Computation, as the core of information processing, has long surpassed mere mathematical operations and permeated all aspects of our lives. From scientific computing to artificial intelligence, its meaning continues to expand, prompting deeper reflections on its essence. This article aims to break traditional perspectives by connecting computation with human cognition, artificial intelligence, and complex systems theory to explore its fundamental nature and future direction.
Traditional views often confine computation to algorithm execution and coding, overlooking the underlying information processing and cognitive aspects. We believe that the essence of computation consists of two core dimensions:
1. The Realization of Computational Logic
This foundational layer encompasses programming languages, algorithm design, and hardware architecture. Its goal is to efficiently and accurately perform computational tasks by transforming inputs into desired outputs. However, as Moore’s Law gradually reaches its limits, traditional computational models face performance bottlenecks. Emerging paradigms such as quantum computing and neuromorphic computing offer potential breakthroughs by leveraging quantum mechanical phenomena or mimicking neural network structures. These approaches provide more efficient and energy-saving computation, opening new pathways for solving complex problems.
2. Addressing Computational Complexity
Real-world problems are often highly complex, involving vast amounts of data, nonlinear relationships, and emergent behaviors. Traditional computational models struggle to handle such challenges effectively. Advancements in distributed computing, cloud computing, and the application of complex systems theory enhance our ability to manage computational complexity.
• Distributed computing breaks tasks into smaller parts executed in parallel across multiple nodes, improving efficiency and fault tolerance.
• Cloud computing enables on-demand access to computing resources, making large-scale data processing feasible.
• Complex systems theory studies interactions and emergent behaviors within complex networks, providing a theoretical framework for understanding and predicting intricate system behaviors.
The human brain is a highly sophisticated distributed computing system, where each individual processes the world uniquely through their neural networks. This “individualized computation” is a core feature of human cognition and a significant inspiration for AI development. Every brain possesses unique structures and experiences, leading to diverse interpretations of external information. This variability allows humans to perceive the world from multiple perspectives and cultivate creative thinking.
Paths to Artificial Intelligence Development
Artificial intelligence seeks to create computational systems that emulate human intelligence. Currently, AI development follows two primary paths:
1. Connectionism
Represented by deep learning, this approach models neural networks and learns patterns from vast datasets. The success of large-scale language models like GPT highlights connectionism’s potential in fields such as natural language processing. However, these models often suffer from a lack of interpretability and generalization ability.
2. Symbolism
This approach emphasizes symbolic representation and logical reasoning, as seen in knowledge graphs and expert systems. Recently, neuro-symbolic AI has emerged, attempting to integrate connectionist learning with symbolic reasoning. This hybrid approach aims to harness the strengths of both paradigms—combining the pattern recognition capabilities of deep learning with the structured reasoning and interpretability of symbolic AI.
Computation does not exist in isolation; it requires a reference point or observer to hold meaning. Similar to the observer effect in quantum mechanics, the presence of an observer influences computational outcomes. Constructing a “computational world” centered around the observer may help us better understand the essence of computation and the future of AI.
In this computational world, characteristics of complex systems—such as individuality, emergence, and irreducibility—are fully realized. Each individual acts as an independent computational unit, interacting and exchanging information with others to form a dynamic and intricate network. The overall system behavior emerges from these interactions rather than being merely a sum of individual agent properties.
This article reexamines the nature of computation, expanding its scope from mere code execution to broader information processing and cognitive functions. We analyzed the two core dimensions of computation: the realization of computational logic and the management of computational complexity, linking them to human cognition and AI development.
By introducing the concepts of individualized computation and the computational world, we aim to construct a more comprehensive and profound understanding of computation while offering insights into the future of artificial intelligence. As computational technology continues to evolve, we anticipate the emergence of novel paradigms and applications that will drive human progress forward.
