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/