Biological, Intelligent, and Information Systems as Processing Entities
I have chosen to explore the origins of life from a systems engineering perspective as my personal research theme.
Biological cells are assemblies of chemical substances, and life activities are sustained through a series of complex chemical reactions. This article examines the chain of chemical substances from the viewpoint of processing, akin to information processing in computers or brains, to explore similarities between biology, intelligence, and information processing systems.
Network of Chemical Substances
Imagine a solution in a container, dissolved with various chemicals, continuously supplied with energy from external sources like heat or light, or easily extracting energy from chemicals within it.
When some trigger is applied to this solution, chemical reactions occur. The outcome of the initial reaction may serve as a trigger for subsequent reactions, potentially repeating within the solution.
We will explore patterns of these chain reactions.
Patterns of Chain Reactions Triggered by a Single Event
Let’s refer to chemical reactions as processes.
First, we consider the case of a single trigger, which may lead to linear processes, process branching, and process loops.
Linear processing simply follows one process after another.
Process branching happens when multiple processes can occur due to an external trigger or as a result of a preceding chemical reaction. In the solution, which process happens depends on which chemicals meet first.
Process loops occur when the outcome of a process generates a chemical that serves as a trigger for a previous step in the chain. Though loops eventually cease due to finite energy and reactants, certain loop structures can repeat the same process chain.
Linear processes, branching, and loops are concepts familiar in computer programming, suggesting that the chain of chemical reactions in a solution can have a processing structure similar to computer programs.
Cases with Multiple Triggers
We consider cases with multiple triggers, both of the same kind and of different kinds.
For identical triggers, this scenario represents a situation where multiple instances of a chemical enter the solution. In reality, it’s challenging to introduce only a single molecule, making multiple entries the norm. Furthermore, a drop of water can contain countless molecules, making this a common scenario.
With countless identical triggers, the branching process differs from the single-trigger case. Instead of selecting one branch, the presence of numerous triggers allows for parallel processing across all branches, according to their probabilities.
In the case of different types of triggers, each trigger leads to its own chain of reactions. Some of these chains may converge if they produce the same chemical or if a reaction requires multiple chemicals produced by different triggers.
With multiple types of triggers, each usually present in countless numbers, both branching and merging processes are comprehensively covered.
Thus, a single event can lead to a network of processes that encompass both branching and merging within the solution.
While a single-trigger process chain can be likened to a computer program, chains with multiple triggers resemble the structure of neural networks commonly used in artificial intelligence.
Neural networks receive multiple types of inputs at various nodes, propagating comprehensively throughout the network and producing diverse outputs at multiple nodes. This mirrors the described chemical processing conceptually.
Processing Foundations and Models
In machine learning, a trained neural network is referred to as a model. Such models are used in AI applications for tasks like identifying objects in images or answering questions posed in text.
In this sense, implemented programs can also be called models, as they enable various applications.
The environment in which neural networks or programs operate can be considered a processing foundation. A model must be provided to this foundation for it to perform intended processing. The foundation itself is a general environment that does not execute specific tasks without a model.
Applying this to chemicals, the container with the solution and external energy forms the processing foundation. Without a specific network model of chemicals, it does not perform a specific task.
Cells, with their intricate chemical compositions, serve as models in this context. The cell membrane and cytoskeleton are the container, and the cytoplasm is the solution, with various organelles and the DNA-to-RNA-to-protein synthesis mechanism functioning within.
Cells are the smallest units of life, containing processing foundations capable of chemical network processing, similar to neural networks or programs, with complex chemical models developed through evolution leading to life’s emergence.
Self-Organization of Models
Thus, living organisms as solutions of chemicals, intelligence with constructed learning models in neural networks, and computer systems running implemented programs share the characteristic of being processing entities composed of a general processing foundation and specific models.
This includes sequential, branching, and looped processing, with the ability to form network structures that comprehensively handle parallel branching and merging.
Models form through learning in neural networks and through implementation in programs. Life likely evolved sophisticated models naturally, akin to learning processes.
The transition from non-living to living entities likely involved feedback loops similar to those in AI learning, driving the evolution of chemical substances.
Considering the implementation, learning, and evolution of models as processes on a general processing foundation toward specific processing groups, we can see the formation of individuality, or identity.
In my research on the origins of life, I consider these feedback loops and the formation of identity as critical points. They are essential elements for models to self-organize within complex systems.
This concept of self-organization is crucial in understanding how, from a collection of non-living chemicals, life emerged as a self-sustaining, evolving entity. Similarly, it sheds light on how artificial intelligence systems can develop and refine their capabilities through learning and adaptation.
In Conclusion
What is life? How did biological organisms originate? What are intelligence and consciousness? There are various questions surrounding life and intelligence.
As discussed in this article, viewing both from the perspective of processing foundations and models reveals commonalities that might point to a unified answer to these mysteries.
The boundary between non-living and living entities, similar to the divide between non-conscious AI and conscious humans, might be more a matter of complexity and organization than a strict dichotomy. Research into the origins of life could thus offer insights into the nature of intelligence itself.
Moreover, just as neural networks were developed with inspiration from the networks of neurons in the brain, developing information processing systems based on biological chemical networks might lead to technologies with capabilities distinct from current software and AI.
Such exploration into the parallels between life’s origin and the development of intelligence and consciousness highlights the profound interconnectedness of natural and artificial systems. It suggests that the principles underlying the emergence of life on Earth might also guide us in creating more advanced and capable forms of artificial intelligence, underscoring the importance of interdisciplinary research in unraveling these fundamental mysteries of our universe.
