest way is to use the DNA bites. There can be empty sequences without base pairs. In this system, two base pairs in a certain distance can mean zero. 

And the three base pairs can mean one. The distance between those base pairs can help the system to separate bits. In that case, the AI or computer can use a regular microscope and camera to detect the binary code. That is stored in the DNA. The DNA can form the small-size bar codes. 

The DNA that stores information is the thing that can make the chemical qubits possible. Each acid can be the one part of the qubit. The length of the DNA is unlimited. And that makes it possible to transport data in the tubes using the DNA. The spectrometer can separate those base molecules from each other. That helps to make the analyzation process more effective and faster than the optical camera. 

https://github.com/evo-design/evo?tab=readme-ov-file

https://www.quantamagazine.org/the-poetry-fan-who-taught-an-llm-to-read-and-write-dna-20250205/

https://www.science.org/doi/10.1126/science.ado9336

AI can replicate itself. And quite soon it might have imagination.

And more things that can turn into reality sooner than we thought. 

The problem with the AI is that the same algorithm that can sort books in libraries can be used to sort the DNA samples. That allows researchers to search for things like hereditary diseases. But the same algorithms can search DNA sequences that make the "Jennifer Aniston" concept neurons able to create abstractions. There is the possibility of making clones of those concept cells that helps us help us think, imagine, and remember episodes from our lives. If those "Jennifer Aniston" neurons are combined with microchips that gives the computer an ability to think abstractions. 

The bio-chips or biological microchips where the computer communicates with cloned neurons can involve those "Jennifer Aniston" cells. That requires that the "Jennifer Aniston" cells are put in the microchip that can keep them alive. Then those cells must communicate with microchips and non-organic computers. 

The speed of human thinking is ten bits per second. Most computers are faster than they are, but human brains are more powerful than any computer.  The reason why the human brains are so powerful is that they are neural networks. When human brains begin some computing operations they start at multiple points in the same moment. 

All neurons are quite close to each other and that means data must travel between them in short distances. The neurotransmitter allows the brain to make the backup copy of the data if it needs the neural tracks for some other more important purpose. When that happens brain will guide the neurotransmitter to another cell, which stores it with a memory block. And when that "more important" case is over brain retakes data from its memories.  

There are billions of neurons in brains. Every neuron has its own memory block. This makes brains more secure. If one cell dies the bite that loses from memory is as small as possible. And the AI mimics human brains and their processes. 

All networks act similar way without depending are they organic or not. So biological networks get and process information similar way as computer-based morphing neural networks. 

The new AI can use the user's computers. This ability makes this tool more powerful because it can create neural networks using its user's computers. The neural networks are computer-sensor combinations whose size has no limits. The AI with the ability to self-replicate itself is the tool that makes it dangerous. The self-replication ability is the thing that gives AI the ability to keep its service even if its central servers are down. 

The AI is mostly an algorithm. Or algorithms, that the large language model, LLM commands. The computer drive is backward and the users don't see anything on the screen. The next step in AI's advance is that it starts to develop itself. The neural networks can collect lots of data from large areas. They can analyze it and then help to develop new microprocessors. 

The AI can collect lots of data from the computers their batteries, and other kinds of stuff. In neural networks, the system can handle multiple things same time. The system can handle itself in its entirety or every workstation can operate separately. 

Neural networks can take on new duties simply by separating one part of themselves and then the sensor can transport data to the system. Neural networks can also read texts from the screen and that means they can use cameras to get data from the net. 

The ability to make a copy of itself gives the AI a tool that can develop itself. The AI can play ping-pong ball with the duty. The other AI uses its replica as the test environment where it tests its changes. The biggest difference is this, when the AI develops other AI it can do that duty 24/7 without brakes. And that can cause nasty surprises. 

https://interestingengineering.com/innovation/worlds-first-living-computer-switzerland

https://www.livescience.com/technology/artificial-intelligence/ai-can-now-replicate-itself-a-milestone-that-has-experts-terrified

https://www.quantamagazine.org/concept-cells-help-your-brain-abstract-information-and-build-memories-20250121/

https://www.quantamagazine.org/new-book-sorting-algorithm-almost-reaches-perfection-20250124/

https://scitechdaily.com/caltech-scientists-discover-the-surprising-speed-limit-of-human-thought-just-10-bits-per-second/

https://www.tomshardware.com/pc-components/cpus/worlds-first-bioprocessor-uses-16-human-brain-organoids-for-a-million-times-less-power-consumption-than-a-digital-chip

Quantum computers and AI threaten data security. The only thing we can do is to be prepared for that threat.

"Researchers at the National Center for Supercomputing Applications are developing new cryptographic standards to protect against quantum computing threats. Their work includes measuring adoption rates and implementing quantum-resistant protocols, with early results indicating gradual progress." (ScitechDaily, Quantum Computing Threatens Cybersecurity: Are We Prepared?)

Mathematicians found the new prime number. (2^136,279,841 )− 1. That is a large number. If the computer uses Riemann's conjecture to break the code, that thing means that it takes a very long time to break the encryption that is made by using prime numbers. The system must try every known prime number for the message. 

That means traditional computers may take years to break the code. The neural networks can start simultaneous calculations at multiple points on a number line.  And that shortens the time very much the system can consist of thousands of personal computers. And that makes it possible to solve complex cryptological problems in a short time. 

The thing that connects neural networks to quantum computers is that neural networks might look like single, solid-core, or monolithic computers. But those networks are multiple computers. Those computers can operate independently. As well as. They can operate as an entirety. 

That thing means that when the neural network takes on a new mission, it must not stop. The traditional Turing machine must stop before it takes the new mission. If one unit of the neural network gets stuck, that means other participants will help it. That means the computer that is stuck can clean up its memories. And before it, the other computers can download that data into them. 

In the morphing neural networks, the system can analyze what caused the overleak. The morphing neural network means that the role of the participants changes. The AI-based control makes it possible to create a system with advanced self-diagnostics. That kind of system allows to use same systems for multiple uses. 

If the system also backs up the RAM- memory to independent hard disks. That denies the data loss if some units must be rebooted. Quantum computers can make similar things. It doesn't need to stop. But the quantum computers are new tools. And there is a long need to use binary computers. 

Three most powerful computing systems. 

1) PC-computer based neural networks 

2) Supercomputer-based neural networks 

3) Qauntum computer-based neural networks. 

Another thing is that quantum computers are coming. Maybe the NSA already has those systems. But also Chinese and their company states have their quantum computer projects. Those nations have money for that kind of system development. 

And that means our data security is endangered. The major problem is that things like RSA encryption are made to protect information against single computers. The major problem is that the RSA encryption algorithms are created for binary computers. It took years to create prime numbers using supercomputers. 

The quantum computer can create those prime numbers in less than a second. And that makes them the ultimate problematic tools. Those systems can also create an ultimate defense. The AI-controlled neural networks can also make it possible to create prime numbers more effectively than single computers. 

A supercomputer is a powerful tool, but neural networks are more powerful, especially in cases where the system must create the prime numbers from the number line. When somebody asks what is the most powerful computer system in the world, I would answer that the neural network of quantum computers. 

https://scitechdaily.com/quantum-computing-threatens-cybersecurity-are-we-prepared/

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. 

The Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game.

  The Chat GPT is a pathfinder but in the future, smaller and more specific AI systems change the game. 

The Chat GPT,  Bing, and many other AI-based chatbot versions are massive systems that should fit every situation. The problem with common AI is that these kinds of systems require lots of capacity, and there are lots of sources that those systems must use. 

This thing means that the trustworthiness of sources is problematic. The reason for this thing is that the AI doesn't think. It collects data by following certain parameters. That thing makes those systems vulnerable in cases where they should search for information that is not very common. 

The smaller-size specific AI-based systems that can use the same engines with Chat GPT and Bing are more suitable for things like scientific writing. The AI is an ultimate tool if it has a pre-programmed list of trusted and estimated sources. If a writer wants information about some very uncommon things like quantum mechanics. The AI can use sources. That passed scientific estimation. The results are best in business.

The limited AIs can act as independently operating modules in the networked AI-based systems. In that case, those limited AIs act as event handlers for the common AIs. 

Maybe we think that those small AIs operate independently. But the fact is that those independently operating smaller AIs can used as event handlers. That system is connected with bigger AI:s. In that model, those limited AIs can form independently operating module networks, which makes the common AIs more powerful, and accurate than ever before. 

Those independently operating limited AIs can network below the Chat GPT style AIs. The idea is that the limited AIs can form the entirety under the common AI control. This means that the limited AI:s can form a network. That the bigger AIs can be used as event handlers. 

Next comes two examples of limited and powerful AIs. Those things can turn game changers. 

The Finnish AI predicted very accurately where the wildfire started. 

One of the examples of specific highly accurate AI is the AI that predicts wildfire. The researchers from Finnish Aalto-University have created an AI that can predict wildfires. And that AI has shown its success. In this case, the AI uses parameters like humidity in the air, air temperature, and wind speed. The system also can use statistics about the conditions and places where wildlife is starting. 

Also, things like the frequency of lightning and things that are lightning common along with rain or in dry weather are things, that help to predict wildlife. The system also can follow volcanic activity and how often and in what kinds of conditions people are making fire. If there are no spark arresters in chimneys that increases the risk of wildfire.  In that case, the specific AI follows only a limited number of variables. And that thing makes it very accurate. 

The military AI can predict where the enemy attack comes from. 

The military AI can use variables like how hard the ground is, is there some muddy river bottoms, and other kinds of things to predict the place where the enemy might want to attack. There are also many other variables like enemy vehicles and weapons that can affect to that place.

But if the enemy uses tanks the ground's hardness is extremely important. Another thing that the system must know is how steep the riverbed is. That is important information for tanks. In the cases that they cannot use bridges. 

There are, of course, many other variables that the system must have. But those two things are examples of small, and specific AIs. Those RISCs- AI:s are not as flexible as Chat GPT and Bing, but they are highly accurate. And the limited operational areas make variable handling easier than in some common AIs.  

https://www.dezeen.com/2023/08/24/ai-wildfire-model-firecnn-aalto-university-aitopia/

https://www.aalto.fi/en/news/new-ai-system-predicts-how-to-prevent-wildfires

A new brain model can pave the way to a new type of AI.

There are two versions of AI or Artificial intelligence. 

*Software-based AI. That is the software that emulates some intelligent- or intelligent-looking actions. 

*The Iron-based AI. That system is the physical system that learns things. Those iron-based AI systems are like brains. They are learning like the brain. 

The organic version of those systems is the laboratory-grown neurons. That is connected to machines. The idea of iron-based AI is that there is no need for any kind of special software. The system is cognitive and it learns to be like the human brain. 

Most modern AI solutions are algorithms. And that means they are software-based systems. But new knowledge of the brain. And especially neural interconnections are making it possible to create "iron-based" artificial intelligence. 

The "iron-based" artificial intelligence is the system that we can describe as the "artificial brain". But the problem with the human brain is that the neurons have multiple connections. And the neural connections are making virtual neurons in the human brain. 

There are four main types of neural connections in the human brain. 

*Connections between individual neurons. 

*Connections between neural groups and neural groups are the brain areas or brain districts. Each of those areas or neuron groups controls certain skills. 

*And each connection has a counter connection. The purpose of counter-connection is to make sure that the cell group is made its action. So when the neurons in the brain are giving orders to the muscle cells the muscle cells send information back to the brain that they have done their job. We can say that all other skills than speech are neuromotor skills. 

When a person learns something that means there is forming a memory unit in the brain where the required motions are stored. We can say that all skills. There are needed motorial functions are movement series. And memory stores those series of movements for re-use. 

When we are thinking about neuromotor skills. All of those skills require three neurons and muscle cells. Those neurons are sensory neuron that brings data from the senses. Reactions to that kind of thing are stored in memory neurons. And finally, the neuron transmits the signal to the muscles.

If no memory block is suitable for certain situations, the brain must make that memory block for similar situations. The reason why the learning process is so complicated is that. When a person faces some kind of situation in the middle of the day brain must store that information in different places around the brain. 

So in the daytime brain are busy. One purpose of sleep time is that brain is free from the stimulus. And during sleep the brain sort the memory blocks. During that process the brain sort similar memories in the same areas. And also neurons are exchanging information with each other. 

https://scitechdaily.com/a-new-brain-model-could-pave-the-way-for-conscious-ai/

https://designandinnovationtales.blogspot.com/