Biomimicry: How We Copy Nature in Tech????

Biomimicry, a fusion of "bios" (life) and "mimesis" (to imitate), is a discipline that takes inspiration from nature's 3.8-billion-year-old R&D. Ever found yourself pondering how a brainless slime mold could school us in artificial intelligence or why a cat's brain has Silicon Valley scratching its head? Well, probably not. But we're here to answer the questions anyway.

Biomimicry in Action

The collaboration between nature's creations and human ingenuity becomes apparent in examples like the kingfisher's beak shaping high-speed trains or the Namibian beetle influencing a water-collection plant.

Similarly, Otto Schmitt's exploration of neural signals in nature paved the way for the Schmitt trigger, a cornerstone in electronics, demonstrating the fusion of biological inspiration and technological innovation. In summary, a Schmitt trigger is a simple yet versatile electronic circuit that can be used to clean up, shape, and compare signals. Imagine you have a garden hose that you're using to water your plants. The water pressure coming out of the hose can vary depending on how hard you're turning the faucet. A Schmitt trigger is like a valve that can smooth out this uneven water pressure. If the water pressure is low, the valve will remain closed, preventing any water from flowing. But if the pressure rises above a certain threshold, the valve will open wide, allowing a full stream of water to pass through.

The link to sustainability becomes palpable as biomimicry aligns with Life's Principles—building from the bottom up, self-assembling, optimizing, embracing diversity, and more. Nature's inherent sustainability becomes a lighthouse for software engineers seeking a huge shift. Could adopting biomimicry smooth the way for sustainable software development, echoing the resilience found in the intricate balance of ecosystems?

Methods

Biomimicry unfolds through two primary avenues: biology-to-design and design-to-biology. In the former, keen-eyed biologists or naturalists observe nature, identifying functions with potential human applications. Conversely, the latter involves inventors actively seeking solutions to specific design problems, like mitigating loud sound waves in tunnels from speeding trains. Notably, biomimetic designs, predominantly documented in papers and patents, often originate from the quests of mechanical engineers, chemists, or designers in search of inspiration from the natural world to address their unique challenges.

Neural Networks

We all know the human brain is the most powerful computer on Earth, capable of performing complex tasks that even the most sophisticated machines struggle with. Inspired by this remarkable organ, scientists have developed neural networks, a type of artificial intelligence that mimics the interconnectedness of neurons in the brain.

Neural networks have revolutionized image recognition, natural language processing, and machine translation, enabling machines to understand and interpret the world around us in ways that were once unimaginable. For example, Google's AlphaFold is a neural network that can predict the 3D structure of proteins with remarkable accuracy. The dataset includes over 170,000 protein sequences and structures, which is the largest dataset of its kind.

Another prime example is the development of convolutional neural networks (CNNs), which are inspired by the visual processing system of the human brain. CNNs have revolutionized image recognition, enabling computers to identify objects, faces, and scenes with unprecedented accuracy. This technology has applications in various fields, including self-driving cars, medical image analysis, and social media content recognition.

The idea of convolutional neural networks (CNNs) was first proposed in the 1980s by Kunihiko Fukushima, a Japanese neuroscientist. Picture the visual cortex, that brain part handling visuals, like a well-organized band. Each layer has its own role—some riff on edges, others on lines, and some on textures. Motivated by the intricacies of the visual cortex, Fukushima envisioned a layered structure where neurons specialize in processing specific image features. This intellectual pursuit laid the foundation for a technological leap, enhancing contemporary image recognition capabilities.

Swarm Intelligence

Ant colonies, beehives, and bird flocks exhibit remarkable collective intelligence, achieving complex tasks through the coordinated actions of individual agents. This concept of swarm intelligence is inspiring the development of distributed algorithms in software engineering.

Distributed algorithms can solve problems by breaking them down into smaller tasks and distributing them among multiple agents, each working in parallel. This approach is particularly well-suited for large-scale systems and cloud computing environments.

For instance, the Bees Algorithm (BA), inspired by the foraging behavior of honeybees, has been successfully applied to optimize task scheduling and resource allocation in distributed computing systems. BA algorithms have also been used to optimize parameter settings in machine learning models, improving their predictive accuracy.

Beyond Algorithms

Biomimicry's influence extends beyond algorithms and techniques, shaping the way software engineers approach development processes and team dynamics.

The concept of self-assembly, observed in biological systems like coral reefs, has led to the development of self-organizing software systems that can adapt and evolve without explicit programming. These systems can dynamically adjust to changing requirements and recover from disruptions, making them more resilient and adaptable. This ability to self-adapt is particularly valuable in complex and dynamic environments, such as cloud computing and distributed systems.

Similarly, the concept of symbiotic relationships, where different organisms coexist and benefit from each other's interactions, has inspired the development of software ecosystems. In these ecosystems, different software components interact and exchange resources to achieve common goals, fostering innovation and collaboration. Even fraud detection systems use algorithms inspired by the immune system to identify fraudulent patterns in financial transactions.

One example is the use of "negative selection" algorithms, inspired by the way our immune system differentiates between self and non-self cells. These algorithms analyze large datasets of transactions to identify patterns that deviate from normal behavior, flagging them as potential fraudulent activities.

Another example is the use of "artificial immune systems" (AIS), which mimic the adaptive nature of our immune system. AIS can learn from past fraud attempts and evolve to detect new and emerging fraud patterns, staying ahead of the curve in the ever-evolving world of financial fraud.