Archiving and retrieving information transcend the act of mere data storage; they represent an enduring human pursuit to preserve experience and knowledge. These processes reflect our collective desire to understand the past, navigate the present, and shape the future. Yet, in an age of overwhelming information abundance, it is imperative to critically examine the systems we use to manage this deluge.
At its core, remembering is a battle against entropy. The universe’s natural tendency towards disorder necessitates a conscious effort to retain meaning. I think of this act of remembering as similar to “swimming upstream,” a task that grows exponentially more challenging over time. However, a complementary perspective of forgetting, as my friend Deb aptly described, suggests that forgetting is not inherently negative. It creates space for new ideas and adaptations, much like composting transforms discarded matter into fertile soil.
The tension between preservation and forgetting underscores the need for nuance in how we approach building knowledge archives. Hoarding every piece of data indiscriminately is neither practical nor meaningful, unless it is queryable and retrievable with its context. Instead, we should prioritise data that fosters understanding and insight. Intent becomes a critical factor here; archiving the motivations, decisions, and contexts behind actions often provides more value than the actions themselves.
Here’s the thing: collective memory often appears orderly when viewed as linear history, but it is chaotic to individuals within the system. To avoid biased narratives, it is essential to capture diverse perspectives, similar to viewing a single event through the eyes of multiple characters in a story.[1]
Before diving deeper, it is crucial to differentiate between information[2] and knowledge. Information consists of raw, unprocessed data—facts, figures, and records that can be objectively verified. Knowledge, on the other hand, is contextualised and internalised information, shaped by human understanding, interpretation, and experience. Archiving, therefore, is not merely about storing information but about enabling its transformation into knowledge. Misrepresenting this distinction undermines the purpose of archival systems.
Philosophy offers profound insights into the act of remembering, situating it within the broader frameworks of epistemology, metaphysics, and ethics. Contemporary thinkers like Luciano Floridi highlight the ethical dimensions of memory, suggesting that information management is not a neutral technical process, but a deeply ethical practice involving careful consideration of preservation, access, and potential impacts[3][4][5]. Remembering, thus, is both a cognitive and moral endeavour, rooted in the human pursuit of coherence as we observe and negotiate the flux of existence.
Absurdist literature, such as Lewis Carroll’s Alice in Wonderland and Sukumar Ray’s Abol Tabol, teaches us that even seemingly nonsensical narratives can hold deep meaning. Reading these works early on life influenced my view of logic and structure, instilling in me the view that systems must balance rigidity with adaptability.
By deconstructing existing tools and rethinking their functions, we can design systems that model complex, dynamic realities. This approach reflects in Deb’s idea of “Anthropic Inertia” — the tendency of human systems to resist change while gradually evolving through external pressures.
A decentralised approach to history acknowledges the multiplicity of truths and the fallibility of singular narratives. By employing local-first, decentralised data management, we can create systems that prioritise individual agency while fostering collective memory. Such systems grow organically, connecting disparate perspectives into a cohesive whole without imposing centralised authority.
Further, as I mentioned earlier, I consider intent as a driving force behind action. Archiving intent alongside events allows future generations to understand not just what happened but why. With effective summarisation, such archival systems can generate maxims while providing traceable, version-controlled history, thereby, tracking how knowledge evolves over time to ensure transparency and adaptability. Think of how archiving the development of ethical principles and emergent scientific theories in the form of a log of intent-driven iterations can provide valuable insights into the evolution of human thought over time.
Criteria for effective knowledge archival systems
Given the complexities of memory and the subjective nature of interpretation, any knowledge archival system should address the following key criteria:
1. Adaptable data model
In a world where data volume grows exponentially, systems must adapt to handle both complexity and scale. Knowledge graphs offer a promising solution by representing information as interconnected nodes and edges. This dynamic structure enables the seamless integration of diverse data types and relationships, making it a powerful tool for evolving archives.
2. Context awareness
Data divorced from its context loses much of its meaning. Effective archival systems must go beyond capturing raw data to include the surrounding environment, intent, and motivations. Contextual awareness is particularly vital in transforming information into knowledge.
3. Generate perspectives
A single perspective can never encapsulate the full truth of an event. Systems may be designed to incorporate diverse viewpoints, mitigating biases and fostering a more comprehensive understanding. One way to achieve this is by enabling targeted queries that highlight alternative perspectives.
4. Tamper-proof history
In an age where digital data is easily manipulated, ensuring authenticity and immutability is critical. Technologies like zero-knowledge proofs, content-addressable representation, and smart contracts can provide a tamper-proof foundation for archiving systems. This guarantees that once recorded, historical data remains unaltered, preserving its integrity.
5. Intelligent search and retrieval
As archives grow, finding specific information becomes increasingly challenging. Sophisticated search and retrieval mechanisms, powered by AI and machine learning, can address this issue. By employing natural language processing and semantic understanding, these systems can surface relevant information based on user intent rather than rigid keyword matching.
6. User-driven annotation and graph enrichment
Empowering users to contribute to archives through annotations fosters collective responsibility for knowledge preservation. However, this requires balancing open participation with content quality. Moderation mechanisms, such as peer review or reputation-based systems, can ensure that contributions are accurate and meaningful. Though, federated knowledge graphs for individuals can do this better.
By integrating these principles, we can design systems that empower us to remember, understand, and learn. Such systems should:
- Embrace knowledge graphs to handle complexity and scale.
- Capture context to preserve the richness of human experience.
- Support multiple perspectives to foster inclusivity and mitigate bias.
- Employ tamper-proof mechanisms to ensure data integrity.
- Leverage AI for intelligent search, retrieval and summarising.
- Encourage user-driven curation to democratise knowledge enrichment.
Archiving is not just about storing information; it is about crafting a narrative that honours the past while remaining open to reinterpretation. By adopting a systems approach, we can navigate the complexities of memory, balancing the preservation of knowledge with the inevitability of change.
Imagine if Lord of The Rings was retold from the perspective of every character. I would like to read Sauron and Samwise Gamgee’s version. Did Sauron regret coming out (as Sauron) to Celebrimbor? How else can we verify if Samwise was a closeted gay in a strictly cis-heteronormative society? ↩︎
See: Adriaans, Pieter, “Information”, The Stanford Encyclopedia of Philosophy (Summer 2024 Edition), Edward N. Zalta & Uri Nodelman (eds.). ↩︎
Floridi, L. (2014). The Fourth Revolution: How the Infosphere is Reshaping Human Reality. Oxford University Press. ↩︎
Floridi, L. (2010). Information: A Very Short Introduction. Oxford University Press. ↩︎
Floridi, L. (2013). The Philosophy of Information. Oxford University Press. ↩︎