Atom-size microchips are coming.

"When circular polarized light is irradiated onto a thallium lead alloy, the majority “up spins” of electrons flow in the right direction (spin-polarized current). Credit: Taniuchi et al 2025" (ScitechDaily, Revolutionizing Electronics With Single-Atom Spin Currents)

Theoretically, researchers can create transistors using three carbon or metal atoms. The base, collector, and emitter would be atom-size structures. 

The atoms that spin are controlled and can input electricity to that system. Two, or maybe three-atom-size structures can form switches and routers. 

That thing can make ultra-small microchips possible. Atom-size routers and switches are coming to microchip technology. The system's base is in a system. That controls the atom's spin. And that can unlock new and very fast processor technology. Miniature, nano-size microchips are tools that make many things possible. 

Nano-size microchips can control nanomachines. The problem with nanomachines is their small size. The outside remote controller can control those tools. However, they cannot operate in electromagnetic fields which limits their ability to operate for medical purposes. The small microchips allow those systems to operate as well as in versatile missions. 

They can drive as complicated code as larger computers. The new microchip technology opens new routes for medical and military applications. 

There are two versions of independently operating machines. First uses outside computers to control it. The system uses remote controllers that are connected to outside computers. The independent operating machine that uses an internal command unit is independent of remote controllers and data transmission channels. That makes it hard to affect them from the outside. 

And that is one of the wildest things that we can imagine. The futuristic liquid robots that can change their shape are groups of nanomachines. Those systems seem solid structures. But they are a large group of small silicone nano-robots. 

Nano-size microchips are also tools that can operate very highly intelligent robots. The system that fits in the human-size robots can have far more calculation units than in the highest-power supercomputers. And that makes them drive very complicated codes and algorithms. Those robots can be far more intelligent than even humans. 

https://scitechdaily.com/revolutionizing-electronics-with-single-atom-spin-currents/

Sam Altmann says that superintelligent robots are coming.

We are ready for the next step in the AI. The fast internet and deep neural networks turn superintelligent robots into reality fast. Nobody predicted how fast this advance could be. 

The AI-controlled robot can use large language models remotely anywhere where it can reach the network. The network allows that robot to communicate with supercomputers. If a robot or its computers have access to supercomputers and LLM they can give very intelligent impressions of themselves. The impression is like the cases where LLM is connected with microphones and loudspeakers. 

The robot must have cameras as eyes. Microphones and loudspeakers for vocabulary communication and text-to-speech/speech-to-text applications. The system transforms speech into text and then dumps it for the LLM. During that process, the system must have an error correction protocol. 

That allows the robot to communicate with the LLM. The vocabulary correction makes the speech more understandable to LLM. If the LLM doesn't understand what the user says it can give the list of choices, what the LLM thinks the user wants. That makes it possible to give commands to robots in a noisy environment. 

If the LLM doesn't recognize those words it can give a list of what the word looks like, and then ask to select from the menu would the word be A, B, or C. Or if there is no match the user can give the command again using better articulation. 

Satellite communication allows the robot to communicate with fixed supercomputer sites. Anywhere on the planet. The only needed thing is the system has access to a satellite. 

The LLM can itself be in the computer center. But the robot bodies can also create neural networks by connecting each other. Into one entirety. This kind of network-based application benefits the shared calculation capacity. That makes those systems handle information very effectively. 

But when we think about superintelligent robots the robot itself is not intelligent. A supercomputer that controls those things is intelligent. Or the network-based solution that connects those robots in their entirety is intelligent. However individual robots are not intelligent. Their computing capacity is low. But united they are strong. 

In robotics superintelligent robots are tools that can follow spoken commands. 

They are tools that can have the same abilities as humans except they are not living. The controller can give spoken commands to the system. The weakness of that model is the background noise. The solution for that thing can be voice recognition that allows the system to deny unauthorized commands. 

Active denial or active filtering is the tool that denies robots and LLM to take unauthorized commands. The AI can also ask for confirmation after each command. That thing means that the system introduces what it should do and waits the acceptance before it starts to act. The command can be "clean the yard" and when the robot starts to operate it can clean its entire area without different orders. 

And those robots can communicate with the user. Those robots are only tools that follow the supercomputer orders. We can think that those systems are like advanced mobile telephones. 

https://bigthink.com/business/sam-altman-the-superintelligent-robots-are-coming/

AI turns the R&D process very fast.

 

Image by Gemini

Maybe in the future, the R&D process will be fully automatized. Polish SciFi writer Stanislaw Lem introduced that model in his novel "Peace on Earth" the model where the AI-developed machines will turn a threat to Earth. The system would include two systems. Super simulators and selective simulators. 

Supersimulators are AI-controlled computer-based systems where the system creates virtual models of the aircraft and other tools. The selective simulator is the AI-based system that hosts the opponent's digital twins. 

In the R&D the super simulator creates a virtual model. Then a selective simulator uses its digital models to fight against it. It's possible that if the super simulator and its model win. It delivers that model to the selective simulator. And after that, it gets data on why those digital competitors win or lose. Then it tries to develop a new model that beats its original model. 

AI shows its power in the R&D process. The AI developed in Dubai created the rocket engine in three weeks, while NASA engineers made the same system over decades. 

That is one of the areas. Where AI shows its power. The system requires specific and well-made orders. Then, AI and deep neural networks do the job. Deep neural networks can connect free, internet-based data collected from simulators and laboratories. 

Deep learning systems can develop materials. And shapes of other things. Like aerodynamic structures in other laboratories. 

AI can involve those processes in other projects. Highly advanced flow simulations are tools that can make many things virtually, that previously required physical models. Because the system uses mainly virtual tools it's cheaper than traditional processes. The system can make even millions of simulations and calculations in seconds. 

When AI controls the 3D printing systems it can search things like bubbles inside the materials. That makes those systems more flexible and more effective than previous systems. The fast R&D process is the key element in many new tools. The ability to make the rocket-engine type of tough tools that require precise and high-quality work. That kind of system is dangerous in the wrong hands. 

The AI-based R&D process makes it possible to create things like stealth fighters in a new and effective way. 

AI-generated structures and materials including computer codes are things. That some people like Kim Jong-Un want. 

X-ray spectroscopy, scanning tunneling microscopes aerodynamic and radio reflection simulations are tools that make it possible to create very advanced systems very fast. 

https://interestingengineering.com/photo-story/ai-designed-rocket-engine

https://leap71.com/2024/06/18/leap-71-hot-fires-3d-printed-liquid-fuel-rocket-engine-designed-through-noyron-computational-model/

In data security. The weakest link is the user.

Quantum tools make new materials and information possible. Information security and quantum technology are more advanced than before. Even the best systems cannot protect information. If people don't care about the basic rules of data security. 

Unauthorized persons have access to the rooms. Where there is access to quantum computers that destroys data security immediately. Another thing is that if the personnel are not prepared for social hacking.

Social hacking can mean cheating, burglaries at home or even hostage-taking. Violent gangs can also use violence to extort passwords. 

That makes the system vulnerable. Knowledge is power in security. 

The knowledge about virtual actors and other things like hidden cameras makes the systems a world more secure. If intruders can put cameras in the office. 

 If the camera sees the screen. It makes even the best quantum security useless. The hacker sees everything that happens on screen. And that is a big risk to data. 

Hackers can try to lock the user's access to the system by typing the username. When a user makes the new password the hacker sees that from the screen. 

That is one version of the threats to data security. The second thing that endangers data security is long-range communication. Long-range communication uses traditional binary transmission. Enhanced secure versions can use different wavelengths and different routes to enhance data security in long-range data transportation. 

The system can cut information into pieces and send those pieces to different routes or radio frequencies. That makes data systems more secure than regular systems. Data segments in the system must have serial numbers that the receiving system can collect so that data flows in the same order as it is transmitted. 

Because information is bound with physical particles it makes quantum systems more secure than traditional networks. The problem with this thing is it doesn't work for long distances. 

Information transportation that is connected with a physical transporter doesn't alone make anything secure. Even in the best systems we must use strong passwords and be sure that the entrance to the office is secured.  

If the user doesn't use strong confirmation in the system. That makes quantum systems the same way vulnerable as regular computers. The weakness is the quantum systems is the binary port. The binary port is the machine that connects quantum systems with things like input-output tools. 

The binary port controls the quantum system and data that travels between long distances is always vulnerable to attacks. AI and social hacking are tools that can also cause vulnerabilities. Social hacking is a problem in all data systems. And if a person forgets their cell phone or even small paper there is a new password on the table. 

That destroys even the best security protocols. The ability to take pictures of the screen and papers that people forget endangers security as effectively as traditional hacking. 

Fascinating prime numbers.

Is there some non-random sequence in prime number series? 

Prime numbers are divisible by only one and by itself. That thing makes prime numbers the prime tools for ASCII (American Standard Code for Information Interchange) encryption. The ASCII means the numeric code of keyboard symbols. In that process, computers count ASCII numbers using prime numbers. That allows the encryption computer to hide messages from outsiders. And that makes the prime numbers so important. Prime numbers have also fascinated mathematicians throughout history. 

Famous mathematician Carl Friedrich Gauss (1777-1855) spent 15 minutes per every day in his career. Calculating prime numbers. Another mathematician, Bernhard Riemann (1826-1866), created the "Riemann's Zeta function" for calculating prime numbers. The Zeta Function zero points should not include any other than prime numbers. That's when researchers use normal numbers. But there are zeros in complex numbers. 

"The real part (red) and imaginary part (blue) of the Riemann zeta function along the critical line Re(s) = 1/2. The first non-trivial zeros can be seen at Im(s) = ±14.135, ±21.022 and ±25.011." (Wikipedia, Riemann zeta function)

"This image shows a plot of the Riemann zeta function along the critical line for real values of t running from 0 to 34. The first five zeros in the critical strip are clearly visible as the place where the spirals pass through the origin." (Wikipedia, Riemann zeta function)

Numbers from 1 to 49 placed in spiral order. (Wikipedia, Ulam Spiral)

and then marking the prime numbers:(Wikipedia, Ulam Spiral)

There can be a couple of nonprime numbers in Riemann's Zeta Function zero-point series in the case that the zeros. Riemann's hypothesis is one of the biggest unsolved mathematical problems. The problem is this: is there some kind of non-random order of prime numbers? Is there some kind of non-random sequence in the prime number series? If that series exists and somebody finds it, that thing can revolutionize number theory. 

But then we can think about another way to find the prime numbers than calculating. Mathematician Stan Ulam created the Ulam Spiral. If we want to calculate prime numbers using the Riemann Zeta function that takes time. The Riemann Zeta function is the tool whose weakness is that it forms prime numbers in reading lines. Faster computers must only calculate that series that slower computers used. And that breaks the code immediately. The Ulam spiral is the tool that should respond to the need to select certain prime numbers without linear models. 

"The Ulam spiral or prime spiral is a graphical depiction of the set of prime numbers, devised by mathematician Stanisław Ulam in 1963 and popularized in Martin Gardner's Mathematical Games column in Scientific American a short time later. It is constructed by writing the positive integers in a square spiral and specially marking the prime numbers." (Wikipedia, Ulam Spiral) 

"Ulam and Gardner emphasized the striking appearance in the spiral of prominent diagonal, horizontal, and vertical lines containing large numbers of primes. Both Ulam and Gardner noted that the existence of such prominent lines is not unexpected, as lines in the spiral correspond to quadratic polynomials, and certain such polynomials, such as Euler's prime-generating polynomial x2 − x + 41, are believed to produce a high density of prime numbers. "(Wikipedia, Ulam Spiral) 

"Nevertheless, the Ulam spiral is connected with major unsolved problems in number theory such as Landau's problems. In particular, no quadratic polynomial has ever been proved to generate infinitely many primes, much less to have a high asymptotic density of them, although there is a well-supported conjecture as to what that asymptotic density should be."(Wikipedia, Ulam Spiral) 

"In 1932, 31 years prior to Ulam's discovery, the herpetologist Laurence Klauber constructed a triangular, non-spiral array containing vertical and diagonal lines exhibiting a similar concentration of prime numbers. Like Ulam, Klauber noted the connection with prime-generating polynomials, such as Euler's" (Wikipedia, Ulam Spiral) 

https://www.3blue1brown.com/lessons/zeta

https://www.quantamagazine.org/mathematicians-uncover-a-new-way-to-count-prime-numbers-20241211/

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

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

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

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

The Gemini AI tells Riemann's hypothesis in this way.

What is the Riemann zeta function?

The Riemann zeta function is a special mathematical function that is closely related to the distribution of prime numbers. It is defined over the complex numbers and has many interesting properties. The zeta function can be used to study the distribution of prime numbers among positive integers.

Zeros

The zeros of the zeta function are the complex numbers for which the function's value is exactly zero. These zeros are distributed throughout the complex plane, and their location has deep connections to number theory.

Trivial zeros: The zeta function has infinitely many so-called trivial zeros at negative even integers (-2, -4, -6, ...).

Non-trivial zeros: These are more interesting. The Riemann hypothesis states that all non-trivial zeros lie on the so-called critical line, where the real part is exactly 1/2.

Riemann Hypothesis

The Riemann hypothesis is one of the most famous unsolved problems in mathematics. It states that all non-trivial zeros of the 1  zeta function lie on the critical line.   

Why is the hypothesis important? If the hypothesis were proven to be true, it would be a huge breakthrough in number theory and would have significant applications in many other areas of mathematics.

Why has the hypothesis not been proven yet? The Riemann hypothesis is a very difficult problem, and proving it would likely require entirely new mathematical tools.

Summary

The Riemann zeta function is a complex and fascinating mathematical object whose zeros are closely related to the distribution of prime numbers.

The new particle is so fundamental that it's almost unbelievable.

Scientists have discovered semi-Dirac fermions, quasiparticles that behave as massless in one direction and massive in another, inside a crystal of ZrSiS. This discovery, achieved using advanced magneto-optical spectroscopy, could revolutionize technologies like batteries and sensors while opening new avenues for quantum physics research. (Artist’s concept.) Credit: SciTechDaily.com (ScitechDaily, “Totally Unexpected” – Scientists Stumble Upon a Bizarre Particle That Defies the Rules)

16 years ago researchers predicted a particle that has mass when it travels in another direction. And it has no mass. While it travels in another direction. The particle has mass and no mass at the same time. So particles are massive and massive at the same time. 

The name semi-Dirac particle means that the Dirac equation can introduce that particle when it travels to another direction and the Dirac equation cannot introduce that particle when it travels into another direction. 

The new semi-Dirac particle is one of the most fundamental findings in physics. The new particle has mass when it travels in one direction. But it has no mass when it travels in the opposite direction. The semi-massless particle can answer the question of why photons have no mass. The half-massless particle is also interesting because it can introduce how fields and other wave movements turn into one particle. The term "Dirac-particle" means particles that spin 1/2. And the Dirac equation can introduce them mathematically.  

"Landau levels spectroscopy sheds light on semi-Dirac fermions at the crossing point of two nodal lines within a semi-metal material (Left: Fermi surface of a nodal-line crossing model, Right: Band structure of material). Credit: Yinming Shao/Penn State" ((ScitechDaily, “Totally Unexpected” – Scientists Stumble Upon a Bizarre Particle That Defies the Rules)

The semi-Dirac particle means that the Dirac equation can introduce it when it travels in another direction. But it cannot introduce a particle when it travels in another direction. Dirac equation cannot introduce particles that spin is something else than 1/2 and that don't have mass. The photon spin is 1. That means the photon rotates a full round around its axle. That spin can explain why a photon has no mass. That spin makes it impossible for photons they touch the quantum field and interact with it. Or the photon cannot bound that quantum field inside it. 

The ring-shaped structure in the photon aims energy field over it and that keeps the energy balance in the photon. Because a photon gets as much energy as it delivers it cannot turn older. There is something that denies the semi-Dirac particle mass when it travels to another side. There is the possibility. That kind of ring-shaped quantum field makes the particle massless when it travels in another direction. The interesting thing is the question about the mass of that new particle when it's in a stable position. 

The new semi-Dirac particle causes ideas that maybe the Dark Matter particles the mysterious and hypothetical Axions are similar parts to this semi-Dirac particle. But can the particle have two masses? That means the particle can have mass when it travels in the universe. But the particle can have a different mass. While it travels in another direction. The reason for that can be that the quantum field packs to the other side of the particle. The particle that has mass while it travels in another direction and has no mass while it travels in another direction is the thing. That is a miracle. 

https://scitechdaily.com/totally-unexpected-scientists-stumble-upon-a-bizarre-particle-that-defies-the-rules/

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

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

AI gives superpowers to personal computers.

"Diffeomorphic Mapping Operator Learning, DIMON, a new AI framework, accelerates modeling by solving partial differential equations efficiently, reducing computation times from days to seconds. Tested in heart simulations, it promises transformative applications across engineering and science." (ScitechDaily, AI Breakthrough Solves Supercomputer Math on Desktop PCs in Seconds)

"The adaptable technological solution has the potential to revolutionize engineering designs." (ScitechDaily, AI Breakthrough Solves Supercomputer Math on Desktop PCs in Seconds)

"A breakthrough in artificial intelligence is making it possible to model complex systems—like how cars deform in crashes, how spacecraft endure extreme conditions, or how bridges withstand stress—at speeds thousands of times faster than before. This innovation allows personal computers to tackle massive mathematical problems that once demanded the power of supercomputers." (ScitechDaily, AI Breakthrough Solves Supercomputer Math on Desktop PCs in Seconds)

"The new AI framework offers a versatile and efficient method for predicting solutions to challenging mathematical equations. These equations are crucial for modeling phenomena such as fluid flow or electrical current behavior in various geometries, commonly encountered in engineering and design tests." (ScitechDaily, AI Breakthrough Solves Supercomputer Math on Desktop PCs in Seconds)

New AI gives table computers the supercomputer abilities. The idea is that the system uses the complete and complicated models. 

That makes it unnecessary to begin all modeling processes from the beginning. The system can use models that some other computers have made. 

The idea is similar to the picture. So we can use mosaic pictures as an example. Developers make it by using ready-to-use sub-elements. The ability to use free elements is making data-handling operations more effective. That increases the power of regular computers. But it also fits in use of the supercomputers.

"A Revolutionary AI Framework: DIMON. Details about the research appear in Nature Computational Science. Called DIMON (Diffeomorphic Mapping Operator Learning), the framework solves ubiquitous math problems known as partial differential equations that are present in nearly all scientific and engineering research. Using these equations, researchers can translate real-world systems or processes into mathematical representations of how objects or environments will change over time and space." ((ScitechDaily, AI Breakthrough Solves Supercomputer Math on Desktop PCs in Seconds)

And the thing that can boost that ability is the memory handling tool that cleans memory when the model is ready. The system develops models as layers. Every single layer is an independent model. When the model is ready the computer can store it or give it to another computer. 

Then it can clean its memory. Then that system can receive the model that another computer has worked. And continues to develop or build the new model. In that model, the AI-based systems play ping-pong balls with models that they create as stages. In every stage, the system can connect more and more complicated objects into that layer. The system handles program data like Photoshop handles its layers. 

The image that the developer makes could be an example. The city area image. 

The developer or system can select items for that image from the data library. The system can use things like images of houses. That made for some other purposes. 

And that is the idea of object-oriented programming. The idea in C++ and similar programming languages is simple. 

They involve libraries of commonly repeating operations. That means the user of that language doesn't have to program things like simple calculations from the beginning. They must just load the 

Those libraries (like Stdio.h) deny the need to do basic things every time the programmer starts a new job.  

The programmer can use libraries that involve responses for the orders that the programmer writes. Using the programming language. 

The new AI-based model is a new and very advanced version of the old object-oriented programming. The system can collect complicated models from libraries. 

Oor from the net. And that makes it possible for the table computers to get steroids. The other thing is that the computer can take another computer to its work. The system can make the digital twin and download data to the other computer while it cleans its memory. The ability to clean memory while the system develops the model makes it more powerful. In that model, the model is developing in stages. 

When the system makes the new model it can transfer it to its digital twin. And the system can clean the memory of the first computer. The AI-based system can also call more computers to handle the operation. 

https://scitechdaily.com/ai-breakthrough-solves-supercomputer-math-on-desktop-pcs-in-seconds/

https://www.nature.com/articles/s43588-024-00732-2.pdf

https://pubmed.ncbi.nlm.nih.gov/39653845/