Why do we focus on the negative aspects of AI?

One of the reasons why many people have a negative view of AI is a common trend. Negative stories are good selling stories. Negative stories form a model in our mind. We should see that story because we must react to that case. Most of the writing in the ICT sector is somewhat negative. Experts tell how the internet steals our privacy. They tell us when robot taxis hit people. And when experts warn us about the risks of AI. Experts say that AI launches nuclear weapons. Or makes crimes without warning. When. Researchers do research about AI. 

But we read only things. That. The AI made mistakes. We never think about why that mistake happens. 

They want to show that AI makes mistakes. And in those tests, they bomb AI with the same questions multiple times. This thing forms a situation. That. AI-created content starts to accumulate. This thing forms a case. There, the AI starts to surround data that it processed. And then the AI starts to make mistakes. There is a possibility that. The AI starts to make conclusions. 

That there are mistakes. So it interprets queries. That come from the same IP all the time. That there is something wrong. At the same time, when the algorithms get multiple queries at the same time. That starts to cause overload. The resources that AI reserves for the users are limited. The algorithm is like a conveyor belt. It collects data and processes it. 

When. The algorithm gets multiple queries simultaneously. The algorithm has no time to clean its memory. This causes a situation. The algorithm starts to block.  The algorithm has no time to clean its memory. And we can think that this situation is similar to the case. That we leave garbage on the table. Sooner or later, that garbage fills the entire room. Computer programs are forming digital garbage. Files that nobody reads or uses. That garbage includes temporary files. They are reserved for a certain process. Algorithms form those files. 

When. The memory it uses is filled. A single user might not fill the data center. But.  Maybe. Millions or even billions of users. Use data centers. And one big user group is other AIs (Small language models, SLMs, also called: AI agents). Each user has certain resources. This means that. One. User. Cannot get the entire data center’s resources. 

In programming, some programmers call the process. Where. The program removes itself and its output as garbage removal. The AI creates lots of those temporary files. And if they are not removed. The user can exceed the resources that are reserved for that user. 

And maybe we think negative things about the AI because most of the stories. There is a connection with AI. They are negative. Experts warn about an AI bubble in investment marketing. That thing has nothing to do with AI itself. That gives a negative view. Of. The ICT sector. AI takes jobs from programmers. We heard that argument very often. But that is the result of the chain. There, ICT companies maximize their incomes. Their programming is outsourced to countries like India. And that is one of the things that we must realize. 

When we want to think critically about AI, we think. About. Negative things. But do we think those things? Because AI is a bad thing? Or. Are we afraid that AI takes our jobs? And what jobs does AI take? Are they jobs? What do you and I want to do? Or is the reason for the negative attitude simply that we cannot think that way? That we created beats us in things like chess. 

Winning against a computer in chess has been an introduction to the superiority of human intellect. We forget that the person who played against the AI was a many-time chess world champion. This means that the average player could lose those matches much earlier than this chess world champion. We are reading about cases. There, the best of the best beats AI. And of course, the 1980s chess console. It can beat AI. But that doesn’t mean that those consoles are easy to win. When. 

We read about AGI (Artificial General Intelligence). We forget that no human can do everything. The chess computer is a RISC (Reduced Instruction Set Computer) machine. It knows how to play chess. And there is nothing else that this system knows. Chess is a game; there are certain rules and limits. The chess program is quite easy to program. And there are no surprising effects. When. A robot walks on the street. Something can come suddenly in front of it. This makes it harder to program robots. And that makes it possible. That. Some old-fashioned chess computer can beat AI. When. 

We transfer something out from a controlled environment. We face a situation where something can happen. And if we transform that thing. To a physical object like a car. We must be very careful. When. An AI-controlled car hits people. We read that in the media. When ten drunk drivers hit people. We don’t read those things in headlines. Except. If. Those drivers are some foreigners. 

Then we must realize that we have AI. We must accept its existence. When. We talk about killer robots and AI. That eliminates enemies without mercy. We face one thing. We have the military that makes those things without mercy. We talk about the dangers of AI-controlled weapons. But we don’t remember that sometimes ex-military men became criminals. They turn their skills against their society. War is not a game. Sometimes people break. Sometimes they get mad. And even. If. They get punished. About war crimes. Those things happened. 

Hard punishments. They don’t bring anybody back alive. And that is the problem with crimes. The elite troopers have potential also in the hands of the criminal organization. This is one of the reasons. Why. The AI plays a more vital role in missions. Here we must understand one thing. Small countries. They get more benefit from the robot armies. And drones than large countries. When we look at the Ukrainian conflict. 

We must understand that without drones and robots, the Ukrainian army would be lost. And the situation is similar to what it was in the Pacific war after Pearl Harbour. There, the U.S. military had to start using submarines and aircraft to stop the Japanese. Because. Their major battleships were sunk at Pearl Harbour. This forced them to change their tactics. To use aircraft and submarines. 

The pilot is alone when he is in an airplane. There is always a possibility that something will happen. If. The remote control is in a room in the house.

It is possible to take that person away from the controls. The fact is that. If. The jamming device cuts off the communication line. The remote control device is useless. That is why artificial intelligence comes.

Excitement. That we call stress makes people make mistakes. When people fly in Iranian airspace, there is always that thought in people’s brains. “What if one grenade hits and our B-2 falls from the sky?” “How do those people treat prisoners who attacked their leaders and vital infrastructure that they wanted to use in holy war?” Those things can cause horrifying situations. 

We must understand. That. We cannot do only good things. Sometimes we must do bad things. There are no good things. There are just bad things. We blame AI. But then we must realize that the AI makes things. That. Are accepted by humans. AI has stolen the place of a secretary. The boss blamed them all the time. Everything that went wrong was the henchman's fault. The AI is a tool that we can easily blame. We forget that if we don’t give clear and precise orders. The AI makes mistakes. And if we purposely try to make the AI make mistakes. That means they make mistakes. 

https://medium.com/@TVvman/why-do-we-focus-on-the-negative-aspects-of-ai-f14c97821b48

The Jacobian conjecture is partially solved. (Jacobian conjecture is disproved in three or more dimensions)

Jacobian conjecture is disproved in three or more dimensions. But in the two-dimensional model. It remains open. 

“A remarkably simple three-dimensional function has exposed an unexpected limit to a century-old mathematical conjecture. Credit: Shutterstock. An AI-assisted counterexample disproves the Jacobian conjecture above two dimensions while leaving its original two-dimensional form open.” (ScitechDaily, AI Helps Crack an 87-Year-Old Math Conjecture With One Tiny Formula)

AI is the basis for new mathematics. The ability to share problems and connect them again makes that tool impressive. In cases like the Riemann conjecture, AI is the best tool in the business. The system can share the number line across different servers. And then that makes this system an impressive code-breaking tool. In this case, the AI tries to make calculations. That encryption system is made backwards. And that opens the original bits to an attacker. The Riemann conjecture generates binary numbers. That connects to some other formula. 

The last impressive thing that we see is the solution to the Jacobian conjecture. That thing is important in linear algebra. This makes it a tool that is suitable for economics. In that case, the Jacobian matrix is used. For resource optimization. The next examples are things. There, the Jacobian matrix is used. 

Mathematics: An important part of methods for solving differential equations.  

Engineering: Helps model and optimize complex systems.  

Economics: Analyzes economic models and optimizes resources.  

Computer Science: Used in neural networks and machine learning algorithms to aid optimization.  

So, the Jacobian matrix is a versatile tool that provides deep insight into both mathematical and practical problems.

AI  shows that the Jacobian Conjecture is wrong. If. There are three or more dimensions. This means that all polynomial functions cannot be introduced backwards. And that means all calculations. They cannot be checked simply by calculating all calculations backward.

But. The Jacobian conjecture is wrong only in dimensions ≥3. The two-dimensional problem is open. So, dimensions 2≥ are still open. This means. That.

Encryption algorithms that involve the Jacobian Conjecture. It should have three or more dimensions. 

“In mathematics, the Jacobian conjecture is a conjecture concerning polynomials in several variables that states that if a polynomial function from an n-dimensional space to itself has a Jacobian determinant that is a non-zero constant, then the function has a polynomial inverse.” (Wikipedia, Jacobian conjecture). The conjecture can benefit the Jacobian matrix in its models. 

“In vector calculus, the Jacobian matrix of a vector-valued function of several variables is the matrix of all its first-order partial derivatives. If this matrix is square, that is, if the number of variables equals the number of components of function values, then its determinant is called the Jacobian determinant. Both the matrix and (if applicable) the determinant are often referred to simply as the Jacobian. They are named after Carl Gustav Jacob Jacobi (1804-1851).”(Wikipedia, Jacobian matrix and determinant) 

“If m = n, then f is a function from Rn to itself and the Jacobian matrix is a square matrix. We can then form its determinant, known as the Jacobian determinant. “ (Wikipedia, Jacobian matrix and determinant) 

In texts. The Jacobian determinant sometimes is referred to as "the Jacobian".(Wikipedia, Jacobian matrix and determinant) 

“The Jacobian determinant at a given point gives important information about the behavior of f near that point. For instance, the continuously differentiable function f is invertible near a point p ∈ Rn if the Jacobian determinant at p is non-zero. This is the inverse function theorem. Furthermore, if the Jacobian determinant at p is positive, then f preserves orientation near p; if it is negative, f reverses orientation. The absolute value of the Jacobian determinant at p gives us the factor by which the function f expands or shrinks volumes near p; this is why it occurs in the general substitution rule.” (Wikipedia, Jacobian matrix and determinant)

https://scitechdaily.com/ai-helps-crack-an-87-year-old-math-conjecture-with-one-tiny-formula/

https://esimerkkeja.com/jacobin-matriisi-esimerkki-ja-sen-sovellukset-matematiikassa/

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

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

https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant#Jacobian_determinant

Quantum networks and phase singularity.

“Researchers have shown that quantum entanglement can survive a journey through a busy metropolitan fiber network carrying powerful conventional data traffic. Credit: Shutterstock” (ScitechDaily, Quantum Photons Survive a 24-Kilometer Journey Through Chicago’s Busy Internet)

Quantum photons traveled 24 kilometers in Chicago. And another thing that can make that case interesting is this. Those quantum photons or quantum entanglements survived in a regular optical fiber network. 

This is a big advance in quantum communication. And it allows developers to connect quantum computers. But another thing that makes the quantum internet interesting is this.  The information in that network. It is almost fully secure. Because. Information that travels in a quantum network is bound to a physical particle. That makes it possible to secure data. Data that travels in the quantum network. It can be stored in photon series. Or into individual photons. 

This means that a quantum network can transport data in internal structures. The photon series can hide messages. That is stored in the individual photons. If. Somebody steals information from photons. Those photons lose their energy. So, we can think of each photon as a key. Curves in the photon structure. They store data. And if a photon loses energy. That makes it smaller. Researchers can split a photon into two identical photons. 

But the sum of those photons' energy levels is identical to the original photon. This means their energy level is half of the original photon. The situation is similar to the key losing half of its size. That key cannot open the lock. And the system sees that thing. But can somebody split a photon? That travels in the quantum network? We know that there are laboratories. There, researchers investigate technologies for breaking those ultra-secure data networks.  

Quantum networks are not completely safe. Connection points. Those points. Transform information from the quantum internet into electrical impulses. For. Binary computers. The attacker can attack those points. Or to the regular LAN network that delivers data into laptops. Even if we keep that binary state as short as possible. And. Make a faraday cage around that space. This state exists. And. Modern technology. Like drones, allows them to steal information from those spaces. 

A drone can carry a relay station that transmits data out from those ultra-secure spaces. The drone puts its antenna in that cage. The thing that makes those networks safe could be the multi-channel transmission. In that model, the WLAN transmits data by sharing it between multiple channels. The information involves data packets that have serial numbers. That system transmits all those data packets. 

At the same time. Or it can use mixed order. Serial number. It allows the system to sort received data packets into the right order. That makes WLAN more secure. But as we know. There is no absolute security. There is vulnerability in all networks. If. Somebody damages a quantum network. That means there must be some backup system. 

A big problem with quantum photonic networks is that they require a physical structure where they operate. This means they are not as suitable for satellite data transmission as they are in other cases. There, photons can travel in the tube that protects them and the data that they carry. The laser beams are also suitable tools for data carriers. But that requires a laser beam that carries information. 

It is protected so that the observer cannot see it.  The internal laser beam. That travels in the other beam. It can solve that problem. If. Somebody tries to steal information from the internal beam. That travels in the outer beam. That actor must take that data through the outer beam. That dims the beam. And it alarms the controller. 

Darkness can transport photons faster than light. 

When darkness protects information. Phase singularity. It is the thing. That can protect a photon inside it. The phase singularity can also travel “faster-than-light”. Because. It makes fewer curves. This thing means that because of the phase singularity. It travels in a straight line. And a photon makes curves. So. Because a photon makes curves. It travels. Through a longer trajectory. Than a phase singularity. This means that a photon and a phase singularity travel at the same speed. But a phase singularity selects a shorter route. And that makes it reach a goal before photons. 

The most incredible thing that can make the quantum internet so effective is something we can never imagine. It is the thing called phase singularity. That is a phenomenon in wave movement. There. Amplitude is zero. The optical vortex can travel “faster-than-light”. Because. It doesn’t make any curves. This means that this vortex travels a shorter distance than a photon that makes curves. Phase singularity. It can also protect information. Stored photon inside it. 

Optical vortex. It could also transport information. The photon is locked inside it. This means that. The phase singularity. It can protect the photon. And if that phase singularity travels in a laser beam. That makes it an interesting information transporter. That system can take long-range quantum networks and remote control to the next level. 

https://www.sciencealert.com/physicists-found-something-that-can-move-faster-than-light-the-darkness-inside-it

https://scitechdaily.com/quantum-photons-survive-a-24-kilometer-journey-through-chicagos-busy-internet/

https://physicsworld.com/a/darkness-can-travel-faster-than-light/

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

Russian intelligence uses captured web cameras as a spy tool.

“Russian intelligence services are actively compromising internet-connected security cameras across Europe to gather military intelligence, according to a new advisory from the Dutch General Intelligence and Security Service (AIVD) and Military Intelligence and Security Service (MIVD). (Bitdefender, Russian hackers are hijacking internet-connected cameras to spy on NATO and Ukraine)

Russian intelligence uses internet cameras for spying on NATO and Ukraine. How effective is this kind of spying? The answer is that it depends on what those cameras see and the purpose of those operations. Those cameras can be used to locate people and their vehicles. Or the door camera. It can help agents create timetables for targeted people. Door cameras can also be used to warn agents who operate in some houses. The agent who puts surveillance equipment in a place. Benefits from those systems. Those systems. They can tell those operators when the apartment’s owner comes home. 

U.S. and Israel used hacked traffic control cameras to locate Iranian leaders for air attack. 

The door codes can tell if someone walks into the house. The personal door codes. They can help to map the person’s route in the house. And if spies are making a burglary in some apartment. The use of personal entry codes. It can warn those spies. Or it can act as a stand-by order for real bad boys. Hit agents need information about routes. That people use in those houses. They can use that information for kidnapping. Agents can use medical components to get information. 

That they want. That information. It can be access codes to critical systems. Or access to a critical environment. 

In the same way. An anti-VIP team. Can use those door cameras. And traffic control cameras to target people for elimination. And here we must realize that the AI-boosted systems. They can easily collect data about people and their routes. The system uses images from hacked cameras. And the image recognition tools can see where targeted persons are. 

This kind of information. It can be useful for those attackers. But sometimes surveillance cameras can collect information from other vital objects. The security camera that sees the screen can deliver that information as effectively as some malware that infects the computer. The security systems of the house. They can also involve critical information about people’s IDs. This information allows espionage operators to gain access into the building. 

The AI-based image processing tools. They can uncover even state secrets. If. Hackers can intrude on an airfield's security cameras. Those people can collect information from the aircraft and other important material. In harbours, the situation is similar. Hacked surveillance cameras. They can collect data from ships and other important things. Things that the surveillance camera sees. They determine the effect of that type of spying. 

https://www.bitdefender.com/en-us/blog/hotforsecurity/russian-hackers-hijacking-cameras-spy-nato-and-ukraine

Most of the internet traffic. It is. Not made by humans.

Most of the data that travels on the net involves information that has nothing to do with humans. That data is invisible to us. It involves things. Like system updates, system information. TCP/IP checklists. Resending TCP/IP information when checksums don’t match. The checksum tells. That all data reached its goal. If. The checksum doesn’t match. The system resends information. 

The dead Internet conspiracy theory. There, most of the net is AI-generated bots. The AI-created texts, videos, and applications seem human-made. We create more content than ever before. But we must accept that most of the data that we deliver is images or movies. Today, AI can generate almost everything by following instructions. That people give them. AI  can create. A 100-page novel. In a very short time. So if we give ten words as an order to create a novel. The length. Of that novel is about 100 pages. 

Who makes the work? Is it me or AI? At this time, we must say that most of the work is made by AI. And that is one of the problems. If. We say that we create more and more content. The maker of shared content is more often AI. But is the net dead or alive? We could calculate the data that human users create by writing themselves. Or how much data human users use or share? When they share real photographs. And then we can compare that data mass with the data. 

That AI-generated texts and images involve. This means that the internet is dead. If most of its content is made using AI. But then we must realize that AI can surround data. AI can use images and texts. That it made. And then generate new images and texts by using other AI-generated content as a model. The problem with that advance is this. The AI follows page rank. To make images and content that pleases users. This means that this thing accumulates AI-created content. 

And then we must draw some conclusions about the dead internet. Is it just theory? Or something that we misunderstood? Most of. The internet traffic. Is other than communication between people. That content involves the machine BizTalk. That BizTalk is invisible to humans. This means that most of the data that moves on the net. It is something other than social media. Media, or something else. Than humans produce. 

Sometimes dead internet means homepages. And emails and other content. That the owners are forgotten. Especially in the early years of the net. People opened many emails and created homepages. That are still there. But those people have forgotten passwords. Or those passwords and usernames are hosted. By. Companies that no longer exist. People can open emails and forget passwords. People can open workspaces and homepages. And then. They forget those addresses. Sometimes people. Just change their phone numbers. And then they have no access to data that still exists. 

Dead code is one of the things. that uses lots of space. That dead code. Means code that is in the computer program. But that code is neutralized. It forms an empty line in that code list. This neutralized code fills hard disks. We cannot ever see those code lines. Sometimes. They are neutralized because the programmers are in a hurry. This means. They must turn the code into an “instruction line”. These kinds of things affect the checksums that TCP/IP sends. When. It must confirm that the data traveled. Between. The receiver and the sender. It is not changed. 

The information storage. In the network is impressive. But it's not endless. This means that the data storage can be filled. The data storage expands. But at the same time, people create more content. AI-created. Content is more complicated and requires more memory than regular texts. 

A new semiconductor is a big step for photonic computing.

“When two pulses of different colored lasers light  (the two waves at the top of the image)meet in a new device created at the University of Michigan, researchers create a beam of electrons (small golden particles) that flows in a controllable direction. By changing the laser colors, the electron beam can sweep through different directions. Like the beam of a lighthouse. Credit: Yiming Gong." (ScitechDaily, New Semiconductor Device Turns Light Into a Directed Current)

"The light-controlled electron current could open new paths for sensing, telecommunications, and other advanced technologies.” (ScitechDaily, New Semiconductor Device Turns Light Into a Directed Current)

“A pair of laser beams can now send electrons through a semiconductor in a chosen direction without any external electrical power. Researchers at the University of Michigan built the device to explore a previously unobserved physical effect and demonstrate that light alone can both generate and steer an electronic current. ” (ScitechDaily, New Semiconductor Device Turns Light Into a Directed Current)

The biggest problem with photonic computers is the nano-sized optics. That optics is needed to transmit information in the system. It uses light for data transmission. Photonic computers are becoming more interesting. Because they could use less energy. But the main role is that photonic computers. They are immune to EMP (Electromagnetic pulses). Optical data storage doesn’t care about electromagnetic radiation. The problem is. Of course, the control system. Of those computers. Moving parts like turning mirrors. Their turning mechanisms are still vulnerable. So. If we want to make a computer that is fully protected against the EMP. 

We must put the entire computer. Along with its power source, in the EMP-protected space. The name of that space is a Faraday cage. 

This metal cage denies radio waves. Travel through it. The system must communicate with the internet by using an optical switch. This means laser data transmission through that Faraday cage. The computer must use laser data transmission with the EMP-protected computer and the net. The system must not have one single iron or metal wire through the Faraday cage. Or. The EMP pulse travels through it. But optical communication through the cage. It could solve that problem. 

There are actually three versions of photonic computers. 

1) The system where data travels in laser beams between the microprocessors. This system uses conventional microchips. Laser beams transmit data into photovoltaic cells.  They transform it into electric signals. Microprocessors compute those signals as regular computers. 

2) In the second photonic system, the data travels in photonic form through the entire system. The system. It can have nano-scale optics. That controls light. Like an electric computer controls electric signals. Optics require electric systems. That control those mirrors. 

3) Fully photonic computers. There, the entire system operates. With. Some other than regular mirrors and prisms. Things like photoacoustics are promising tools. The photoacoustic or optoacoustic systems. It could control light by using pressure or sound waves in the optical materials. One of the things that can make this kind of dream possible. It could be the tool. 

It uses electric eruptions in a mountain crystal to manipulate light. The light beams can be conducted to the quartz crystal. Then the system sends pressure waves into that crystal. That causes electric phenomena that affect light. The idea is to aim the laser beam into those lightning strikes that form in that crystal. But the problem is how to make those crystals small enough. 

New semiconductor aims light precisely in the desired direction.  Researchers at the University of Michigan created a system. Two laser beams send information into the semiconductor. That semiconductor resends that information in the desired direction. This system can turn light in the desired direction. That is important for photonic data transmission. The system must control light beams. The diameter of those light beams is extremely small. And that makes it hard to create normal mirrors. These types of crystals can bring optical computers one step closer. 

The crystals could manipulate natural light. They can make a new model for quantum optical stealth systems possible. But even if they could manipulate only IR light. That could be fundamental. If the system. It could aim just the IR radiation into the desired direction. That could make it possible to deny the IR signature. The system. It just directs IR light away from the observer. That makes it possible to create a system that is not visible in IR light. We know that turning the natural light away from the observer is challenging. But changing the direction of one wavelength type is easier. And the ability to aim IR into the desired directions. It can give the ultimate night-operation capacity. It could turn the system invisible to IR cameras. Because. It aims IR radiation away from the system. 

https://scitechdaily.com/new-semiconductor-device-turns-light-into-a-directed-current/

The new memristor-based RISC systems. They can create models of human brains.

“A new memristor chip reconstructs intricate brain surfaces at near-biological speed. Credit: Stock. A memristor chip. It brought complex brain modelling into millisecond-scale operation. While preserving detailed cortical structure.”(ScitechDaily, Scientists Develop a Groundbreaking Chip That Operates at Brain-Like Speed)

“Reconstructing the brain’s deeply folded surface in real time requires enormous numbers of calculations. Researchers in China have now developed a chip that performs this work in less than 10 milliseconds. A speed, they say. It is comparable to the human brain’s functioning pace.”(ScitechDaily, Scientists Develop a Groundbreaking Chip That Operates at Brain-Like Speed)

We all have our vision of RISC (Reduced Instruction Set Computer) systems. We see those systems as controlling CAM (Computer-Aided Manufacturing) systems. Like. Computer-controlled lathes. The reduced instruction set makes those systems more limited. But faster than regular computers. 

“In electronics and computer science, a reduced instruction set computer (RISC, pronounced "risk") is a computer architecture designed to simplify the individual instructions given to the computer to accomplish tasks. “ (Wikipedia, Reduced instruction set computer)

“Compared to the instructions given to a complex instruction set computer (CISC).  RISC computer might require more machine code to accomplish a task because the individual instructions perform simpler operations. The goal is to offset the need to process more instructions by increasing the speed of each instruction. In particular. By implementing an instruction pipeline, which may be simpler to achieve given simpler instructions.” (Wikipedia, Reduced instruction set computer)

But modern RISC systems are more complicated than some computer-controlled lathes. Those systems. They can control civil and military robots and drones. The newest RISC systems and RISC architecture. They can model brains in a very short time. 

"Overview of NDS hardware with multilevel and fine-grained CCD memristor. Credit: Peking University"(ScitechDaily, Scientists Develop a Groundbreaking Chip That Operates at Brain-Like Speed)

The new microchip that uses memristors brings brain modelling in milliseconds. This chip still retains the cortex’s simple structure. This new microchip is the tool that beats ASIC circuits. That means application-specific integrated circuits. This new circuit can keep the brain structure. Topologically logical in simulations. “The researchers tested the chip by reconstructing the boundaries of the brain’s white and grey matter and producing 3D manifold-based surface meshes in real time. The resulting cortical surfaces were smooth, closed, and topologically consistent while preserving the brain’s intricate folds.” (ScitechDaily, Scientists Develop a Groundbreaking Chip That Operates at Brain-Like Speed)

“The researchers tested the chip by reconstructing the boundaries of the brain’s white and grey matter and producing 3D manifold-based surface meshes in real time. The resulting cortical surfaces were smooth, closed, and topologically consistent while preserving the brain’s intricate folds.”(ScitechDaily, Scientists Develop a Groundbreaking Chip That Operates at Brain-Like Speed) These kinds of systems can serve in Alzheimer's and Parkinson's research. They can also operate as simulators. They show how medications affect the brain. These kinds of RISC systems can be interesting tools. 

For making. Models of other organs and structures. RISC processors are systems. That developed. For certain. Specific operations. RISC processors can process data more effectively. But the big difference is this. Those systems have their operational code inside them. 

Than computers. Computers. Must exchange information between the processor and the application all the time. RISC systems. They don’t need special applications. Many operations are programmed. Straight. Into the chip. Or those actions are stored in ROM circuits. And that makes it more effective. The minus is that the computer. It's slower than a RISC processor. That is created for one specific purpose. RISC systems. They could simulate things like nuclear reactors. The system can use a similar codebase. As. This Chinese brain simulator uses. The system could be much faster. Than. Regular computers. 

The most widely used RISC systems are pocket calculators. Those systems are limited only to solving mathematical problems. The high-speed RISC processors can be modified NVIDIA A-series circuits. They can act as tools. They can calculate things like the Riemann conjecture with supercomputers. The system requires those prime numbers for the encryption and decryption process. The RISC simulator can operate along with submarine and aircraft computers. The system can compare the predicted and calculated values with the real values. Those RISC systems can also control the thermal effects of the superconducting systems. 

https://scitechdaily.com/scientists-develop-a-groundbreaking-chip-that-operates-at-brain-like-speed/

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

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