Natural vs Formal
Are large language models (LLMs) more like natural systems than the deterministic machines we usually associate with computers?
🔵 What is a Natural System?
To explore this, we first need to define what a natural system is. Let’s start by understanding what makes natural language "natural." Unlike formal languages—such as Python, lambda calculus, or first-order logic, which are designed with rigid syntactic rules—natural language evolves organically, without strict rules that can be “computed.”
Formal systems, such as mathematics, ontologies, and programming languages, operate in a mechanistic, predictable manner. They’re akin to classical physics: governed by clear rules and producing deterministic outputs.
In contrast, natural systems—such as biology, ecosystems, and human language—are adaptive, complex, and emergent. They evolve in ways that can’t be reduced to pre-programmed, rule-bound models.
🔵 So, What About LLMs?
At first glance, LLMs appear purely formal—they’re built on algorithms and mathematics and run on computers. We might assume they are formal systems, but the more we study them, the more they resemble natural systems in fascinating ways:
🔹 Non-Deterministic Outputs: Like nature, LLMs are inherently non-deterministic. Given the same prompt, they may produce different responses due to their probabilistic nature.
🔹 Distributed Representations: LLMs represent concepts across billions of parameters. No single neuron holds all the information—meanings are distributed across the network.
🔹 Emergent Capabilities: LLMs develop their abilities not through explicit programming but through exposure to vast amounts of text data.
🔹 Contextual Adaptation: LLMs adapt their outputs based on the input context during inference.
🔹 Self-supervised Learning: LLMs aren’t reliant on strict supervision. Instead, they learn patterns through unsupervised training.
🔵 Treating LLMs as Natural Systems
LLMs are still fundamentally engineered systems. Unlike living systems, they have no self-loop, no true world model, and no agency—they are probabilistic rather than anticipatory. But pragmatically, it might be useful to treat them as if they were natural systems.
If we begin viewing LLMs as natural systems, this changes how we approach them. Machines are built to be deterministic and predictable—we can usually dissect their behaviour using reductionism. However, the complexity of LLMs makes it difficult to reduce them to simple, mechanistic rules. This suggests that instead of developing a "physics" for LLMs—a precise science of how they operate—we might need to approach them more like biologists.
Furthermore, if we treat LLMs as natural systems, then we need to pair them with formal systems to carry out true deductive reasoning. What could that formal system be, you ask? Well, how about an Ontology? 😉
