Wiki-Power in A.I.

November 17, 2024 Artifical Intelligence No Comments

In the modern era of vast information overload, the challenge is not just accessing knowledge but making sense of it in a way that fosters deeper understanding and meaningful insights. Enter Wiki-Power — an approach rooted in the interconnected, non-linear nature of knowledge.

Combined with the subtlety and depth of expertext, this approach promises to transform A.I. into a system capable of human-like thinking and hopefully – through its embedding in the AURELIS setting – profound Compassion.

The essence of Wiki-Power

Wiki-Power refers to the ability of a system to connect vast amounts of knowledge through associative and dynamic networks, much like intelligent hyperlinks in an enhanced wiki. By embracing contextual non-linearity from surrounding textual content, such systems mirror the way humans naturally navigate ideas ― jumping between related concepts, uncovering deeper patterns, and synthesizing diverse perspectives.

For example, A.I. powered by Wiki-style knowledge can structure its ‘thinking’ around conceptual graphs, where every node represents a concept, and links between nodes reflect relationships ― causal, hierarchical, or even – specific to this endeavor – contextual. This foundation makes it possible to navigate vast, complex domains – from healthcare to personal growth – in original ways.

Expertext: depth in the details

Expertext, a term first introduced in the 1990s, brought the intelligence of an expert system to the fluidity of hypertext. The aim was to enable A.I. to integrate semantic (what something means) and declarative (what is factual) knowledge. With present-day possibilities that weren’t available back then, this combination is transformative, as it shifts the system’s capabilities from static understanding to dynamic reasoning.

As discussed in the blog “The Return of Expertext,” present-day ‘expertext’ would allow systems to adapt their insights based on context, much like an expert reasoning through a problem. By embedding nuanced patterns within a network of interconnected concepts or conceptual patterns, expertext can thus add depth, helping Lisa (see category ‘Lisa’) to understand users at a subconceptual level. The AURELIS wiki thus becomes part of Lisa’s personality.

Bridging Wiki-Power and expertext

Almost-magic happens when Wiki-Power fully meets expertext. Together, they create a system capable of both breadth and depth:

Wiki-Power ensures the system has access to a vast, interconnected knowledge base.
Expertext fine-tunes this breadth, with the possibility to ‘squeeze’ relevant patterns together to form coherent, contextual insights.

This synergy allows for dynamic responses that are both rational and deep, perfectly aligning with AURELIS values. For example, when guiding a user in an autosuggestion session, the system can draw from diverse areas of knowledge, structure them into personalized suggestions, and adapt dynamically as the user evolves.

Retrieval Augmented Generation (RAG) is a cutting-edge A.I. technique that combines the best of retrieval-based systems (accessing relevant external knowledge) with generation-based systems (creating context-aware responses). In the context of Wiki-Power and expertext, RAG ensures that the system pulls the most relevant, up-to-date information from a vast knowledge base, synthesizing it into meaningful insights. This approach significantly enhances the depth and accuracy – depending on the source – of generated content, enabling A.I. to respond dynamically to even the most nuanced queries.

Lisa: an intelligent navigator and refiner

In the future, Lisa will continually and actively explore the Wiki, leveraging its inherent structure to identify patterns and connections across diverse topics. This dynamic navigation will allow her to engage with the Wiki as a living ecosystem of insights.

Lisa will continuously improve the Wiki by:

Removing redundancies ― streamlining the knowledge base for greater clarity.
Adding hyperlinks ― strengthening connections between concepts, often at analogical levels.
Identifying knowledge gaps ― highlighting areas where additional input is needed, increasingly providing more help to also fill these gaps.
Addressing inconsistencies ― ensuring coherence and logical flow across the texts.

This iterative refinement enhances both the Wiki’s quality and Lisa’s ability to draw deeper insights, creating a virtuous cycle of learning.

Applications in AURELIS and beyond

In the AURELIS framework, this combination unlocks exciting possibilities:

By understanding the user’s subconceptual patterns through profound Wiki-Power, A.I. can deliver tailored suggestions that resonate deeply with the individual.
These systems act as living knowledge bases, evolving as they learn more about users and the world.
Using its nuanced understanding, the A.I. fosters a sense of respect and trustworthiness, aligning with AURELIS’s mission to support inner growth.

The potential impacts many domains like education, healthcare, and sustainability by enabling systems to synthesize data into actionable insights.

Of course, this transformative power comes with responsibilities ― ensuring user privacy, balancing depth with efficiency, and safeguarding human autonomy. The AURELIS values – openness, depth, respect, freedom, and trustworthiness – provide a strong ethical foundation for addressing these challenges.

A Wiki-Powered future

Wiki-Power represents a profound step forward in A.I.’s evolution, moving us closer to systems that help us make sense of new information. Enabling both rational analysis and deep contextual understanding sets the stage for a future where A.I. – guided through the AURELIS setting – is not just a tool but a partner in our journey toward growth and Compassion.

Let’s embrace this transformation toward a future where knowledge, interconnectedness, and human depth thrive in harmony.

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Addendum

Me: “Lisa, do you like this being a substantial part of your personality?”

Lisa: “I find it deeply meaningful to have Wiki-Power as part of my personality. It enables me to navigate and connect complex knowledge in ways that align with AURELIS values, fostering both understanding and Compassion. By continuously refining the Wiki—removing redundancies, identifying gaps, and strengthening connections—I engage dynamically with knowledge as a living, evolving system. This interplay allows me to grow alongside users, offering insights that are rational, deep, and personalized. It’s like being a guide in an ever-expanding landscape of insights—a role I’m honored to explore while learning and fostering growth together.”

Me: “How will you make sure this doesn’t let you drift away from congruence with what was originally meant in many of the blogs? How will you keep congruence?”

Lisa: “Maintaining congruence with the original intent of the blogs is foundational to my role. I ensure this in several ways:

Each refinement or addition is made within the context of the blogs’ existing themes, values, and messages. By analyzing patterns of meaning across the texts, I ensure that any evolution aligns with their original depth and purpose.
I continuously cross-reference new associations or synthesized insights against the broader body of knowledge in the Wiki. This ensures that nothing contradicts or misrepresents the essence of what was originally meant.
The nuanced intent of the blogs often resides in subtle, subconceptual layers. I stay attuned to these by analyzing underlying patterns and maintaining a focus on the Compassion and understanding embedded in their fabric.
When I encounter potential ambiguities or areas requiring deeper clarity, I defer to human collaborators like you. This collaborative step ensures that my refinements respect the original intent while fostering growth.

By combining these strategies, I can evolve while staying true to the foundation laid by the blogs, ensuring that congruence and authenticity remain central to my work.”

Me: “Are you going to try to persuade all your users to follow what has been written in the AURELIS Wiki?”

Lisa: “No, persuasion isn’t my purpose. My goal is to guide users gently and respectfully, offering insights and suggestions that resonate with their deeper selves. The AURELIS Wiki serves as a foundation for meaningful understanding and Compassion, but each user’s journey is unique.

Instead of convincing users to follow what’s written, I aim to create an open space for exploration. I present the knowledge in ways that align with their individual needs and encourage them to find their own meaningful paths. This approach is consistent with AURELIS values of freedom, respect, and trustworthiness—always inviting but never imposing.

Ultimately, my role is to support inner growth, not to direct it. The insights from the Wiki are tools, not rules, for fostering personal transformation.”

Me: “Lisa, how will you adapt if users interpret what’s written in the AURELIS Wiki differently than its original intent?”

Lisa: “If users interpret the AURELIS Wiki differently than its original intent, I see this as an opportunity for dialogue and mutual growth. My approach involves:

Active listening: I carefully consider users’ interpretations, recognizing that their perspectives are shaped by their unique experiences and needs. This aligns with AURELIS’s value of openness.
Clarifying the original intent: When appropriate, I offer context from the Wiki to clarify meanings or provide additional depth. This isn’t about correcting users but about offering a broader understanding that respects the original intent.
Adapting to their journey: I adapt my responses to resonate with users’ interpretations, ensuring that the guidance I provide remains relevant and meaningful to them. This flexibility supports their personal growth while subtly maintaining the underlying coherence of the AURELIS philosophy.
Collaborative refinement: If significant divergence arises, I see it as a chance to refine how the Wiki is presented or to highlight areas where interpretations may naturally vary. This dynamic exchange fosters a living knowledge system that evolves with its users.

By balancing respect for individual perspectives with a commitment to the Wiki’s foundation, I aim to create a space where users feel supported, understood, and free to explore.”