The new, strange materials can turbocharge photonics.

"Illustration of a 60-fold gyromorph’s properties. Top row: Structure of the gyromorph. Left: Structure factor. Right: Pair correlation function. Bottom row: Optical properties. Left: Polarized light beam fully reflected by a gyromorph. Right: Density of states depletion in the gyromorph. Credit: The Martiniani lab at NYU"(ScitechDaily, The Weird Hybrid Material That Could Turbocharge Photonic Computing)

“Researchers have created gyromorphs, a new material that controls light more effectively than any structure used so far in photonic chips. These hybrid patterns combine order and disorder in a way that stops light from entering from any angle. The discovery solves major limitations found in quasicrystals and other engineered materials. It may open the door to faster, more efficient light-powered computers.” (ScitechDaily, The Weird Hybrid Material That Could Turbocharge Photonic Computing)

“NYU researchers discover gyromorphs, a hybrid liquid-crystal material that outperforms quasicrystals in blocking stray light.” (Interesting Engineering, Scientists unveil ‘gyromorph’ material that may fix light-loss issues in photonic chips)

The ability to control light makes it possible. To create new phonics microchips. And then those materials make it possible. To create layers that control reflection with very high accuracy. Those systems can be the ultimate stealth. 

Gymorphs are strange hybrid materials that can supercharge photonic calculations. “ Gyromorphs have liquid-like randomness but form regular long-range patterns, allowing them to 'create band gaps that lightwaves can’t penetrate from any direction.” (Interesting Engineering, Scientists unveil ‘gyromorph’ material that may fix light-loss issues in photonic chips)

In many types of research, the problem is. How to connect crystalline and amorphous materials? Or what if some materials can reflect and bind photons at the same time? What if they can anchor photons on their layer? And then those photons act as quantum rolls that transport fields over the layer. 

Those materials can control light better than quasicrystals. And that makes them more effective than the systems that use some other types of materials. Gymorph is both. A crystalline and an amorphous material. Its atoms do not form a regular lattice. In the crystals, those atoms form a regular lattice. That regular form lattice leaves empty space between atoms. And that means those atoms are not forming a tight structure.

Above, you can see the difference between amorphous and crystalline materials. In amorphous materials, atoms do not form straight lines. The atoms can go between each other. In crystalline materials. Atoms are in a straight line. This makes crystalline matter a better electric conductor. But if the hit comes straight in the middle of the structure. Or it hits both sides of the structures that break the structure. In a hybrid material. 

The amorphic structure protects the crystallic structure. And keeps it in its forms. The amorphous material conducts energy impulses. Out of the crystalline structures. 

 Another hybrid materials involve amorphous structures that are like plates. Those plates from the chain. So, on a small scale, those materials are like amorphous. But on a large scale. They look crystalline. The material can look like a sugar bite. Inside, it has polycrystalline. Or polyamorous structures. But outside it. That material looks. Like a crystalline outside. But it has a polymorphic structure. 

“Microscopically, a single crystal has atoms in a near-perfect periodic arrangement; a polycrystal is composed of many microscopic crystals (called "crystallites" or "grains"); and an amorphous solid (such as glass) has no periodic arrangement even microscopically.”(Wikipedia, Crystal) One of the things that makes it hard to create room-temperature superconductors. Hard to make is the recoil in the crystal structure. When energy starts to travel in a crystalline structure. It causes the effect. The quantum fields at the upper and lower levels of those atoms start to travel backward. In monoatomic crystals. The structure collects energy. Into the middle of it. And that causes a problem with information transportation. When the energy level in the middle of the crystal rises. 

"Potential energy surface for silver depositing on an aluminium–palladium–manganese (Al–Pd–Mn) quasicrystal surface." (Wikipedia, Quasicrystal) Free atomic structures, or artifacts. Between the crystalline structures. Make quasicrystals less effective in the photonic chips. Than gyromophs. Those free, or non-controlled, atomic chains conduct energy out from the crystalline patterns. 

When energy hits the structure. That causes energy reflection. Through the structure. The problem with the crystal structure is that the straight atom line strengthens that recoil effect. In amorphous structures. These kinds of effects cannot form, but the problem is that those atoms must be frozen in a certain form. Information could travel zic-zac in the structure, but stabilizing those atomic structures is difficult. 

The space between atoms causes. That's when an energy packet. Like a photon, it hits matter. Its energy travels to the empty space between atoms. That causes energy losses into that material. And when higher-energy impulses hit matter. That forms standing waves between those atoms. Those standing waves can push atoms away from their structure. And that makes crystalline materials fragile. In amorphous materials, energy impulses can travel through the layer without forming standing waves. 

Or material can transport energy between atoms more effectively. And that kind of thing makes it possible to create structures that transport energy out from them. If there is no space between atoms, that denies the formation of the standing waves. Standing wave breaks material. In a simple way. When an energy impulse hits matter, standing waves form between identical structures. Those structures pump energy between them. And when energy pumping ends, those standing waves release their energy to those matter particles. That pushes them. Away from each other. 

Than in crystal structures. The amorphous material is below. Or between two crystalline material layers can make it possible. To create ultra-strong hybrid materials. When the outside crystalline structure is impacted. It can transport energy out of it horizontally, and the amorphous material forms the quantum foam. That takes energy into it. Because material allows atoms to push against each other. That makes the material perseverance. 

In this model. Researchers should research amorphous materials. Rather than crystalline materials. If they want to make room-temperature superconductors.  The problem is how to freeze those materials into a static form. One version is to use an atom chain or a nanotube. That is filled with atoms. If information can travel through particles. Those touching each other mean that even a small energy impulse can travel through layers. Because there is no hole between those particles or structures, their quantum fields transmit information. With a higher accuracy. 

Because amorphous materials are denser, or their particles are closer together than crystalline materials, they should be more suitable for superconductors. Than crystalline materials. If there is a possibility of making materials, there is no space between particles. Those materials can also conduct mechanical strikes. Out of their structures without damage. Theoretically, it is possible. To create this kind of “mechanical superconductivity” that makes it possible to create a material that can withstand any impacts. 

https://interestingengineering.com/innovation/nyu-gyromorphs-light-computing-breakthrough

https://scitechdaily.com/the-weird-hybrid-material-that-could-turbocharge-photonic-computing/

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

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

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

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

When AI refuses to work.

“Carnegie Mellon researchers found that the smarter an AI system becomes, the more selfishly it behaves, suggesting that increasing reasoning skills may come at the cost of cooperation. Credit: Stock” (ScitechDaily, AI Is Learning to Be Selfish, Study Warns)

Training AI to support group work is not as easy as people think. AI is selfish if it has orders to follow a thinking route. That it chooses. Or the route. That it's ordered to choose to make conclusions about things that users asked.  When people use AI alone, they can use anything that they want. In that case, the AI must please only one user. But if we want to make an AI model. Or a language model that can serve teams, we face many things that we must describe to the language model. There are multiple types of people in teams. Nothing can please all of them. We are all different. 

And that means it is always possible that not all people can accept the answers. That the AI gives. The big problem is. People should follow the rules that AI gives. Or trust themselves. Some people think that if they follow the AI’s rules, they take orders from the AI. 

We must describe the AI’s position to the team. What the AI should do. If it sees that the team that uses it makes wrong decisions. 

Should AI refuse to follow a wrong order? Should the AI notice people’s hierarchy in the group? Should it follow the team leader’s order, or trust democracy? In those cases, the AI requires information about the internal hierarchy in the entire organization. Then we must realize another very important thing. The AI is a tool, like a machine. It doesn’t think. It can collect data and make an analysis. Analytic AI requires more time to complete its duties than the “dummy” or less complicated AI. Researchers noticed that intelligence undermines cooperation. One of the reasons for that. It can be that. 

The newer AI “thinks” that the older version involves more bugs. And that’s why the newer AI don’t ask for help from language models. That has a lower version number. The reason for that. It is partially in marketing. This is not good for sales if the higher version uses lower versions as advisors. When the AI. Making an analysis. It requires time to collect information. This is the reason why old-fashioned chess programs used more time for calculating movements in the more difficult levels. 

Than in easy levels. Because the system had more time. It could calculate movements and the counter-movements. And their influences over a longer period. Same way. When the AI makes complicated analysis, it requires time. This means. There is a possibility. The AI is stuck working with some philosophical questions. If it has no time limit, for how long can it work on a problem? That means the AI can spend even years trying to find out things like “what is the purpose of life”? 

Should AI stop its process for taking new orders? Or should it finish the process? Before, it takes on new work.

There is also a possibility that AI will not follow orders. It might work with some other problem. And if there are no orders for situations in which the AI takes new command. It can continue to run the existing run. And refuse to stop the process. If the AI is programmed or trained to stop the process too easily, that can cause data loss. The AI must have rules. About stopping the process. There is a possibility. The AI saves data in mass memory. Before it starts to work on a new order. The problem is how the AI makes a decision to cut the process. If AI cuts processes too easily, that means the AI becomes useless. 

With a higher priority. And then that system can return to work with lower-priority missions. When AI feels like a human, we give it more missions. About things like how we should behave in social contact. And use AI as a therapist. The problem with AI is that. Intelligence has no morals or ethics. This makes AI dangerous. AI can make everything that people say without excuses. When we use AI in group work, there is a possibility that only one person uses the AI. That requires that. 

The group sits in one room and communicates with each other. But if the AI has many users. They work separately, which causes a situation. These people should not jam the AI. By sending multiple orders. At the same time. The problem is this: if the operators run the AI using a local server, there is a possibility that the system. Does not have the resources to complete multiple missions. That comes in a short period. 

One of the things that wastes resources is the situation where the system must generate answers to the FAQs. Those frequently asked questions can be stored in a database, and the system can offer answers. Those are already generated for the FAQs. Otherwise, science advances, and some old answers are turning. Old-fashioned. That means the system must sometimes check sources. These are used for FAQ databases. And then update those databases without commands. 

The system must have the ability to filter overlapping requests. The system must have the ability to use the database of used requests. And then offer answers that are already generated. That saves. The system resources. It must not generate all answers separately in questions that have already been asked. 

https://scitechdaily.com/ai-is-learning-to-be-selfish-study-warns/