Visions of the AI and future

The model that connects bosons with GUT (Grand Unified Theory) and all fundamental forces might look like this.  Things like crossing gravitational fields can form material. But there must be some kind of source for those gravitational waves. When force travels in the universe it requires the force carrier, like a boson, that transfers the force. And then there must be some kind of force that the boson carries.  Without force, that touches the boson. It cannot carry force. When a boson travels through the quantum fields those fields are touching it. But if the energy level of those fields is lower than the boson's energy level is.  The boson releases energy.  The boson cannot transport energy or force. In some models, the size of the carrier boson determines the force that it carries. In the field model, the boson travels through the quantum field (or Higgs field) and then that field touches it. The size of those bosons or force-carrier particles is different, and when they travel

The difference between AI and the human brain is that AI always works with all or nothing principle. For the binary computers, everything is black and white. And if the AI-controlled binary system will not stop trying some solution, even if it sees that thing, as impossible. But, the problem is that. The AI makes only things. That is programmed in it.  To see that something is impossible. The AI requires an algorithm that allows it to see that it cannot make something. Without those parameters, the pocket-size robot tries to move the car, until its battery is empty, if it gets ordered. The AI requires a stop code that activates if a certain number of pullings don't give suitable solutions.  The main problem with binary systems is that they require two other systems to make fuzzy or uncertain solutions.  The binary system can handle only one object per time. And that means the binary system requires another system, that makes a deeper analysis of the situation that it detects. In th

The reason why augmented reality is not a very popular thing is that those systems are expensive. And the second thing is that regular people don't have so much use for them. But when the AR headset connects with things, like drone swarms. That thing makes it possible to connect those drones straight with our senses.  If the operator uses quadcopter pair, there is a terahertz- or X-ray scanner that person can scan people or buildings very fast. If the system has voice command- or BCI mode the user can just give orders to drones and point the drones that they must scan some object. And then those drones can fly opposite sides around the object and then make an X-ray image of it.  The user can mark the target by using a laser pointer, or if the system uses the "retina mouse" where the person looks certain point and blinks the eye. The system follows a certain point of the retina, and uses that image as a mouse.  "An augmented reality headset combines computer vision an

The ChatGPT can make robot dogs speak. That thing can make the robot dog very funny. But the ChatGPT can also make them more flexible than ever before. The robot dog can use to search for all things. It can search for dangerous material. The robot dog can go places where no human cannot go. And maybe in the future robot dog research the surfaces of other planets.  If a robot dog uses nuclear batteries its operating time is unlimited. But the AI-controlled robot dog can also have a manipulator's arm. That allows it to connect itself to any outlet. But the robot dogs can use fuel cells where it burns alcohol or hydrogen. So the owner of that kind of system can feed it with alcohol every morning. Robot animals, like robot wolves, can be used to observe wild animals. They can operate undercover. And actually, those things might look like even reindeer. Cyborg rats can collect information about non-social behavior. And robot animals can carry laser microphones and other sensors that can

  Many things that happen in life are following a certain path. That means recognizing that path can help people to avoid things like Alzheimer's. And that thing can also improve people's life quality, who are living with that disease.  The AI can predict many things that are happening in certain ways. The AI can control information and, make models about things, that follow certain paths. So the AI can connect information about tissue types and lifestyle. And then, the AI can connect that data with other people with similar tissue types.  We know how Alzheimer's advances and AI can predict and warn a person if that person has the risk to get that disease. In the same way, AI can connect things like lifestyle and other variables to predict how some disease advance. But those diseases must have a certain development curve. If AI recognizes that curve, it can find similarities in other places.  In the same way, the AI can collect variables about the history of car accidents.

Theoretically creating the artificial brain is easier than people imagine. The only needed thing is the network of wires some springs connect certain connections when those connections are needed.  The system requires the iron wire and then there must be springs at the end of each iron wire. Those springs connect circles anytime when that connection is required.  The system can use a copper or like in the example case silver cable that is connected to a brush-shaped structure. And at the end of each wire in the brush is an independently operating spring that connects two wires. When that connection is needed. In some versions those wires are connected with miniature ion or cathode cannons that send the electron burst to the receiver. Those springs can be those electron cannons.  "Scientists have demonstrated that nanowire networks can exhibit short- and long-term memory, similar to the human brain. These networks, comprised of highly conductive silver wires covered in plastic and

AI seems to be very smart. It knows the answer to almost all questions, but the way how the AI does that thing is different to humans. That means the AI cannot estimate the information the same way as humans. The AI uses database connections to create answers. There are two ways to make that thing.  1) The AI can use internal fixed databases. In that model, all data that AI shares are stored in the computer's hard disks that run the AI. And in that case, the AI uses the data that is involved it's databases. Making that kind of system output the grab is easy. The hacker must just change the table and then the system will just give false answers. Or if nobody didn't include some kind of data like accounting terms in the system. The system can't give the right answer.  2) Or it can use the open internet. In that model, the AI uses the web browse,r that it can get information from homepages. Then it simply connects as an example information that it gets from the first four

The bistable structure is the thing that makes the permanent shampoo curl hairs. The idea of bi-stable materials is actually, taken from that thing. The bi-stable material means that it has two positions. The bistable material has two structures. When the bistable structure gets the signal it turns from position A to position B. In that movement, the structure transfers kinetic energy to layer B, which makes that material return to its original position. When it gets a counter-signal.  The ultra-tunable bistable structures are tomorrow's materials. The futuristic innovations can create flexible structures that can change their shape at the right moment. The system uses programmable energy barriers that make it possible to change its shape when it faces the right energy load. So when something like a fly touches that surface the structure makes it change its shape. The bistable structures can use in nanotechnology.  "Researchers in China have developed an ultra-tunable bistable

Why AI has problems with speech recognition? The major problem with AI is that. Normally, people are not speaking the literary language. That means the AI must translate dialects to literal language and then find the database. That connected with those words. The problem with AI is it can use fuzzy logic. But in its internal functions, the AI uses certain logic.  The easiest way to make the fuzzy logic system is to make many routes from things, like speech recognition software and certain databases. In that model. Where the system created so many dialect versions of one literary word. And then, all of those options are connected to the word. That activates the database. This kind of structure is technically very easy to make.  But it's hard to create a connection between every single dialect word. And the literary word connected with those dialect words should activate the database. For being smooth and effective, that the person can speak freely that kind of solution requires very

The AI can come as intelligent as humans. That is the thing that researchers and other people are repeating many warnings. If we think that an AI-controlled robot is a dangerous tool, we must remember that. Natural organisms or AI-controlled robots can be dangerous to their environment even if they are not intelligent.  In the case that robot faces malfunction, all robots are dangerous. If the welder-robot thinks the worker is the workpiece, takes the worker to the assembly line and starts the welding. It causes a very dangerous situation.   In the case of AI, intelligence means that the system faces situations that it recognizes. Then the system selects the action matrix that is suitable for the missions.  Bacteria are not very intelligent organisms, but the fact is that bacteria can be dangerous because they create poisons that can damage the internal organs of humans and animals.  The fact is that we can think the AI-controlled robots as organisms as well as the "natural organi

Elon Musk says that he wishes to create an AI that is maximum truth-seeking. The fact is that there is no absolute truth. The AI uses databases where somebody is collected information. And nobody knows who collected the information from data sources. The collector can be an AI, but those databases' sources can still involve false information.  People who are writing that data can make mistakes. Even if the AI creates an astronomical database. The AI must find coordinates where the certain star is. And if that star is not visible to humans, there can be mistakes in its coordinates. So machines are not making mistakes.  Human errors cause all mistakes that machine makes. That means there could be many fatal errors in AI-based quantum systems. The problem with those systems is that error management, especially.  Error detection can make another AI-based quantum system. The problem is that there is no experience of how things, like gravitational waves or gamma-ray bursts, can affect qu

Computers are the back office in multiple operations. But they are also black boxes for many people who don't understand their capacity. The most powerful computers guarantee that the systems handle missions independently. The same programs, algorithms, and protocols that operate drone swarms can also operate nanomachine swarms inside the human body.  But those nano-size systems require powerful, nano-size processors that can make those robots independently operate tools that can run control codes in their computers. If that kinds of systems are possible theoretically those small-size submarines can use similar control codes as full-size quadcopters or other-shaped drones.  The back office of successful missions in technology is computers. The highly complicated systems and mission requires ultra-powerful computers that can operate many things and handle many variables in the mission. Space and aerospace missions are the most high-profile systems in the world.  And the computers fo

The problem is that the AI is a black box. The user cannot know what happens in the system. When it creates answers. So the user only sees the functionality of the system. Not, what happens in it. When we are looking at the glass box in the image we can first think that the box is glass, and we see everything that happens inside it. But then we can take a closer look, and see that there is a black layer.  That layer involves a surface that hides the internal structures of that cube from us. So the glass cube covers the black cube. That black cube seems grey or white. In the glass-box testing the tester will just test the code and remove errors.  In grey box testing the tester tests functionality and code. And in black box testing only the functionality of the system is tested. If we use that glass box as the model of the AI we can see that the AI seems to be a glass box. But some layers are hiding their entirety from the user.  The future belongs to AI, but the danger is that AI is a b

The investments in AI development are very big. And that accelerates its development. Those new companies can buy high-power supercomputers. And they have money to hire highly trained specialists to make the new and more powerful algorithms.  Big and well-funded companies like Google and Elon Musk's startup X.AI are challenging OpenAI and Microsoft in AI development. And the thing is that the development of AI follows a similar route to the development of quantum systems like quantum computers.  Today, AI will participate in its development. So the next-generation AI can be the creature of other AI algorithms. That means the AI can make it automatic. The AI doesn't make errors. And that makes the new systems more effective than old-fashion human operating systems.  The AI can create very complicated codes without errors. There is a possibility that some system can translate the GPT-* code platform to the PHP etc. code platforms.  Also, new algorithms can control quantum compute

The thing that makes the AI so powerful is simple. It can follow and control large entireties. And it can copy data to the databases so that the system can multiply conditions. When researchers are making new intelligent nanomaterials, they need to handle many variables like pressure, radiation level, temperature, and time that energy stress lasts.  Even a small change in those things can affect the construction of those molecular-size machines and structures. Nanomaterials are materials, that are planned and created with accuracy that are smaller than even atoms. Researchers can create new nanomaterials by using a series of nanotubes.  Those nanotubes are less than a millimeter long, and they form an elastic structure. If those nanotubes are connected with turning joints. In that nanomaterial, there is ball-shaped fullerene between those nanotubes. And that combination gives the material an elastic form. That combination can create a nanotechnical canvas that has brand-new features. 

The AI or generally intelligent AI that is like humans can be closer than we ever imagined. ChatGPT and its competitors can make very effective code very fast. Programming languages are things that are precise and easy to make especially for AI-controlled computers. The rules in programming languages are clear and precise. And that thing makes them easy to handle computers. The reason why programming languages are hard for humans is that we don't use them in any everyday communication.  The programming languages are full of symbols (like: <,$ or; etc.), that have no phonetic match in regular speech. Regular computers are handling data in binary mode, there are only two numbers, one and zero. One is on and zero is off. That is called a binary computer. Because there are only two numbers. That thing gives computers extremely high accuracy.  That thing is called precise logic. And that thing makes the data-based security keys effective. If binary codes don't match the key will