TECH GRIMOIRE

The more I ponder about this, the more I realize that much of our coding and social structures are based on natural systems. I believe that computational systems can simulate natural processes—and that nature itself can often be thought of as a computational system. Sometimes, I see no difference between the two.

About Natural Computing

We are reinventing nature through our technological processes. Consider innovations such as Velcro inspired by burdock plant burrs, bulletproof materials inspired by spider webs, airplane designs influenced by birds, and even social organizations modeled after ant colonies.

In this context, today let’s explore some interesting aspects of natural computing. and its applications in our daily lives.

"Natural computing is the computational version of the process of extracting ideas from nature to develop computational systems."

— de Castro, L. (2007). Fundamentals of Natural Computing: An Overview. Physics of Life Reviews.

Systems and algorithms

Among all the natural computing approaches, today we going to talk about algorithms and systems inspired by nature because they are the oldest and the most popular ones. But there's other ways to approach this content.

Many approaches are used to develop natural computing and its main branches

The one of mainly objectives to use natural computing today involves to help computational systems to improve her works and create alternative solution techniques to problems that could not be resolved by other traditional techniques

• Artificial Neural Networks (ANNs)

Artificial Neural Networks (ANNs) are computational models inspired by the human brain's architecture. They consist of interconnected units, known as artificial neurons, which process information similarly to biological neurons. Each artificial neuron receives input signals, applies weights to these signals, sums them, and then passes the result through an activation function to produce an output.

ANNs are commonly used in voice recognition systems, helping virtual assistants like Siri and Alexa process voice commands. They also analyze medical images to detect diseases like cancer and heart problems early. Financial companies use them to predict market trends and improve investment decisions. Social media apps and smartphones rely on ANNs for image recognition, and their applications continue to expand in various fields.

Diagram of an artificial neural network (ANN) with input neurons, hidden layers, and output neurons. The network adjusts weights based on errors calculated from training data using a training algorithm.

This diagram illustrates the learning process of an artificial neural network (ANN). Input data is fed into the network, passing through multiple layers of interconnected neurons. Each connection has a weight that determines the strength of the signal between neurons. The network generates an output, which is then compared to the expected target values. The difference between the predicted and correct outputs, known as error, is used by a training algorithm to adjust the weights. Through repeated iterations, the network minimizes this error, improving its ability to recognize patterns and make accurate predictions.

• Mathematical Model of ANNs

• $x_i$ (inputs) – Represent the input neurons in the diagram, which receive raw data.
• $w_i$ (weights) – Correspond to the adjustable connections between neurons that influence how signals propagate through the network.
• $b$ (bias term) – Helps shift the activation function, allowing the network to learn more complex patterns.
• $f$ (activation function) – Represents the transformation applied at each neuron, determining whether the signal should pass forward.
• $y$ (output) – Matches the final outputs of the network, which are compared with target values to compute the error.

By continuously adjusting the weights $w_i$, the training algorithm reduces the error over time, improving the network's accuracy. This iterative process enables ANNs to make precise predictions and recognize patterns effectively.

• Swarm Intelligence (SI)

SI draws inspiration from the collective behavior observed in social insects, bird flocks, and fish schools. One popular method in this domain is Particle Swarm Optimization (PSO). In PSO, each “particle” represents a candidate solution, and the motion of each particle is influenced by its own past experience and the collective experience of the swarm.

• Mathematical Model of PSO

Where:

• $x_i(t)$ is the position of particle $i$ at time $t$,

• $v_i(t)$ is its velocity,

• $p_i$ is the best-known position of the particle,

• $g$ is the best-known position of the swarm,

• $\omega$ is the inertia weight controlling exploration vs. exploitation,

• $c_1$ and $c_2$ are cognitive and social coefficients,

• $r_1$ and $r_2$ are random values between 0 and 1.

• PSO Web Application

I developed an interactive web application to visualize PSO in action. You can explore it here:

pso-ai.vercel.app

https://pso-ai.vercel.app/

• How It Works

1. Initialization: The system sets the number of particles and coefficients (C1, C2).
2. Particle Distribution: Particles (blue dots) are randomly positioned in a 2D space.
3. Target Definition: The red dot represents the optimization target.
4. Iteration Process: Each particle updates its velocity and position based on the formulas.
5. Progress Tracking: A table displays each step of the green dot, showing the particle coordinates at every step of the algorithm.

Each of these techniques (ANNs, and SI) exemplifies how computational systems can mimic natural processes. They serve as modern tools for solving problems that traditional algorithms often struggle with, reflecting the core idea of natural computing: using nature’s own strategies to inspire innovative computational solutions.

With many problems in our modern society, how do i know when use natural computing to solve them?

Given that nature has proof of time, every challenge we face has already been addressed through its processes. By employing one or more natural computing approaches, we can solve problems, synthesize complex phenomena, or answer fundamental questions—and, at the very least, gain valuable insights. Overall, natural computing appears to be used when:

• Complex problems that involves a large number os variables, N different solutions that is always changing...
• We can't be sure a solution is the best, but we can measure its quality to compare it with others.
• Natural systems, including biological, physical, and chemical processes, must be realistically replicated.

Natural computing has taught us to think ‘naturally’ about computation and also to think computationally about nature - DECASTRO, L.