Writing about machine view. (That's a multipurpose tool)

The same system that recognizes and sorts garbage can also recognize everything else that we teach it to recognize. 

What else would you do with the system that we can teach to recognize and sort garbage? What if that system sends information about that item to some central? What if the system sends the location where that garbage is? We can teach the system to recognize things like drug syringes and then that thing sends the image and location to the center. The system can see if some person carries a gun and then send that information and the image to the central. The same system can also used to select things from the warehouse. And then the person can sort them into the right boxes. 

Many AI products are double-use. This software can be used for many purposes. And when we think that the software is made for some normal things like recognizing garbage and then giving instructions where to put that carbage we face another interesting thing. If we should teach the AI that thing we can also teach the same AI many other things. The AI doesn't think. That means we can take images of people from the TV or net. And then we can use those images to recognize people from the streets. We can teach that system to recognize almost everything. It can recognize cars, people, and many other things. 

Those algorithms might officially used for some other purposes. But the same system that can recognize and sort garbage. Can recognize and sort everything else. The thing is that we can teach that same system to recognize and sort almost everything. And that kind of system can also serve law enforcement and military work. 

When we make a system that recognizes something and puts it into the right locker, we can make a system. There that functions and activates some handler. The machine view just activates some action when it sees something that matches with some image. 

The purpose where we can use software that recognizes things like bottles or garbage and send that information to the city garbage unit can be used to recognize things like vehicles and cannons and send that information to the military leaders. The same software can recognize plastic bottles and sort them into the right recycling box. 

And it can also recognize tanks from the battlefield. And then that thing can even sort those tanks by their marks and types. Then the system can select the right ammunition to destroy that thing.  This kind of software is suitable for military work. As well as its original purpose is something else. 

Calculation power doesn't itself mean that the LLM turns into AGI.

When we think about large language models (LLM) and artificial general intelligence (AGI), we sometimes forget that AGI is an extended version of the LLM. The LLM can handle any mission. We can imagine that it has the right dataset. Then we face another thing. We sometimes forget that AGI just handles the data that it has. The system connects data into new forms like puzzles. To generate things the AI requires data that it puts in the new order. The biggest difference between AGI and LLM is the scale of questions that they can answer. The system is productive if it has data that it can handle. And that is one of the things that we must realize. 

The AI systems are impressive. But they are also computer systems. Those systems have two layers. The "iron" or physical layer. And software layer. The AI can run on separate programs or be integrated into the operating system.  Or the AI algorithms can operate on the kernel when the AI software is loaded into microchips. That thing can seem like "iron-based AI". But it is software, like all other AIs. 

When we think about AI and its shape. 

We must realize. That even the best systems like human brains are useless without information. 

The software sorts information like puzzles. We call that process using the name: "thinking". Human has two thinking speeds. The fast and slow. The slow is the analytic and the fast is like reflex. 

Computers are useless without programs. Those programs are algorithms that the AI uses to control data. The thing. That makes it hard to make cognitive AI seem simple to solve. 

We must make a system that learns like humans. And then mimic that process in the system. When we make a robot that reads a book and then stores that information in its memory we must realize that there are things like program code that the computer can handle quite easily. 

It simply sees the code and then compiles it with its data. The system sees the details or attributes that make the database controller search for the right database that involves the right programming language like C++ etc. But when the system must handle abstract or non-certain data there is a problem. When the system learns something by watching movies the system must put the data to match with things that it sees on streets. 

The problem is that the computer doesn't think. We can show anything like movies about some circus artists and tell that those people that the computer sees are "boxes". The computer can have details about things like boxes. But then it can connect those people in the database there are boxes. That might seem ridiculous. But that is possible. Same way if the robot gets the order to get the car. 

A robot walks to the street and takes the first car. If there is no program. That makes the robot choose only the car that its master owns. The robot doesn't itself make a difference between, a car, lorry, van, or truck. For a robot, they are all cars. So if the robot must get the car to take some sand, it can go to the nearest car and then take it. Then robot can simply put sand on the trunk. The robot must have information about what type of vehicle it needs. For carrying sand. 

That means we must also develop programs. That we can create AI that doesn't make surprises. The large language models are quite a new tool. Advancing is fast. But then we must realize that the road to the AGI can be longer than we expect. Or it can become a reality sooner than we expected. And then we must also understand that there is not a single person that can ask all possible questions in the world. The human's knowledge is limited to data. That human is stored. And there is no "General person" who knows everything in the world. 

Will LLM lead to artificial general intelligence, AGI?

"Artificial general intelligence (AGI) is a type of highly autonomous artificial intelligence (AI) intended to match or surpass human capabilities across most or all economically valuable cognitive work. This contrasts with narrow AI, which is limited to specific tasks.[1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly exceeds human cognitive capabilities. AGI is considered one of the definitions of strong AI." (Wikipedia, Artificial general intelligence)

When we think about the AGI and its relationship with humans we can say that the AGI is only an extremely large language model, LLM. That means that to turn into AGI the LLM requires only an extremely large database structure. That kind of database structure is hard to drive. But it's possible. The AGI makes "droplets" of the small language model, SML for each mission. 

We can put all our equipment under the control of the large language model, LLM. Those systems require the computer and socket that the LLM can use to control them. In the traditional model, every single device and system. that we have can involve the computer the system that drives the vehicle on the road or cleans our house. The artificial general intelligence, AGI requires those sockets to control the vehicle. When the user says "Car come here" the AGI locates the person. And gives instructions to the vehicle that drives to pick up the person. 

In that model, the AGI requires that we update all things. That we have. But then we can make another way to create things. That thing is a humanoid robot that has an extremely large database structure. That system can operate in every situation that we can. So, we can say that the AGI is only a large-scale LLM. The robot has only a small computer. However, the internet allows it to communicate with data centers. When a robot gets a new mission the data center generates the data structure or dataset that the robot needs. 

And then creates a more limited, but compact database for that robot. In that model, the LLM creates a series of SLMs to make the robot operate in situations like visiting shops. 

The robot can use three, or four datasets in that mission. 

First, the robot must go out. Then it deletes the "home" dataset and uploads the "walk to shop dataset". 

At the shop, the robot changes the dataset to "operate at the shop". 

Then it changes the dataset to "go home and carry things that you bought". And finally, the last needed dataset involves data that the robot must do when it takes its shopping to the kitchen. In this model, every skill. That the robot has a different database structure or dataset. The central computer cuts a small part of its master data to the dataset series that the robot needs for missions. The humanoid robot is the thing. That can use all kinds of stuff. 

The system can use old-fashioned cars, trucks, and hovering machines. And we can say that the humanoid robot is the socket that can connect everything to the Internet. The robot can use machine learning to find new skills. If a robot must fix the TV it must only know the model of the TV. And then it makes checks like cable checks and other things. Just like humans. 

When we talk about AGI we must realize that even humans cannot do everything. We need to practice everything. When we face some system that is unknown to us, we must read instructions. When a robot learns something it creates a new dataset. And if some data center handles thousands of robots one of them can learn new things and then the system can scale that dataset all over the network. 

Will LLM lead to artificial general intelligence, AGI? That is a good question. The answer depends on what we mean about the AGI. If we think about the situation we can use every single vehicle, that we see. From microwave ovens to taxi cars and street sweeper robots using the AI that asks when we want that street sweeper to clean our yard and robot taxi when it comes to get us we can say that the LLM can be the AGI. The street-sweeping robot can also ask if we need help with our baggage and maybe that same robot can cut our hair. 

Then we must say that the robot that we see in front of us can make everything that we ask. It can turn to cab drivers cutting our hair cleaning our homes and making pizza for us. That kind of robot has a large scale of skills that it can use in every situation. Every skill that a robot has is a database in a large database structure. The central computer can upload the right dataset to a robot. That it controls. 

When we think about the order "pick up my baggage and take them to the car and then drive car to pick me up". The fast internet makes it possible to download and change needed datasets in the robot computer's memory. In that case, the AI. That control's robot. Creates a small language model, SLM for each stage of the mission. The SLM is a compact, reflex version of the LLM. The system can use SLM in cases where the robot requires fast reactions. 

https://scitechdaily.com/quantum-computers-just-got-smart-enough-to-study-their-own-entanglement/

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

New drones can listen to underwater communication.

"Researchers from Princeton and MIT developed a way to intercept underwater messages from the air using radar, overturning long held assumptions about the security of underwater transmissions. Credit: Princeton University/Office of Engineering Communications" (ScitechDaily, Not So Secure: Drones Can Now Listen to Underwater Messages)

"Cross-medium eavesdropping technology challenges long-held assumptions about the security of underwater communications." (ScitechDaily, Not So Secure: Drones Can Now Listen to Underwater Messages)

"Researchers from Princeton and MIT have developed a method to intercept underwater communications from the air, challenging long-standing beliefs about the security of underwater transmissions." (ScitechDaily, Not So Secure: Drones Can Now Listen to Underwater Messages)

"The team created a device that uses radar to eavesdrop on underwater acoustic signals, or sonar, by decoding the tiny vibrations those signals produce on the water’s surface. In principle, the technique could also roughly identify the location of an underwater transmitter, the researchers said." (ScitechDaily, Not So Secure: Drones Can Now Listen to Underwater Messages)

New MIT drones can use radars to see submarines. They can hear the underwater messages. Submarines are used to communicate with each other. Drones can also connect themselves to the submarine's hull. They can slip into the harbor and then connect themselves to submarines. 

Those drones can hear everything inside the submarine. This is why submarines should have a vacuum layer between the inner and outer hulls. That vacuum layer makes it harder to hear things. That the crew says in the submarine. That vacuum can also decrease the noise from its engines. 

Drones can endanger privacy in many ways. They can take images of buildings. They can hear what people say in their rooms. They can carry normal and laser microphones in the houses. They can see through clothes using IR cameras. Drones can also put sensors on the data cables they can take images on screens. Drones can carry plasma- and spectroscopic sensors that allow them to see things like chemical compounds of the fuel, by analyzing exhaust gas.

By shooting targets using low-power laser systems, the plasma sensor sees the chemical compounds of the materials. So those drones can see many things. That humans cannot see.

But drones can also hear underwater communication. They can use acoustic microphones (hydrophones) to hear things. Like acoustic messages from submarines. Those systems can also hear sounds from the submarine engines and propellers. There is a possibility that the underwater drone travels near the submarine and connects itself to the submarine's hull. That allows those drones to hear everything that people say in the submarine. New drones can operate airborne and underwater. And they can operate in many ways against the enemy. 

Advanced acoustic detectors can also use laser beams and radar systems to see how water molecules move. Those miniaturized systems can perform almost the same missions as manned helicopters. 

The underwater drone can also use things like hollow warhead detonators to damage the submarine's outer shell. Those small drones can make holes in the hull like torpedo tube hatches. And they can damage submarine communication masts. Drones can also have acoustic transmitters that uncover the submarine's positions. 

 https://scitechdaily.com/not-so-secure-drones-can-now-listen-to-underwater-messages/

How to decrease the computer's time to run databases?

"“Tiny pointers,” which show the way to a piece of stored data, inspired the creation of a new kind of faster hash table." (Quanta, Undergraduate Upends a 40-Year-Old Data Science Conjecture)

"A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible." (Quanta, Undergraduate Upends a 40-Year-Old Data Science Conjecture)

"Sometime in the fall of 2021, Andrew Krapivin, an undergraduate at Rutgers University, encountered a paper that would change his life. At the time, Krapivin didn’t give it much thought. But two years later, when he finally set aside time to go through the paper (“just for fun,” as he put it), his efforts would lead to a rethinking of a widely used tool in computer science." (Quanta, Undergraduate Upends a 40-Year-Old Data Science Conjecture)

"The paper’s title, “Tiny Pointers (opens a new tab),” referred to arrowlike entities that can direct you to a piece of information, or element, in a computer’s memory. Krapivin soon came up with a potential way to further miniaturize the pointers so they consumed less memory. However, to achieve that, he needed a better way of organizing the data that the pointers would point to." (Quanta, Undergraduate Upends a 40-Year-Old Data Science Conjecture)

There was no use for that idea. 

However, the tiny pointers can help to create more effective databases. 

In the cases. That the system must handle even billions of database tables. There is the possibility that the query has two stages. 

The database tables are sorted under big pointers like boxes. 

There the tables that involve similar data are. The system uses pointers like maps. The main query is sent to the database cells or boxes. 

These tables are under certain topics. Those boxes are like cities. There are streets. That shows the system where it should find the right data. 

Then the subsystem searches the database from the street, blocks, and finally from the door number.

Or the system can search for the right thing. Using the name. But a tiny pointer turns that name to the door number. 

When somebody makes a query like "Where are the BMW gearboxes"?The system can search for the right table and event handler by using an algorithm. 

That connects the query words like "automobiles" to the box or door A21. There are databases. That handles cars and their spare parts. The system must have a connection for the words "cars" and "automobiles". 

But there is another way to sort the database. That is a straight line like a card file. The tables can be sorted like this. The tablet that responds to an event that requires the fastest reaction is the first. This kind of linear database can act as the system that should act as reflexes. 

When a computer user creates or uses a database. That requires a query. The query makes the program find the right table from the database structure. The problem is how the database program finds those tables. In professional database work, database creators keep every table as small as possible. 

The system must search all databases until it finds the right one. The system checks keywords from the query. 

Then it tries to find matching words from the database. This thing means that the system might compile the entire database structure to find the right tables. And in the worst case. That table is the last one on the list. This means that the system uses x-time for the database structure and the limit of the is the time that the system uses with each table. 

That time seems meaningless. Until we face an extremely complicated database structure. When there are billions of tables the time that the system uses databases compiling turns longer. 

If the query has no straight match in database keywords. That causes problems. Grammar errors can cause problems in the system match queries and tables. 

Especially if it requires very fast reactions. This kind of database is artificial intelligence.

Artificial intelligence means that in the large language model, LLM the user makes similar queries for the system. Then the system searches for the right table and data from the hard disk or the Internet. In the AI that controls physical robots and operates as an independent system, the query is replaced by the details of the things there the system must react to. When the system sees something it searches matches from the database. 

In that case, those databases must be sorted under the tiny marks. But the system must sort them like in boxes. On all boxes have etiquette. Like "response for traffic evens". That makes the system search the files very fast. 

The morphing neural system can make that process more effective. There certain boxes are under certain computers. The system routes the query to all of those computers. And they can search databases like card files. 

Then those tiny pointers are under one bigger pointer. That makes it easier to respond to things that require fast reactions. The morphing neural network can sort databases that involve certain types of reactions under certain big marks. 

The system can sort databases so that. The table that requires the fastest reaction place is the first in the line. 

That makes the system more effective. As you see small databases don't need special arrangements. 

 https://www.quantamagazine.org/undergraduate-upends-a-40-year-old-data-science-conjecture-20250210/?utm_source=flipboard&utm_content=topic%2Fsoftwaredevelopment

Jetson's flying car made the flight without issue.

"The eVTOL offers a radar sensor-driven auto landing system and redundant battery propulsion system for convenient flight." (InterestingEngineering)

Flying cars are cheap VTOL aerial vehicles that are easy to handle. Those vehicles can also sometimes operate on the roads. In those cases, the quadcopter systems are on the wheels. In the newest models, the Kamov-type double rotors connected to quadcopter structures improve maneuverability and safety.

Those rotors are also used in NASA's Dragonfly drone. That it sends to Titan moon. Those manned quadcopters can make it possible to create helicopters that can operate underwater and airborne.  

They can carry scuba divers to operational areas. Or people like law enforcement in the forest areas. They can bring tools to remote building sites. And anything that you might want to do with a small aerial vehicle that can carry one or two humans. Those small aerial vehicles can have manipulators. That allows them to collect garbage from yards. 

Those systems can operate in situations where supersonic aircraft deliver operators while they fly with maximum speed. The flying car can operate autonomously or in a manned model. It can carry injured people out of the area. It can also avoid rush hour flying over traffic. 

The quadcopter can be in the aerodynamic capsule below the tactical aircraft. Or those systems can be shot to the operational area by using a rocket. These kinds of systems are multipurpose tools from fun to civil and military purposes. The operator can hover above the area and make things like paintings. 

Those flying cars can also be equipped with machine guns. So they can operate as lightweight patrol systems. That kind of flying system can also make it possible to carry things like man-shaped robots. That flying system can operate in hostile places like Venus's atmosphere. The system can collect samples and analyze the environment. But the fact is that those flying cars can also danger security in houses. People like burglaries can use them to get access to roofs. Or the snipers can use those tools as platforms. 

 https://interestingengineering.com/transportation/jetsons-first-production-model-flying-car-flies-with-stability-lands-without-issue?group=test_b

https://en.wikipedia.org/wiki/Dragonfly_(Titan_space_probe)

The new quantum computers can beat supercomputers.

"A quantum computer has been used to generate and certify truly random numbers, something classical computers can’t do, paving the way for unhackable encryption. Credit: SciTechDaily.com (ScitechDaily, A 56-Qubit Quantum Computer Just Did What No Supercomputer Can)

Researchers have achieved a major quantum computing breakthrough: certified randomness, a process where a quantum computer generates truly random numbers, which are then proven to be genuinely random by classical supercomputers." (ScitechDaily, A 56-Qubit Quantum Computer Just Did What No Supercomputer Can)

The problem with classical computer-based encryption is that those systems cannot make real random numbers. Classical computers handle numbers as lines. The system generates a series of numbers using some algorithm, like the Riemann zeta function. 

The problem is that if the attacker knows the speed of a computer and the function that it uses it can even guess the number. There is the possibility to make the jumping algorithms, calculations that jump the point where the system picks the number back and forth. Quantum computers can create real random numbers. Or they can calculate those numeric lines in ways that the system can handle multiple points in the numeric lines at the same time. 

The ability to use complex numbers makes the system more flexible than regular binary numbers. When a system uses complex numbers for encryption those number's imaginal sequences make them hard for binary computers. Binary computers can make morphing neural networks. Where each computer or computer group handles its own imaginal sequences. A complex number is a number that can have multiple values at the same time. And that makes it hard for binary systems. 

The system must input those values at the same time to the keyhole that makes it unbreakable. The binary system cannot input multiple numeric values at the same moment. The quantum computer can send a qubit into the receiver and that thing allows it to transfer multiple numeric values into the receiver in the same moment.  

The fact is that quantum computers beat supercomputers only in the most complicated calculations. We can see the quantum computer's power in the most complicated hybrid models. The system must follow billions of objects and then compile their trajectories or other behaviors with calculations. One place where the quantum computer can be the best in business is quantum system development. Theoretically, qubit calculation is quite an easy thing. The qubit looks a little bit atom where hills are one and valleys are zeros. 

The system must use so-called complex numbers to calculate those energy valleys and hills in the 3D structure. Those complex numbers can have many imaginal sequences. Qubit with 56 states requires a minimum of 56 imaginal sequences. The system must also calculate the angles of those energy valleys and hills. And the system must drive data to the qubit in the form. That the computer can handle it.  That requires lots of accuracy. 

So, quantum computers are very powerful tools but there is a point of complexity where the quantum computer beats the binary computer. The quantum computer must adjust the qubit and then drive information into it. That takes a little bit of time. And this is the reason that the binary computer is the best tool for simple calculations. In quite simple hacking the morphing neural network can also make the code breaking very effective. 

The algorithm that encrypts data should be more complicated than just an ASCII mark that is multiplicated using a prime number. The morphing neural network can begin the prime number series at multiple points. The system that can calculate those prime numbers can destroy the data security that is based on too simple models. 

Quantum computers can involve a new layer to the morphing neural networks. The quantum computer requires massive support systems that can control its qubits. 

https://scitechdaily.com/a-56-qubit-quantum-computer-just-did-what-no-supercomputer-can/

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

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

The new 3D-printed robots are a cheap and effective way to make robots.

The new 3D-printed robots have no electricity. They use pneumatic chips to control their movements. Those robots can operate in high-power nuclear radiation. Those pneumatic microchips or "pressure computers" are tools that are also immune to the ECM systems. 

So, they can also have their place in the military world. The difference between those non-electric robots and other robots is that. The 3D printer can make those robots on the desk. 

All robots are not like Atlas. Atlas is a very complicated version of robots. The other versions are cheap and easy-to-produce robots that involve maybe only one sensor. Or the outside operator can control those robots. Using remote control. If there are only a few actions that the robot must make. 

Remotely controlled robots must be at the right point. After the controller finds the right place. The robot gets the order to begin that operation.

That means those robots have only two movements. The travel mode can be swimming in a certain direction. When the robot is at the right point, the system can start to make things like clean water. That can happen by pumping water through active carbon filters and UV lights. Those robots that can clean swimming pools or some other things can be simple small submarines. There are tube, filter, and pumping systems that can remove garbage and chemicals from the swimming pools. 

The idea is that those robots are easy to make using 3D printers. New, cheap pocket-sized PCs can operate as their central units. 

The 3D printer can make any kind of robot. And robot parts. The system requires only CAD images that it can turn into physical systems using 3D printers. 

So those systems can use Rasberry computers as central units. They can make complicated missions. The same robot that can clean swimming pools can also collect samples from things like underground lakes and rivers. 

https://interestingengineering.com/innovation/us-team-makes-3d-printed-robot

Computer scientists published a new open-source operating system for quantum computers.

"The University of Osaka and partners have released OQTOPUS, an open-source OS for quantum computers, to streamline cloud integration and boost global quantum computing development." (SciTechDaily, Scientists Launch Open-Source Quantum Computer OS)

The new operating system should make quantum computer use easier. The problem with quantum systems is that they are extra layers for the massive binary computers and the morphing neural networks that observe and control those systems. The user uses those quantum computers through the binary computers. The binary computer beats quantum computers in easy and simple calculations. 

The system must adjust the qubit and make the superpositions and quantum entanglements before the quantum state is ready to use. The quantum computer is more powerful in cases where the system must handle same time billions of operations. The user can use quantum systems through the internet and things like large language models, LLM makes it easier to control those complex systems.

Computers are useless without operating systems. And quantum computers don't make exceptions. The computer requires an operating system, a layer that combines hardware and software. Any computer program in computer communicates with hardware through the operating system. There are four layers in operating systems. The upper layer communicates with the program. 

And a downer layer that communicates with microchips and their programs. The third layer is the microchips' control program. That program is loaded to read-only memory, ROM. The rest of the operating system is loaded into read-access memory, RAM. And we can see only that part of the operating system. The microchip control program has two layers. The upper layer interacts with the bottom of the operating systems. And the downer layer that controls the microchip's physical components. 

The software that controls microchips is loaded into those microchips in the factory. Without that program, the microprocessor is useless. That program is one of the biggest risks in the systems. If some electromagnetic radiation destroys that program, the microchip is useless. And if the computer involves some kind of "kill switch". That switch is in the microchip's ROM circuits. That program will erase those microchips' control programs and make the computer useless. 

The quantum computer requires operating systems that can control its qubits. The qubit bases in the superposition of the particles. The binary computer has positions zero or one. 

Unlike binary computers. The qubit can have the same time position zero and one. And this takes them slower. 

The problem with quantum computers is that they are enormous machines. Those machines require factory-size platforms. 

And the reason why they are so powerful is that they can perform many operations at the same time. This makes it effective in cases where the system must drive complicated algorithms. In simple calculations, the binary system beats the quantum computers. And in the simplest calculations. Like 2+2=4, the credit card calculator beats the computer. 

But it is possible to make even the simplest calculations very difficult if the system uses very long decimal numbers there are billions of numbers. The thing with quantum computers is that they are slower in simple calculations.  

But the thing is that the quantum computer is more effective when it must drive complicated algorithms and follow the enormous scale of entireties. The thing that takes time in quantum computers is that the system must adjust the qubit before it can start its operation. This is the reason why quantum computers are not worth using in simple calculations. The system can beat the binary computer in the calculations that take the rest of the universe's lifetime. 

https://scitechdaily.com/scientists-launch-open-source-quantum-computer-os/

New nanoparticles can replace traditional medicines.

"New magnetic nanoparticles in the shape of a cube sandwiched between two pyramids represent a breakthrough for treating ovarian tumors and possibly other types of cancer. Credit: Parinaz Ghanbari

Oregon State University scientists have developed a highly efficient, uniquely shaped magnetic nanoparticle that could enable non-invasive, targeted heat-based cancer therapy for hard-to-reach tumors." (ScitechDaily, Scientists Unveil Cancer-Killing Nanoparticles Shaped Like Futuristic Cube-Pyramids)

The new nanoparticles are planned to kill cancer. They look like cube pyramids. And that is the new step to universal medicines that can kill both bacteria and cancer cells. The benefit of nanoparticles is that they are not toxic. Those nanoparticles can driven to the targeted cells. Then they can be activated by targeting EM or acoustic waves into them. Those waves can release some proteins or nano-strings. And when those particles made their mission. Magnets can collect them from blood. 

Those nanoparticles can boost cancer treatments. They are used with the system that sends radiation that heats tumors. That kind of metal particles can allow doctors to use lower radiation emissions. Or it can turn mostly harmless radio waves into a cancer treatment tool. 

The radiation heats those nanoparticles. And they can destroy tumor cells or bacteria and basils by that heat. Those metal particles can also open the road to new nanotechnical tools that can make traditional antibiotics old-fashioned. Some of them are is in the same technology that used in laundry powder. 

There is some kind of string or protein in the ball. When that ball slips into the cell. The enzymes in the cell open that ball, and then that nanostring or protein is released in the cell. That nanostring can turn open like a whip. And that cuts the cell's protein shell open. Or they can just fill the cell. Another tool is the fast-rotating particle that can create bubbles in the cells. 

Those particles start to rotate when some chemical stress activates their rotational movement. That thing makes those particles act like some nanotechnical moulinex. The idea is stolen from some primitive organisms like rodents. When some amoeba or basil eats that rodent it starts to rotate destroying the cell's internal structure.

The nanoparticles are not like regular medicines. Their effect is usually mechanic. In some visions, outcoming acoustic or electromagnetic waves cause resonation in those nanoparticles. And that thing destroys cancer and bacteria. The idea is this. Those nanoparticles resonate when they slip into the cell. And then that resonance sends waves into the cell. Those pressure waves destroy the cell's internal structures. 

https://scitechdaily.com/scientists-unveil-cancer-killing-nanoparticles-shaped-like-futuristic-cube-pyramids/

New Chinese ramjet uses magnesium-burning afterburner.

"Chinese researchers have reportedly developed a new type of afterburner for scramjet engines that could achieve Mach 6 at altitudes of 98,425 feet (30 km). According to the team behind it, this was achieved by cleverly incorporating magnesium powder into the hot exhaust gases produced by burning conventional jet fuel." (Interesting Engineering, China’s magnesium-powered scramjet breakthrough nearly doubles thrust at Mach 6)

Chinese use magnesium in the engine's afterburner there the exhaust gas ignites it. The magnesium can also be delivered into the engine as the magnesium tape. That tape increases temperature similar way as the electric arcs increase temperature in electric jet engines. 

There is the possibility that the electric arcs along with the microwave systems can inject magnesium powder. That makes the system capable of using magnesium as its fuel in the entire engine. 

During the WWII. German researcher Dr. Alexander Lippisch invented a supersonic aircraft that used carbon as fuel. The idea was that the ramjet engine burn carbon powder as fuel. The problem with that Lippisch P.13a plane was that the particle size of the carbon was too big. It is sometimes suggested that gunpowder with very small particle sizes can also be an effective fuel for ramjet engines. 

"Model of Lippisch P13a at the Technik Museum Speyer"

"The solid-fuel powered P.13 was one of several distinct Lippisch design studies to be so designated and became identified as the P/13a. It underwent much the same variations of form as the P.12, being presented in a brochure with the large fin and integral raised cockpit, and with an articulated, double-hinged landing skid. The wing trailing edge is angled slightly forwards and the downturned tip surfaces have been discarded. The outer wing sections could be folded upwards for transportation by rail" The engine was successfully tested in Vienna.  (Wikipedia, Lippisch P.13a)

In hydrogen-powered jet planes, the hydrogen-producing unit or electrolytic system can be in the airplane.

The aircraft will fill its tanks with water. And then the electrolytic system will break those water molecules. The system can get that electricity from any electric source. So the operators must only connect the electric wire to the airplane. Or if those people have time they can cover their aircraft using solar panels that create needed electricity. 

Lippisch got that idea from the dust explosions where the dust with a large fire surface will detonate. Those dust explosions destroyed many mills. So the ramjet engines can theoretically use any powder as fuel. The carbon or molecular-size carbon powder can explode giving thrust. In fact, even wheat flour to give thrust if the size of the powder particles is small enough.  If we want to make a ramjet or scramjet engine that is carbon-free, we must use some fuels that don't involve carbon. 

So, the answer to that problem can be in metals like magnesium. Or metal compounds like electrons. If the particle size of the metal- or metal-iron oxide compound is small enough the system can spray that metal powder to the ramjet engine. The electron is a compound of magnesium and iron oxide. And that chemical compound can give very high thrust to the engine. Because fuel itself involves oxygen that system allows the ramjet to operate also outside the atmosphere. Magnesium powder can be a good alternative to normal hydrocarbon. Or, at least Chinese research that thing as fuel. 

https://interestingengineering.com/innovation/chinese-team-develop-new-magnesium-afterburner?group=test_b

https://en.wikipedia.org/wiki/Lippisch_P.13a

The Chinese military uses DeepSeek in non-combat missions.

The Chinese People's Liberation Army, the PLA uses DeepSeek AI in non-combat missions. The Deep Seek can operate as the opponent and data seek missions in war games or military training. 

The AI can be a good opponent for generals and admirals to research and develop tactics. That they want to use. 

To be effective generals and admirals those people need good opponents in their war games.  AI requires lots of information that they can benefit from in those missions. The DeepSeek can also used to train other AIs for military missions. 

The problem is that. Open military AIs can be banned.  And if mission-critical AI operates directly with outside users. 

Those users can infect it using malicious code. And if that AI does not work right. That is a catastrophe. 

And there is the possibility. In real cases, malware can infect those mission-intensive systems. Or as an example, electronic counter measures, ECM, and denial of service, DOS attacks can deny those LLM's operations. 

So, the solution is to use open applications as gloves. That keeps the military AI sterile against malicious code. 

If mission-critical AI operates directly with outsider users. They can see things like its server gates. The server that runs the LLM is similarly vulnerable to other servers. Hackers and malware can make attacks against those servers. 

Malicious software can infect that large language model, LLM. 

The open civilian application makes one extra layer between outside users and the military AI. 

The DeepSeek can collect databases from multiple sources. And when it is ready. 

The system can transfer those databases to other AI. The thing is that. The AI can use multiple sources to develop or train itself. 

The training is like collecting some kind of encyclopedia of things that the AI must react to. Without information the AI is helpless. It must recognize details and then compile a reaction database with things that it sees. 

The AI must recognize the situation and then find the action that allows it to make counter movement for that thing. 

Those data sources can be sensors like spy satellites and recon planes. The system can also use things like computer games to create counteraction for some maneuvers. The AI can transfer the maneuvers that it detects from those sensors to the computer game. And then, the opponent who might not know about that information source starts to play against those maneuvers. 

The system can render those objects like tanks to things. That even Western players can accept them. That kind of network-based data-collecting and training systems can teach those military AIs at a level. That they will not otherwise reach. The large-scale information collection is the thing that makes the AI more intelligent and capable to give response multiple situations. 

https://interestingengineering.com/military/china-pla-deepseek-ai-non-combat-operations

Image: Interesting Engineering

The new U.S. 6th generation air superiority fighter is F-47.

Boeing will produce the new generation 6 air superiority fighter for the Pentagon. Trump pushed 20 billion dollars to Boeing. To complete that aircraft. The F-47 is a response to Chinese new jet fighters. That fighter will replace the old-fashioned F-22. The system would combine manned and unmanned systems. The F-45 is manned but it cooperates with things like "wingman drones" whose purpose is to defend the central unit. There are many possibilities for what that system can be. In some speculations, the F-47 is the last manned system. 

And maybe there are some unmanned ground-attack versions of this aircraft. There are only a few details about that plane. The concept base is in the "Bird of Pray" test plan from the 1990's. The  F-47 is quite a small size which means it can cooperate with the new long-range cruise missiles or some of its weapon systems are outside its body. The fact is that F-45 can be the most radical aircraft design that we have seen recently. It must be more advanced than F-35 Lightning II. 

The advanced AI helps the system to complete its mission. That system should support pilots under extreme stress. The electronic warfare systems that are based on the large language models are the most effective that we can imagine. 

The biggest advances in that plane are something under the hood. Materials and advanced AI with advanced interfaces should improve that system's ability to complete its missions. 

Advanced sensor fusion collects data from multiple sources into that aircraft. In some visions, the future jet fighters are escorted by the kamikaze drones. Those kamikaze drones that are like flexible cruise missiles can make ground attack missions. Details of those systems are always highly secretive. But those things are predicted involving that new and powerful tool. 

https://interestingengineering.com/military/trump-pushes-20-billion-6th-gen-fighter-deal-to-secure-us-air-dominance-over-china

New microrobots are game-changing.

The new biomimetic robots are awesome tools. They can deliver medicines precisely into the wanted cells. Or they can cut the targeted cells into pieces. The problem with those robot's medical use is in their control system. The medical robot must select the targets with a very high accuracy. 

The small-size robots can look like liquid mass. But that mass includes billions of independently operating robots that can carry miniaturized microchips. Those microchips create the network in that mass. Another way is to create artificial bacteria that uses artificial DNA as the program that allows it to find the right cells. 

The system can use the DNA bites that make the immune cells find harmful cells and then those DNA bites can also injected into the basils that can help those immune cells. Those hybrid organisms must have a self-destruction trigger in the DNA that orders them to die. Those genetically engineered cells can also produce medicals in the human body. 

Miniature robots can also carry things like cloned cells. Into the right points. The system can change the DNA in those cells. That transforms their nature immediately. 

New microrobots with liquid structures are tools that can revolutionize medical technology. Those robots can travel into the targeted cells or close blood vessels that transport nutrients to tumors, denying them the ability to receive nutrients. The liquid robots can also close poisonous molecules inside them and remove those poisons from their bodies. However, liquid robots can also operate in many other places. 

Those robots can also close leaks in the tubes. And they can clean organic and non-organic structures. Those robots' flexibility allows them to transport microchips into the right places. 

So they can also act as spying tools. They can cover some areas. And when something puts pressure on those microchips. They can send information to the support station. That kind of technology those microchips form the morphing neural network. 

Can give those robots a very high-power computing power. In the wrong hands, those liquid robots are the most dangerous tools in the world. They can slip into the targeted person's body and close blood vessels. 

"Animation of how the micro swimmer is coated with magnetic nanoparticles and how it swims in water and viscous liquids. Credit: MPI-IS" (ScitechDaily, Scientists Create Microscopic Algae Robots With Incredible Swimming Abilities)

The new algae robots are also incredible tools. They can be microchip or DNA-controlled. The system can also have hybrid control. There the microchip turns the DNA plasmids into the right position. The ability to create synthetic DNA makes those robots very effective. They can find certain cells and transport medicines to those cells. 

Magnetic microalgae have the ability to operate in the human body. Those systems are hybrid things that contain non-organic and organic materials. The hybrid biomimetic robot that connects miniature submarines and living organisms can be a new tool for destroying non-wanted cells The human nervous system can communicate with those biomimetic robots.

Those systems can also have microchips that take commands from the outside systems. The magnetic structure in those robots can act as an array.  Those algae robots can also have the ability to produce electricity themselves if those cells have the genome that makes them create electricity. Those systems require electricity in their microchips. 

 https://interestingengineering.com/innovation/cell-inspired-liquid-robots-south-korea

https://scitechdaily.com/scientists-create-microscopic-algae-robots-with-incredible-swimming-abilities/

The AI requires powerful microchips.

"By directly leveraging light signals received from distributed acoustic sensing systems, the proposed photonic neural network architecture provides massive gains in accuracy and efficiency over conventional electronic computations. Credit: N. Zou (Nanjing University), edited

Imagine fiber optic cables acting as vast sensor networks, detecting vibrations for everything from earthquake warnings to railway monitoring. The challenge? Processing the enormous data flow in real-time."(ScitechDaily, This AI Uses Light Instead of Electricity and It’s Mind-Blowingly Fast)

"Traditional electronic computing struggles, but researchers have merged machine learning with photonic neural networks, using light instead of electricity to process distributed acoustic sensing data at incredible speeds." (ScitechDaily, This AI Uses Light Instead of Electricity and It’s Mind-Blowingly Fast)

AI is an algorithm and physical platform combination just like all other computer solutions. Running computer programs is impossible without microchips. The problem with complicated algorithms is that they require so much computer power that the system requires truck-sized systems. And those systems require lots of energy. The system can use morphing neural networks that make the algorithms lighter for individual computers. But the paradox is this. 

The more powerful computers make those morphing neural networks more powerful. That is important for data security. 

More powerful computers can break codes that weaker computers make. And that causes the weapon race in microchip research. Fast microchips make those systems more effective. Another paradox is that effective systems make it possible to run more complex algorithms. So advances in AI and algorithms follow the same route as other computer programs. More effective microchips allow developers to develop more complicated programs that require more calculation power. 

That kind of system uses lots of power. In electricity-based microchips, resistance causes very big problems. Resistance causes vibration and data loss. The answer can be superconducting computers but those systems require very big coolers. Or material that can be superconducting at room temperature. It's possible to make room-temperature superconductors. Using very high pressure. 

But if the pressure system's shell is broken. Pressure causes terrible danger. In pressure superconducting systems. Pressure anchors those particles in their places. 

Maybe, nanotechnical, small-size tubes where the nano-diameter wire travels can allow researchers to create a safe pressure system. That system removes vibrations from the wire that goes inside it. 

Another way to make the system is to use the photons. The photonic neural network where light replaces electricity can sense if something touches the light fiber or cuts the laser ray. Outside effects. Pressure causes curves in laser ray trajectories if they travel in optic fiber. And the sensor sees that thing. That improves data security. The system can also sense things like seismic wires and changes in electromagnetic fields. 

The binary system can use photons in two ways. The different light wavelengths like blue, and red can be zero and one. The system can determine that a certain lux level is one.

Below a certain lux level is zero. The photonic computer can use CCD chips or photovoltaic cells as receivers. The photonic microchip keeps the temperature in the system lower. 

"Distributed Acoustic Sensing (DAS) is an advanced technology used for infrastructure monitoring. It detects tiny vibrations along fiber optic cables that can stretch for tens of kilometers. DAS has become essential for applications like earthquake detection, oil exploration, railway monitoring, and submarine cable surveillance. However, these systems generate vast amounts of data, creating a major challenge: processing it quickly enough for real-time use. Without rapid data processing, DAS loses effectiveness in scenarios where immediate responses are crucial."(ScitechDaily, This AI Uses Light Instead of Electricity and It’s Mind-Blowingly Fast)

"To tackle this, researchers have turned to machine learning, particularly neural networks, as a way to speed up DAS data processing. While traditional electronic computing with CPUs and GPUs has greatly improved over time, it still struggles with limitations in speed and energy efficiency. Photonic neural networks, computing systems that use light instead of electricity, offer a breakthrough solution. They have the potential to process data far faster while using significantly less power. However, integrating photonic computing with DAS has proven difficult, mainly due to the complexity of DAS data and the need for precise signal processing." (ScitechDaily, This AI Uses Light Instead of Electricity and It’s Mind-Blowingly Fast)

https://scitechdaily.com/this-ai-uses-light-instead-of-electricity-and-its-mind-blowingly-fast/

From weather broadcasts to social algorithms.

"Aardvark Weather is an AI-driven system that dramatically reduces the time and computing power needed for accurate forecasts. Unlike hybrid models, it replaces the entire forecasting pipeline with machine learning, outperforming traditional systems using a fraction of the data. Credit: SciTechDaily.com" (ScitechDaily, Scientists Just Built an AI That Predicts Weather in Minutes – And It’s Beating the Best)

Researchers created an algorithm that predicts whether to beat the best.

The AI can handle thousands or even billions of objects at the same time. That makes it the ultimate tool for predicting things like solar storms and weather. The system can collect databases from certain things. Then it compiles observations about the thing that it should predict. 

If the AI should predict solar storms. It must collect information about solar activity like changes in its luminosity, and whirls and particle flow that predict solar storms. 

In the same way, weather prediction requires information about the airflows, temperature, and all other things that we can connect to storms and other types of weather.  The new AI- or algorithm makes it possible to create models with a very high accuracy. Those high-accurate AI-based systems can make models about magnetic fields, air flows, and many other things that can help to make the material, and other types of research. 

And then what about "Psychohistory"? That is a mathematical model of the human behavior. The system could predict a large human group's behavior. 

But maybe. Someday that hypothetical system could turn so accurate that it can predict a single person's behavior. 

The requirement for that kind of algorithm is that the system can collect trusted and confirmed data freely. 

Isaac Asimov introduced "Psychohistory" in his SciFi novel "Foundation". 

We can think. That Psychohistory is the mathematical model of social behavior. The idea is that similar algorithms that are used to predict weather can be used to predict human behavior. In this model. The system can predict the large human group's behavior in certain situations.

The system uses the Boltzmann constant and some other types of formulas to create models about human group's behavior. The system compiles things that it sees with data that it found from the net. The prediction forms when data. The system collects from sources, like historical documents is compiled with information that the sensors send to the large language model LLM. 

Then it can compile that behavior with data that it collects from the news and environment. The idea is that the system acts like an astronomical or weather-predicting AI that can calculate large gas mass behavior. The system cannot predict the single gas atoms place in the universe. But it can predict large gas masses like galactic supergroup behavior. 

But then we must realize that our knowledge of neural networks is higher than in Asimov's time. The system can collect data about neural connections in the human nervous system. Then it can compile that thing with people's nervous system that the system knows. The idea is that a person with a certain social background should behave in similar ways. If those social backgrounds are identical that means the nervous connections must be identical. 

But the fact is that. The system must also follow the social behavior that it can make profiles of people. That profile tells about the person's way of behaving in certain situations. So the system must collect data matrix about people. 

Then it must compile that data matrix with people who it observes. The problem with this type of behavior prediction cannot be trusted it must have the full and confirmed dataset that it can compile. Social algorithms are new things that can predict things like riots.  

https://scitechdaily.com/scientists-just-built-an-ai-that-predicts-weather-in-minutes-and-its-beating-the-best/

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

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

Quantum algorithm vs. binary algorithms.

Image: Quanta Magazine

The biggest difference between quantum and regular algorithms is that in quantum algorithms or in quantum networks data is connected to physical items. Sometimes people misunderstand the terms algorithms and networks. Networks are things. That transport data. 

Algorithms are programs that compute data. The term quantum algorithm can mean the calculation program that handles very long decimal numbers. That can involve tens of millions of numbers. The biggest problem with quantum computers is temperature. 

Those systems require so powerful coolers that they are factory-size systems. The compact-size quantum computers are on the door. But that means they are room-size monsters. Those systems require supercomputers that can operate their operational systems. A good and powerful alternative for supercomputers is the morphing neural network. 

That makes it possible that the qubit's values can be zero and one. This allows the system can make many operations at the same time. The binary network's values can be 0 or 1. The things called morphing neural networks can also make binary computers make many things at the same time. The system shares missions between different computers. 

The question is what is the most powerful computer in the world? The answer is interesting. The most powerful or fastest computer depends on the formula that the system should make. If we want to calculate simple calculations like 1+1 the most effective system is the credit-card size calculator. 

The thing. That decreases the quantum computer's power is that making the quantum entanglement there data travel in those systems takes time. That makes the quantum computer slower in the simple algorithms. Quantum computers are most powerful when the system must handle multiple variables and complicated formulas. 

The thing is that the quantum computer is not the best tool in the world. If we want to make simple calculations. 

There are calculations that the that the normal computer makes the universe's entire lifetime. 

And the quantum system makes that thing in minutes.

But then the morphing neural networks are tools that can make many things faster than the regular computer. The morphing neural network is a group of binary computers. That means they can share complicated series. With each other. 

The AI-based binary systems can jump over the zero points of Riemann's conjecture. So AI is the game changer in all of those things. 

Another thing is that. The new high-power binary systems are not like traditional binary systems. 

There the data goes in different wires. And that solves the "zero" problem. 

The zero problem means that the system must separate breaks in the data row from zero. 

And the system must also know. If the system is switched off. 

The system must also separate two zeros from each other. That's why there can be a different wire that shows when power is on and off. And data can travel in different wires.  The system must give serial numbers to ones and zeros. If they travel through different wires. It can sort them into the right order. 

Or there can be two low states in one wire. That allows the system to separate zeros from breaks. Low states like  3-5V are zeros. And 3-0 V is the break. And it accelerates the system's speed. 

And then the final question: which system breaks the RSA algorithm fastest? The morphing neural network or quantum computer? The RSA encryption uses Riemann zeta function. The morphing neural network can begin the code-breaking in the many points in the number series that Riemann zeta function creates. The system can use a number row that was created before. 

The code-breaking operation is not the same as calculating more numbers to the Riemann's series. Or a series of binary numbers. The idea of Riemann zeta function is that the formula generates only binary numbers. That thing means that it should protect the data. 

But if there are zero points that formula can generate also other than binary numbers. And the AI-based encryption systems can jump over those points. The Riemann's series can be programmed to the morphing neural network. Each computer takes the bite or sequence of that number row under the handle. 

https://www.quantamagazine.org/quantum-speedup-found-for-huge-class-of-hard-problems-20250317/

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

The small language models are reflex algorithms.

Researchers are interested in small language models. The reason for that is simple. Large language models include thousands of billions of parameters. Those parameters require lots of computer capacity. And those computers use lots of electricity. Training for the LLM costs billions of dollars in computer and electric bills. And the biggest problem is that the LLM requires a big data center. 

Normal companies have no money or other resources to run the LLM on their own servers  And in the ICT world those tools can cause data security problems. The LLM that runs on the Microsoft or Open AI servers runs on machines. That is under the potential competitor's control. The LLM is a good tool but it requires lots of power. 

If we want to operate robots independently using the LLM. We must be sure. The robot has an internet socket connected to the central computer. In that model, the robot sends orders first to the computer center. There the LLM transforms those orders into actions for the robot. That system is useless if something disturbs it. 

The robot cannot keep the connection in electromagnetic fields. Robots are planned to be used in high-risk environments like nuclear accidents and military work. The remote control is easy to jam, and that's why researchers in military and civil fields search for systems that can be compact and locally operated. 

One of the problems with LLM is this. Those systems search data from the entire internet. That makes them good tools for making things like doctoral theses. But if the AI-controlled robot uses that model it requires lots of energy and those things are slow. 

The robot requires the reflex algorithm. The RISC systems have a limited number of databases. That makes them compact and effective. The RISC system called the small language model, SLM is the tool that can make robots safer. And allows them to operate independently. 

Those reflex command bases can involve responses to things like: "Would you step away from the door". In the same way, the jet fighter cannot ask for advice from the computer centers if it sees an incoming missile. 

The robot must not make contact with the computer center. Every time, when it must react to some everyday things. When a robot hears something it must realize that it must not react to everything 

So, if we want to create an AI that can operate in a complicated environment we must modify the LLMs and create a lightweight version. The lightweight LLM or small language model SLM has only a couple of million or even less than a million algorithms. Sometimes those SLMs called RISC-language models. RISC (Reduced instruction set computer) systems are like pocket calculators. They might be more limited than large systems. But they are fast-reacting and they do their job very fast. 

Those lightweight language models can run on regular servers. They react very fast because they have only limited action libraries. The small language model can be installed on the aircraft's computer. And it can act as an assistant for the pilot. 

https://www.quantamagazine.org/why-do-researchers-care-about-small-language-models-20250310/

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

The electronic warfare in Ukraine shows how important. Is to develop GPS-independent homing systems.

The ground-launched small-diameter bomb, GLSDB, is a good weapon on paper. But its Achilles heel is the GPS homing system. That system is quite easy to jam if the enemy knows the frequencies that the GPS uses. I think. That civilian GPS was in weapons delivered to Ukraine. 

In the war scene, one of the GPS systems.  In enemy hands, GPS can cause catastrophe. Because that gives those systems radio frequencies to enemy ECM systems. Another thing. What can cause failure is this. Maybe Ukrainian troops tried to shoot them outside the allowed area. That is the Ukrainian territory. 

And the safety system destroyed those weapons. But the ability to jam the satellite transmissions is the thing. That should be noticed. The GLSDB types of weapons require new and GPS-independent navigation systems. 

The combination of the gyroscope and optical AI-based seekers can make that system GPS-independent. In real life, a missile must know the shooting point. And then it must know the direction where it must fly. After that, the missile must recognize the target. 

The AI can make that kind of weapon a new way to survive and travel through the air defense. The AI that can recognize missiles that shoot against the incoming missile can make the missile wobble. That decreases the AA missile's ability to point the missile. Weapon research is the race between weapons and counter-weapons. In the Gulf War in the year 2003, the GPS-guided bombs were the ultimate tools. But the thing that weapon researchers should predict is that.

After the Gulf War information about that bomb was public. Russians knew the GPS navigation system's role in the battlefield. And its ability to aim bombs.

And they had 20 years to develop a counter weapon against the GPS-guided bomb. And after the Gulf War, everybody knew that weapon. But for some reason, there was no navigation system. That could replace the GPS and other satellite navigation systems if they are under jamming. For some reason, the developers didn't realize that the missiles and bombs that used only the GPS were vulnerable. They should predict that the GPS is quite easy to lock if the enemy knows its frequencies. 

And when we think about things like Iranian drones or slow cruise missiles that attack against Ukrainian targets we must realize that Ukrainians are lucky. Those slow drones are easy to pick if they are located. 

The AI that recognizes the target and then makes the missile make the evasion movements can make those slow things more deadly. In the worst case, the AI is the thing that allows the drones to communicate with each other. That means that when one drone sees the AA station it attacks against it.  The drone swarms act as an entirety. The fact is that the AI algorithms and the new electronics can make the old, large-size missiles deadly tools. 

It's lucky. That Russians didn't change cluster warheads to their large AA missiles. Those things can be more deadly than the warheads that they had when those missiles shot against Ukrainian positions. 

https://www.eurasiantimes.com/atacms-delayed-but-glsdb-is-headed-to-ukraine/

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

The Chinese AI won human pilot in air combat.

(SCMP, China’s red-eye AI just killed human pilots’ last hope to win in air combat: researchers)

When we say that humans beat AI, we can always ask, can the average person make that thing? Can the average chess player win AI if the world champion can? Or can an average pilot beat the AI in air combat? If the best pilots can? 

Chinese authorities are interested in AI's military applications. One of them is the advanced autopilot. That can operate as air combat duties. Advanced AI can beat humans in air combat. And that fits into the Chinese state policy. The AI and LLMs are tools that can revolutionize air combat. 

And this is one of the reasons why those things are under development. The main role of the Chinese military is political trust. 

Every time the pilot is alone in a plane. It's always possible that. The person starts to rebel and attack against the Communist Party HQ. This is one of the reasons why drone technology is under development. 

Being alone in the cockpit allows pilots to rebel. 

The kill switch can solve that problem.

It's always possible that hackers get an activation code for that switch. In that case, hackers can destroy multiple aircraft. 

If a pilot flies the mission from the ground. Next to military police. 

This authority can arrest the pilot. If something goes wrong in the mission. 

Also, things like the Tiananmen case caused suspicion that the security troops might not want to operate as government orders. 

(SCMP, China’s red-eye AI just killed human pilots’ last hope to win in air combat: researchers)

In the news, The Pentagon connected to robot armies. 

However, trust is the reason why China and its government research robotics. And especially its military applications. 

And we can say that. China is the top government in AI and its military application research. 

The drone aircraft can operated remotely. Or it can operate independently. 

The remotely operated drones are quite easy to jam. That makes them vulnerable. But if a robot fighter operates independently. The last ones are the result of microchip advances. The AI can give new abilities to regular drones and cruise missiles. The drone can make evasive maneuvers. The thing that makes the AI-controlled robot systems superior is that the AI doesn't matter things like G-force. And it can make maneuvers, fatal to humans. 

Without human control, the opponent's last hope is that the control algorithm will not work as it should. The automatic bombardment systems probably already exist. The aircraft or drone must only know the drop point for the bomb. 

And large-size drones can also drop GPS or optically homing bombs. The GPS is an effective system, but it's easy to jam. This is the reason why the replacement for that system is under development. 

The homing system uses an image-based homing system as the Javelin missile. The system has the image of the target. And then the bomb can fly into it.

The system can use a hybrid system. A combination of inertial navigation, a modified TERCOM system, or GPS for that thing. The inertial (gyroscope) is immune against ECM. 

The aircraft or drone can fly to the drop point even if ECM jams the GPS. Using that navigation. Then, the system can drop the bomb. 

That flies into the target first using the inertial. And optical seeker. 

The system flies to the target. Searching for structures that are similar to that stored in its memories. 

When its camera sees a target. A weapon starts to glide against its target. That system is made to operate. Even, if the ECM cuts satellite connections. 

 https://www.scmp.com/news/china/science/article/3300557/chinas-red-eye-ai-just-killed-human-pilots-last-hope-win-air-combat

Humanoid robots are coming to homes.

Who likes cleaning? And who would outsource that thing to some butler? The problem is that butler must get a salary from their jobs. Sometimes, those people have data security problems because they can tell things about their employers. The answer to those problems can be the housekeeping robot. The housekeeping robot can make things like food, and clean house.  

If that kind of robot has the right database, it can also make repairs. Those things are productive. The new flexible robot can make almost everything that humans can make. The robot needs an internet socket and the right databases to get new skills. The humanoid robot is a good tool because it can use the same tools as humans. The humanoid robot is a physical extension of the large language model, LLM. That robot increases the LLM's ability to get information. It can also make the LLM able to interact with the physical world and make physical works. 

Or maybe quite soon this kind of robot can build entire houses. By using the right modules the robot can make almost everything that its owner asks. The human-shape robot can also operate as a security officer. It can also interact with humans and the internet. That thing means that the robot can have a projector that allows the user to use it like a walking phone booth or internet socket. 

Those robots can share their data using satellite communication. So if they have the right power sources like nuclear batteries. They can operate for over 100 years. The problem with nuclear batteries is that in the wrong hands, they are dangerous. 

Of course, robots are also at risk. They can operate as soldiers and assassins. There is always a risk that somebody cracks the robot's security code. And then programs it to operate as an assassin. When we think about things like authoritarian governments. Those home-assistant robots can also watch things that people do in their homes. When we create something new like human-shaped robots we make also multipurpose tools. Those tools have the ability to change the world. 

https://www.freethink.com/artificial-intelligence/humanoid-1x