Visions of the AI and future

    The new processor technology makes AI and morphing networks more intelligent.  The power of the computers determines the power of the AI.  The most powerful morphing network that can run the AI is a quantum computer network where networked quantum computers are operating as an entirety. Quantum computers operate through binary systems. Binary- or gate systems deliver information into quantum systems. And those binary computers that pre-process information for qubits can also networked as regular computers.  Google's Deep Mind AI can make better weather forecasts than single supercomputers. The fact is that the supercomputers do nothing without operating systems and software. AI is software that power connected with the platform's or hardware's ability to run complicated software.  The AI that interconnects multiple regular PCs to one entirety can be more powerful than systems or algorithms that are running on single supercomputers. But the fact is that the AI algorithm

   Internet and social relationships.  The Internet has a bigger influence on society than we thought. It modifies our brains and ways of thinking.  Internet and electronic devices affect brains. The fact is that the brain doesn't fully separate reality from reality that computers offer. This means the person might realize that things that person sees are virtual reality. But deep in the subconscious brain can handle data. That comes from the screen as a real thing. When people learn something that process changes neural networks  Our brains are excellent tools. They are the reason why we adapt to many conditions. The last thing that human faces. And what requires adaptation is the Internet. When we learn something our brain remakes its neural connections. All actions that we do affect our neural connections. And that's why the internet modifies our brains.  Computer games and the Internet require learning. That thing is clear and everything that we see teaches us something. Th

    Self-learning networks can replace current morphing networks.  When we talk about morphing and self-learning networks. We must realize that the difference between those networks is very thin. So what neural network does, when it learns new things? It connects new observations with databases that involve action. That can respond to that observation.  So in that kind of process, the neural network just creates a database from information that its sensors give. Then it interconnects that database with another database. The system searches action models that allow it to detect things, does the thing that sensors bring to the system require a response?  "Scientists at the Max Planck Institute have devised a more energy-efficient method for AI training, utilizing physical processes in neuromorphic computing. This approach, diverging from traditional digital neural networks, reduces energy consumption and optimizes training efficiency. The team is developing an optical neuromorphic c

     The AI cracks nature's secret codes.  "Gladstone Institutes researchers found that the Christchurch mutation in the APOE gene protects against the effects of APOE4, the primary risk factor for Alzheimer’s. This discovery, showing reduced neurodegeneration in Alzheimer’s models, opens up new possibilities for treatment and was published in Nature Neuroscience". (ScitechDaily.com/“Christchurch Mutation” – How Good Can Overpower Evil in Alzheimer’s Disease Genetics) The AI cracks genetic codes from Alzheimer's to cancer and species interaction. That thing makes it possible to uncover secrets from nature. And that thing opens the road to the research that can revolutionize the evolution theories. The AI can see how symbiosis and interactions are forming inside and between species. It can open secret communication between vegetables and animals.  "Japanese researchers at Nagoya University have uncovered new aspects of the interaction between mast seeding plants l

  AI: the illusion of understanding.  When we ask AI to make something, it takes the list of allowed actions. We know that AI requires limits. Things like AI-created photos and other things like that should be marked using the stamp. The AI can give texts that it creates to plagiarism detection software that universities and high schools should use. That thing makes it possible to detect AI-created texts. In that case, the plagiarism detector just compiles those texts together. And that uncovers the plagiarism and AI-created texts with quite good accuracy.   Then the AI makes the asked action by connecting components that it takes from the net. This is how the AI generates images. When the AI makes the text like a letter or scientific article it searches a couple of sources from the net. After that, it just connects those sources. If the thing that the user asks is common, the AI gives good-looking answers. The AI uses search engine solutions for selecting sources. And that makes it ef

   For the first time in history, researchers trapped electrons in 3D crystals.  Sometimes crystal skulls and Stonehenge were introduced and they were primitive quantum computers. In the crystal skull case. The operator puts crystal skulls into a ring around one skull or some crystal pike. Then that formation turns to operate as qubits.  The fact is that trapping electrons requires a special 3D structure. And that means crystal skulls cannot operate as quantum computers. The writing of Stonehenge is below this text.  The crystal brain.  The ability to store and trap electrons in the 3D crystals opens new paths to superconduction and quantum computing. Flowing electrons can form excellent superconduction. But the problem is how to trap those electron chains into their position. The ability to trap electrons in the crystals is one answer to that problem.  "The rare electronic state is thanks to a special cubic arrangement of atoms (pictured) that resembles the Japanese art of “kagom

   Digital twins can help to create treatment programs and protect biodiversity.  The digital gaia.  The new quantum computers make it possible to create digital twins about complicated things like networks in biological environments. Theoretically is possible to create digital things about the entire Earth. This simulation links all environments. And species into one virtual, AI-controlled entirety. That allows computers can simulate the biological processes from the cell level to the complicated biological interspecies networks.  Digital twin means the digital simulation of the physical thing. Digital twins have been used for a long time in aircraft industries. But the new and powerful computers can also make it possible to create digital twins for biological processes and even humans can have digital twins. In that system, the biological processes between cells and cell groups are simulated digitally. And maybe in the future interspecies networks and interactions can be simulated by

    MIT's breakthrough in neural science helps to create AI and deep neural networks for autonomous learning.  One of the most impressive and common deep neural networks is the human brain. And when researchers work with deep networks they learn more about the human brain. That gives researchers and developers the ability to transform things like how the brain works into artificial neural networks. MIT researchers decode the human learning process. And that gives an ability to mirror that process to the deep neural networks. The new autonomous learning model base is in self-supervising learning. That thing helps to mirror learning processes in deep learning networks. The breakthrough makes a revolution for the self-learning process in deep neural networks.  In the self-supervising model, only part of the neural network participates in the learning process. And in that model, the other side of the deep neural network supervises that process. The idea is that the deep neural network

    Researchers created self-healing plastic material.   Plastics are full of lipids. The lipid molecule group looks like a little bit of a zipper. The lipid molecule can act like a zipper. Its other side can pulled separately from the other. And then those sides can reconnect. That ability makes the lipids able to create self-healing materials. Both sides of lipid molecules are connected by structures that look like tweezers. And nano-systems can use single lipid molecules as miniaturized tweezers in nanotechnology. However large groups of those molecules can be used as materials that can self-heal themselves.  And the structure of those molecules makes it possible to create a plastic layer that can heal itself. And now researchers made that self-healing ability true. The new self-healing plastic can fundametalize many things like underwater crafts and protective gear. Self-healing plastic also can used to create clothes that fix themselves. But those kinds of materials also can used

 First-time wireless device used to make non-magnetic material magnetic. Theoretically, it's not difficult to make a magnetic effect in non-magnetic material is not difficult. The system that makes the magnetic effect should only turn all atoms in the targeted object in the same way. That means the north pole of the atoms must turn in the same direction. And then the magnets can get effect to those atoms. The reason why some materials are magnetic and some are not magnetic is that there is entropy in non-magnetic materials. The entropy causes atoms to be topsy-turvy in the non-magnetic materials. And the order of atoms makes iron magnetic. The electron shells of iron atoms can create spins or magnetic domes. Those things can form magnetic dipoles. And then the magnetic dipoles make possible the magnetic effect of the iron.  "Experimental setup. A thin layer of cobalt nitride (CoN) in a liquid with ionic conductivity. The voltage is applied to the liquid via two platinum plates

   AI requires very effective computers.  The new breakthroughs in microchips and quantum research are making it possible to create new and powerful computers. The ability to create quantum entanglement between photons by using atoms is the new way to create qubits and the new problem is how to drive information in it. The complicated AI requires complicated systems. And effective use of complicated systems requires the ability to control them. If researchers cannot control systems, those systems cannot produce and process data, or they cannot make that thing trusted way. If the system cannot remove outside effects that thing can destroy the results. AI-based systems like AI-based search engines require as much energy as states. And that brings very big, non-wanted effects in the system.  One of those effects is heat. Heat causes oscillations that are non-wanted in quantum computers. The quantum system requires powerful binary computers and AI-based systems that can transform binary da

   How much do people know about the AI?  When we talk about things like AI, we might think that it's like a car. You can drive a car without knowing anything about it. You must only know how to connect gears and where is gas pedal and flashers. You must then know how to connect the windshield wiper and switch from low beam to high beam. And that's it.  Companies can use AI without knowing anything about how it works and how it creates things that it does. But then we must realize a couple of things. The AI will not think. It just collects pieces of information and connects those pieces to a new entirety. So the AI does know what it does. It knows how to connect pieces of information from a couple of sources together.  But there is another thing that we should be aware of. That thing is that. If we want the AI to make some illegal images, that can cause the cut of the net. And that thing is bad for business. If we believe that laws protect us from hackers, we are wrong. The AI

How do deep neural networks see the world?    The new neural networks are acting like human neurons.  The new neural networks are acting like human neurons. The new and powerful AI-based deep neural networks have two types of memory. Short and long-term memories are important things in human data structure. The deep-learning process uses short-term memory as a filter that denies the memory store too much data. The system stores data to short-term memory and then the AI picks up the most suitable particles from those memory blocks. The compositional generalization means that in the AI's memory is a series of actions. Those actions or action models are like Lego bricks. The system selects the most suitable bricks for response to the action where the AI needs to react. The AI can use two lines of those models. The first models are "bricks" or action models stored in AI memory.  The second model is observations from the sensors. The sensorial data. And also be cut into pieces