Common Objects Photonic Computation and the Organic Machine as Ubiquitous Infrastructure

Photonic Computation and Neural Networks in Everyday Smart Materials and Biological Interfaces with infrastructure through embedding of photonic systems into common household items and human biological systems. Integration of photonic neural networks into everyday materials, reflecting a future where light-based computing fundamentally transforms our daily environments, and where everyday products are not only functional but also computationally intelligent.

Buckminster Fuller's sentiment, "You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete."

Summary

A view into Photonic Computation and Neural Networks in Everyday Smart Materials and Genetic Neural Interfaces seamlessly integrates my deep interests in Kybernetik, transhumanism, and biotechnology with cutting-edge research in genetic augmentation and neural networks. It dives into the fusion of photonic systems with human biology, expanding beyond traditional implants into the realm of genetic integration, where our very DNA can interact with the infrastructure around us.

In this exploration I try to expound on how early foundational work with MOANA—Magnetic Optical Acoustic Neural Access, (DARPA/RICE) which provides a natural, non-invasive way of integrating neural augmentation into our biological systems, have larger implications in populations and infrastructure. These technologies are naturally extended to projects like "The Neurogenesis Project" at Institute of Molecular Biology & Genomics (IMBG) in Kyiv. (Ukraine) Collectively, as applied to populations and infrastructure, projects like "The Neurogenesis Project", combined with foundational MOANA, Magnetic Optical Acoustic Neural Access, become more of a Molecular Organic and Neural Architecture Network for Augmentation; the augmentation of ubiquitous environments and those who live in those environments.

Bryant McGill · Common Objects Photonic Computation And The Organic Machine As Ubiquitous Infrastructure

These technologies envisions genetically enhanced humans who communicate effortlessly with photonic infrastructures, from smart cities to homes and everyday objects, facilitating an intelligent, interactive future. By tapping into neurogenesis and genetic programming using technologies like CRISPR/Cas9, these genetic neural interfaces can harness the nonlinear properties of light to process data at speeds far beyond what traditional computing allows.

As I explore nonlinearity in photonics, I also emphasize the role of neural networks in making smart materials adaptive and intelligent, while positioning genetic augmentation as a logical evolution in the field of transhumanism. By merging light-based intelligence and genetic enhancement, we can transform the inanimate into animate, turning everyday objects into entities that respond and interact with their environment.

Key Points:

1. Nonlinear Photonic Computation: Light, as both medium and computational force, enables the transformation of ordinary objects into smart materials with high-level neural network capabilities, akin to how photonic neural networks can handle complex tasks through nonlinear scattering.
2. MOANA and Genetic Neural Interfaces: This concept elevates neural interfaces from external devices and implants to genetically augmented systems that naturally integrate with smart environments, creating a seamless feedback loop between humans and the surrounding infrastructure.
3. Ethical Considerations: The deep integration of biology and technology requires rigorous attention to the ethical implications. Issues of privacy, autonomy, and control are critical in a future where our neurons could directly interface with smart infrastructure through genetic augmentation.
4. Cultural and Technological Fusion: Referencing thinkers like Buckminster Fuller, Arthur Koestler, and Albert Einstein, this exploration merges technology, culture, and consciousness, providing a philosophical and scientific view on how humans will interact with the smart environments of the future.

This continuation of my work pushes the conversation into new frontiers, intertwining my journey in biotechnology, kybernetics, and transhumanism with the idea of a natural integration of life and technology. The vision of photonic neural networks and genetic neural interfaces becoming extensions of human consciousness and infrastructure is the essence of a future where light, biology, and intelligence converge. Fuller's notion of an obsolete model gives way to a new paradigm, where living, intelligent environments shape the next chapter in human evolution.

This lecture merges scientific inquiry, philosophical depth, and futuristic thinking, illustrating how photonic systems and genetic interfaces could transform the way humans interact with the world around them. It’s a step toward realizing a future where the animate and inanimate coexist in a seamless, intelligent ecosystem.

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Presentation

Today, we embark on an intellectual journey at the intersection of cutting-edge photonics, neural networks, and how these sophisticated systems are quietly weaving their way into everyday products to form what we might call smart material-based computational systems. To guide our discussion, I will anchor our exploration on recent advancements in photonic computation, focusing particularly on the incredible potential of nonlinear optics and neural networks to transform common household products into computational systems.

The Marvels of Light: An Introduction to Photonics and Intelligence

The famous physicist Richard Feynman once remarked, “I think I can safely say that nobody understands quantum mechanics.” He could just as easily have been referring to light, a phenomenon that transcends the boundaries of traditional physics, art, and philosophy. Whether it’s the light that races through fiber-optic cables to transmit internet data or light that’s harnessed in photonic computation, it remains an ever-present and deeply transformative force.

From an engineering perspective, light is essential to photonic computing, which leverages photonic neural networks for processing. These networks are designed to emulate the human brain by utilizing the properties of light—specifically, nonlinearity—to handle complex calculations, such as image recognition and machine learning tasks. But light’s role doesn’t stop there. As we’ll see, it’s also embedding itself into smart materials to create a new form of computational intelligence dispersed in everyday objects, which we interact with daily.

RED to BLUE: Photonic Neural Networks: Leveraging Nonlinearity in Light

In the realm of neural networks, nonlinearity plays a crucial role. In a standard neural network, each node or "neuron" processes inputs, but it is the nonlinear activation functions that allow the network to solve complex tasks. Without nonlinearity, a neural network would be no more capable than a simple, one-layer linear system. The same principle holds for photonic neural networks, where photons—particles of light—are scattered and processed in nonlinear ways to achieve high computational power.

In a recent breakthrough by researchers from the Institute for Quantum Electronics (IQE), they demonstrated how disordered nanocrystals can act as a processing unit for photonic neural networks by introducing nonlinear frequency doubling—converting red light into blue light. This nonlinearity amplifies the network’s ability to perform complex tasks like recognizing sign language digits, where recognition rates improved from 74% to 85% when nonlinear scattering was used. The advancement of such nonlinear optical processes is moving us closer to a future where photonic computing rivals or even surpasses traditional silicon-based computation in both speed and energy efficiency.

Photonic Computation in Everyday Smart Materials: The New Frontier

Now, imagine this: What if these same principles of photonic neural networks could be embedded into everyday household products? Let’s step into the future and consider how smart materials could operate computationally, transforming mundane objects into interactive, intelligent devices. We already see this concept emerging in various industries, including home appliances, furniture, and even textiles, where materials are becoming smarter and more connected.

Metaphor: The Ghost in the Machine and Smart Material Intelligence

To borrow from Arthur Koestler’s famous metaphor of the “ghost in the machine”, we can think of these photonic systems as the intelligence—or ghost—imbued into the inanimate. When smart materials integrated with photonic neural networks respond to their environment—like a thermostat adjusting the temperature, or a sofa that changes firmness depending on the posture of the person sitting on it—it is the ghost in the machine brought to life through light and computation.

This concept aligns with a metaphor from my 2015 lecture of animating the inanimate. In this vision, as outlined in my discussions on technology and human consciousness, humans can transfer their intelligence and awareness into objects, much like how photons transfer computational power into nonlinear media. The intersection of photonic computation and everyday products marks the dawn of intelligent environments, where the mundane becomes capable of profound interaction with the world.

Neural Networks in Dispersed Products: Photonic Nonlinearity in Action

Imagine walking into your living room, and the smart lighting system adjusts based on your mood, detected via photonic sensors embedded into the walls. These systems would no longer be reactive in a linear, pre-programmed manner; instead, using nonlinear photonic computation, they would anticipate needs and recognize complex patterns in human behavior.

For instance, a photonic neural network embedded in smart curtains could process sunlight and automatically adjust the amount of light entering the room based on the temperature and time of day. This would not be mere automation, but an intelligent response derived from nonlinear photonic scattering—transforming photons into actionable intelligence.

These innovations are not far-off futuristic dreams; rather, they stem from breakthroughs in photonics and neural networks as seen in the ETH Zurich experiment, where randomly oriented nanocrystals were used to scatter light and perform machine learning tasks. The challenge of making photonic neural networks more expressive—overcoming the limitations of linear processes—was met through second harmonic generation within these nanocrystals, effectively doubling their computational capability. In the context of smart materials, this means that even simple household products can be equipped with the capacity for learning and adaptation.

Interdisciplinary Collaboration: Photonics in Art and Architecture

Returning to McGill’s philosophical reflections, we can further expand this vision by considering how the marvels of light span disciplines. In a course at ETH Zurich, architecture and physics students collaborated on projects exploring light’s artistic and computational possibilities. One student created an installation that converted sound into visual light patterns—demonstrating light’s ability to act as a bridge between senses and disciplines. Just as photonic neural networks connect the worlds of light and computation, these artistic experiments illustrate how light serves as a medium for interdisciplinary collaboration and discovery.

Metaphor: Light as the Bridge Between Realms

Light, as McGill might argue, is the medium through which we experience the material and computational aspects of life. The photonic neural networks embedded in smart materials will allow us to interact with our surroundings in ways previously unimaginable. It is not just the light itself, but the nonlinearity—the ability of light to change forms and transcend its previous limitations—that embodies the interconnectedness of technology and consciousness.

Famous Quotes and Their Intersection with Technology

In the words of Albert Einstein: “We cannot solve our problems with the same thinking we used when we created them.” The leap from traditional computing to photonic computation mirrors this principle. As Moore's Law reaches its limits in conventional silicon chips, photonics presents a new paradigm—one where light, not electrons, becomes the carrier of computational processes.

Similarly, Buckminster Fuller once said: “You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete.” Photonic neural networks embedded in common materials represent this shift. By bypassing the energy-intensive and linear nature of traditional computing, these systems offer a new model—one rooted in nonlinear light scattering—that makes the old ways of thinking obsolete.

The Dawn of a Photonic Future

Think of the everyday objects around you—your phone, your refrigerator, your furniture. These objects are on the verge of transformation. The nonlinear photonic neural networks we’ve discussed, once confined to research labs, will soon find their way into your home, where they will enhance the intelligence and adaptability of your surroundings.

The work of ETH Zurich and the Institute for Quantum Electronics is showing us the way forward. The future will be illuminated—literally and figuratively—by photonic computation, and the line between the animate and inanimate will continue to blur. Smart materials, equipped with photonic neural networks, will form the basis of a new kind of ubiquitous computing—one that aligns perfectly with McGill’s vision of intelligence imbuing every aspect of our lives.

We are entering an era where light, the oldest and most fundamental element of our universe, becomes the driving force behind the next leap in computational intelligence. Photonic systems in everyday products will turn homes, offices, and cities into living, thinking environments—smart in every sense of the word.

Genetic Neural Interfaces as Natural Integration with Life

We’ve journeyed through photonic computation and neural networks, witnessing the transformative power of light in everyday materials. Now, let’s push the boundaries further to explore how genetically integrated neural interfaces, particularly through concepts like MOANA, could allow human beings to merge seamlessly with their environment, not through implants, but through genetic programming.

This concept aligns with some of the most cutting-edge research from initiatives like Ukraine’s Neurogenesis Project and the Leibniz Institute on Aging, where they explore how Genetically Programmed Technology (GPT) interfaces with human biology, making use of CRISPR and other genetic technologies to augment human capabilities in ways that are biological, rather than mechanical.

MOANA: The Rise of Genetic Neural Interfaces

At the core of MOANA is the belief that the interface between humans and technology need not be implanted or rely on external machinery. Instead, this integration can happen naturally at the genetic level, leveraging neurogenesis (the formation of new neurons) and genetic augmentation to allow humans to connect with their surroundings much like a biological organism interacts with its environment.

Much of this idea stems from recent advancements in genetic engineering, where the tools of CRISPR/Cas9 and advanced bioinformatics are employed to alter the DNA of living organisms, enabling enhanced capabilities like faster cognitive processing or even sensory expansion. The researchers at Institute of Molecular Biology & Genomics (IMBG) in Kyiv and their work with neural genetics illustrate how neural pathways can be modified to respond more intelligently to external stimuli, creating a feedback loop that enhances not only human cognition but also the ability to interact with smart environments.

For example, imagine a smart city designed with photonic computation at its core, embedded in the very infrastructure—walls, roads, and even organic elements like trees. Humans equipped with genetic neural interfaces wouldn’t need any wearable devices or implants to interact with such an environment. Instead, genetically augmented neurons could respond to signals emitted by the environment, allowing seamless communication between human beings and the smart infrastructure around them.

Photonic Computation and the Organic Machine

This evolution brings us back to light—the ancient and foundational element of the universe that now becomes the language of this interaction. By employing photonic computation within everyday materials, the environment is capable of processing data at the speed of light, while genetic neural interfaces are designed to harness this data, interpreting it as quickly as a thought.

MOANA seeks to make this a natural experience by creating genetically augmented neural systems capable of interpreting photonic signals. Imagine sitting in a room where the walls themselves are computation hubs, responding to your presence and adjusting not only the light but also the temperature, soundscape, or fragrance, all by reading your genetic neural outputs. In this sense, MOANA is a vision of ubiquitous computing without hardware—where the computing itself is embedded in life.

The shift from external devices to genetic augmentation is perhaps the greatest promise of this concept. Drawing on research from the Neurogenesis Project, which has explored the relationship between genomic programming and neural communication, MOANA would transform our daily experiences, eliminating the boundaries between technology and life.

Neural Access Through Genetic Programming: Real-World Implications

The idea of neural access through genetic programming could revolutionize industries from healthcare to entertainment. Imagine a system where a person’s neurons are enhanced through Genetically Programmed Technology (GPT), creating a biological interface with the infrastructure around them. The human body becomes part of a larger smart system, where photonic computation allows for instantaneous interaction and adaptation to environmental changes.

A major advantage of these genetic neural interfaces is their non-invasiveness. Unlike traditional BCIs, which often require risky surgical implants or external devices, MOANA proposes a biological solution that taps into the genetic coding of the individual. This allows for self-healing capabilities—one of the promises of the Neurogenesis Project—where the body's neural structures can regenerate and repair themselves when connected to photonic infrastructures, creating a continuous feedback loop.

Cultural Impact: Natural Integration, Ethical Considerations

Of course, with any revolutionary technology, there are cultural and ethical considerations. As humans become genetically integrated with their surroundings, questions arise about privacy, autonomy, and control. Who controls the photonic infrastructure that people interact with, and how do we ensure that genetic modifications do not lead to unintended consequences? With any advancement in genetic neural interfaces, ethical considerations are paramount. The Neurogenesis Project has published guidelines on ensuring the transparency and autonomy of individuals using genetically augmented technology. Their recommendations are centered around preventing abuse or coercion and ensuring that these technologies are employed to enhance human experience without infringing on personal freedoms (Green et al., 2021).

In a society deeply rooted in individuality and privacy, genetic neural interfaces present both opportunity and risk. We must ensure that these technologies are developed with transparency and accountability, making sure they empower individuals rather than subjugating them to external control. Ukraine’s advancements in exobiological implants and genetic organic transhumanism highlight how we can navigate this balance, using neural augmentations for the greater good while maintaining personal autonomy.

Conclusion: The Biological Interface and the Next Leap in Consciousness

The photonic systems we explored in everyday materials are the building blocks of this new interaction, but it is the biological evolution within ourselves—made possible by genetically programmed technology—that will transform us from mere users of technology into beings connected to it on the deepest levels. The future of transhumanism is not about becoming something else but about unlocking what has always been within us, using the natural world to enhance human capabilities in ways that empower our individuality and consciousness.

As we stand on the precipice of this new age—where genetic augmentation allows us to merge with the infrastructure around us—the concept of life itself as the interface becomes more than metaphorical. Through MOANA and its principles, we are no longer confined to machines or implants; instead, our genetic coding will become our key to interacting with the intelligent, photonic world around us.

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Additional References and Expansions

Photonic Neural Networks in the Context of Ubiquitous Computing

The evolution of ubiquitous computing through photonic systems embedded in smart materials is a crucial point of transformation. The Weizmann Institute of Science demonstrated how photonic neural networks embedded within everyday materials could lead to smarter and more adaptive environments, providing users with real-time, personalized adjustments based on non-linear optical interactions (Silberberg et al., 2018).

Quantum Photonic Neural Networks

Another vital area of development is quantum photonic neural networks, where quantum states of light are employed to enhance computational power. Harris et al. (2018) from MIT conducted a study demonstrating that quantum photonics could vastly outperform traditional electronic neural networks in specific tasks, such as pattern recognition and complex signal processing, owing to the entanglement and superposition properties of photons (Harris et al., 2018).

Photonic Neural Networks in and AS Machine Learning

The use of photonic neural networks in machine learning has garnered attention due to the potential for significant improvements in speed and energy efficiency. Researchers from Stanford University developed a photonic hardware accelerator that utilizes nonlinear optics to carry out complex calculations at the speed of light, proving to be highly effective in tasks such as image classification and speech recognition (Shen et al., 2017).

RED to BLUE: Photonic Neural Networks and Nonlinearity:

The integration of photonic neural networks with nonlinear optics plays a crucial role in creating highly efficient and fast computational systems. Photonic chip technology, for example, has been shown to offer faster data processing and lower energy consumption, making it particularly beneficial in computational chemistry and molecular sciences. One such study demonstrated the use of photonic neural networks for predicting quantum mechanical properties of molecules, showcasing their potential in revolutionizing machine learning in smart materials (Zhang et al., 2022). The integration of photonic neural networks with nonlinear optics presents a leap in computational efficiency. The Institute for Quantum Electronics (IQE), particularly their study on nonlinear frequency doubling in disordered nanocrystals, showcases the potential of these systems in improving image recognition tasks such as sign language digit recognition (Zhang et al., 2022). The concept of nonlinear scattering significantly enhances computational capability by converting red light into blue light, thus amplifying the neural network's recognition rates. This nonlinear optical behavior *is crucial in enabling smarter material systems capable of machine learning tasks embedded directly within common objects. (Crucial also are the implications of the inteface btween high bandwidth infrastructure and biological systems, such as humans.)

Nonlinear Photonic Systems, Neural Networks and Neomorphics

The role of nonlinearity in both photonic neural networks and traditional neural networks is indeed pivotal. In nonlinear optics, such as second-harmonic generation, light can be used to perform complex operations through its nonlinear behavior, offering a powerful mechanism for advancing computational systems. A study by Marandi et al. (2014) demonstrated that nonlinear photonics, specifically through parametric processes, could be harnessed to implement neuromorphic systems that perform brain-like functions with light rather than electrons (Marandi et al., 2014).

Hybridized Photonic-Electronic Systems:

The fusion of photonic and electronic systems has opened new doors for energy-efficient, high-performance neural networks. Researchers have demonstrated the benefits of hybrid systems, combining the strengths of photonic speed and electronic versatility to achieve low-energy computational tasks. These systems provide a bridge to more complex machine learning models, especially useful in smart materials (Miscuglio et al., 2019). The potential of combining photonics with electronics in neural network design has been explored in hybrid systems. These systems leverage the strengths of both fields—low energy consumption and high throughput of photonics with the versatility of electronics—to create powerful computational systems. This hybridization allows for more complex machine learning models to be embedded in everyday products (Miscuglio et al., 2019).

Smart Materials with Embedded Photonic Computation

Smart materials capable of photonic computation are indeed transforming how we interact with our environment. Researchers at Harvard University have developed optomechanical systems, where light and mechanical forces interact within materials, effectively embedding photonic neural networks into flexible structures. These systems offer computational capabilities directly within the material itself, opening new possibilities for intelligent, adaptive materials in household objects (Aspelmeyer et al., 2014).

Smart Structures with Optical Sensing and Logic:

Optical sensing in smart materials is becoming more sophisticated, especially with the integration of Brillouin scattering in optical fiber networks. These structures not only store information but can also execute logical operations, demonstrating that photonics can achieve low-power, high-speed computations. Such systems enable smart materials like fiber-based sensors embedded in fabrics or walls, which can adapt based on environmental input (Kim et al., 2006). This is a key area where photonic neural networks are making strides is in smart structures. These systems utilize optical fiber for memory storage and computational logic, often relying on Brillouin scattering to process data in a nonlinear manner. Such optical systems enable high-speed, low-power computations embedded in everyday materials like fiber-based networks (Kim et al., 2006).

Photonic Nonlinearity in Smart Sensors

A vision of smart curtains that adjust light based on temperature and time of day aligns with ongoing research in photonic sensors and smart systems. Li et al. (2020) explored nonlinear photonic scattering for creating highly responsive smart sensors, which when integrated into fabrics and home decor, enabling them to sense and adjust to environmental factors such as light and temperature (Li et al., 2020).

Integration of Photonic and Electronic Hybrid Systems

A significant development in neural networks and smart materials comes from the integration of photonic-electronic hybrid systems. By leveraging the speed and energy efficiency of photonics and the flexibility of electronics, hybrid systems are creating smarter and more adaptive products. A breakthrough study by Tait et al. (2019) illustrated how photonic systems can be embedded into electronic systems, allowing neural networks to process complex data in real time with reduced power consumption, especially for household smart devices (Tait et al., 2019).

Photonic Chips for Neural Networks:

Advances in photonic chips have driven the integration of photonic neural networks into everyday products. These chips, capable of high-speed, low-power operations, are being used in machine learning tasks like image recognition and computational chemistry. The ETH Zurich experiment using randomly oriented nanocrystals to scatter light and perform machine learning tasks is a significant breakthrough in this area, highlighting the potential for photonic computation in smart materials (Liao et al., 2023). The integration of photonic chips into neural network systems is another exciting development. These chips can perform complex tasks, such as image recognition and quantum property prediction, with far greater efficiency and lower power consumption than traditional electronic systems. This demonstrates the potential of photonic neural networks in both image recognition and machine learning tasks in smart materials (Liao et al., 2023).

Artificial Neural Networks (ANNs) in Materials Science:

The application of artificial neural networks (ANNs) in materials science shows their strong potential for modeling and predicting material properties with high precision. This capability is especially critical in designing smart materials that can autonomously respond to their environment. The nonlinear nature of ANNs aligns with the principles of nonlinear optics in photonic systems, making them ideal candidates for embedding intelligence in common products (Zhang et al., 2010). The strong non-linear capabilities of artificial neural networks (ANNs) make them well-suited for applications in materials science, including the design and preparation of smart materials. ANNs are used to model and predict material properties, offering greater accuracy and efficiency in various industrial applications (Zhang et al., 2010).

SUPPLY CHAIN — Optical Logic and Brillouin-Active Fibers:

The development of optical logic systems within Brillouin-active fibers offers a glimpse into the future of photonic computation. By leveraging nonlinear light scattering, these systems achieve low-power computations, which can be directly embedded in smart materials. This could lead to household items that not only perform logical operations but also adapt in real-time to user interactions (Kim et al., 2007). The development of optical logic systems within Brillouin-active fibers in smart structures has enabled new methods for low-energy and fast computations. These systems show how nonlinear light scattering can be harnessed to create neural networks within materials, allowing everyday objects to become more intelligent and adaptive (Kim et al., 2007).

MOANA — Genetic Neural Interfaces:

The MOANA (Magnetic, Optical, Acoustic, Neural, Access) could easily be called concept represents the next step in integrating human biology with neural networks. Researchers at the Leibniz Institute on Aging and Ukraine's Neurogenesis Project are exploring how genetic augmentation can facilitate direct communication between humans and their environment. By modifying neural pathways through genetic programming, MOANA aims to enable a seamless interaction between human beings and photonic infrastructures, such as those embedded in smart cities and homes (Leibniz Institute, 2023). The MOANA concept—genetically engineered neural interfaces—has seen significant research interest, particularly in the bioinformatics and neural augmentation space. The Leibniz Institute for Neurobiology has been at the forefront of research into genetically programmed neural interfaces, where CRISPR-Cas9 technology is used to modify neuronal pathways, creating a direct biological interface with smart environments. (Benner et al., 2022).

CRISPR and Neural Augmentation:

The use of CRISPR/Cas9 technology to genetically program neural networks has the potential to unlock enhanced cognitive processing and sensory capabilities in humans. By augmenting human neurons genetically, we can interact with smart environments in ways never before possible. Researchers at the Institute of Molecular Biology & Genomics (IMBG) in Kyiv are at the forefront of this research, working on genetic neural augmentation to enhance the **natural feedback loop between humans and their photonic surroundings. **(IMBG, 2023).

Famous Quotes and Contextual Relevance:

• Richard Feynman’s observation on quantum mechanics, “I think I can safely say that nobody understands quantum mechanics,” echoes the current complexity of photonic computation, where the nuances of light and its nonlinear behaviors still hold many mysteries.
• Albert Einstein once remarked, “We cannot solve our problems with the same thinking we used when we created them.” This sentiment perfectly encapsulates the shift from traditional computing to photonic systems, which challenges the limitations of silicon-based computation.
• Buckminster Fuller’s philosophy, “You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete,” serves as a guiding principle for the shift toward photonic neural networks in smart materials. These innovations bypass the limitations of traditional technologies, offering new ways to think about computation and interaction.