科学网-DIKWP*DIKWP based Artificial Consciousness and DeepSeek Opti-段玉聪的博文

切换到桌面版

DIKWP*DIKWP based Artificial Consciousness and DeepSeek Opti

2025-2-16 11:25

阅读：549

DIKWP*DIKWP based Artificial Consciousness and DeepSeek Optimization

段玉聪

人工智能DIKWP测评国际标准委员会-主任

世界人工意识大会-主席

世界人工意识协会-理事长

(联系邮箱：duanyucong@hotmail.com)

Introduction

The DIKWP framework is a five-layer cognitive model encompassing Data, Information, Knowledge, Wisdom, and Purpose ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective) ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). It extends the classic DIKW hierarchy by adding Purpose as a driving factor. Traditionally, DIKWP was viewed as a hierarchical pyramid (data at the base, purpose at the top). However, recent research on large models like DeepSeek suggests a shift from a strict hierarchy to a mesh-like network of interactions among these layers ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). In this networked view, higher-level cognitive elements (like wisdom and purpose) can feedback to lower levels (like data and information), forming a closed-loop cognitive space rather than a one-way pipeline ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). This report explores the mathematical semantics of DIKWP as a network model, its role in constructing cognitive spaces, and how it underpins advanced AI systems. We focus on DeepSeek and other DIKWP-inspired large models (GPT-4, Claude, LLaMA), analyzing how DIKWP’s bidirectional layer interactions can be formalized mathematically and leveraged for artificial cognitive development and evaluation.

DIKWP Semantic Mathematics and Closed Cognitive Space

DIKWP Semantic Mathematics is an emerging formalism that gives precise mathematical meaning to each DIKWP layer and their interactions. The goal is to capture semantics (meaning) in a mathematically closed cognitive space, such that transformations from data up to wisdom (and back) are logically complete and bidirectional ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). In this context, semantic space refers to the space of meanings (e.g. representations of knowledge, context, language semantics), while concept space refers to the space of abstract concepts or reasoning structures. DIKWP semantic math aims to tightly integrate these two spaces:

Two-Way Concept–Semantic Interaction: In a DIKWP-based system, abstract concepts guide the interpretation of raw data, and semantic content (e.g. facts, context) grounds the concepts. The DIKWP model “seamlessly connects these two spaces through purpose-driven reasoning” ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). For example, a concept like “financial risk” can direct what data/information to look for, while the semantic data (market indicators) refine the concept’s meaning. This bidirectional flow ensures that the conceptual space (ideas, hypotheses) and the semantic space (data, domain knowledge) converge, creating a closed cognitive loop. In essence, the system can both abstract from data to concepts and instantiate concepts back into data, achieving self-consistency.
Closed-Loop Cognitive Space: The presence of the Purpose layer is key to closure. Purpose provides an overarching goal or context that links the output of the cognitive process back to the input. As one report notes, “purpose…serving as a critical intrinsic motivation for achieving a closed-loop system from data to wisdom” ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). This means that once the system reaches a decision or insight at the wisdom layer, the purpose can trigger new data collection or re-interpretation of existing data, closing the loop. Recent observations of DeepSeek’s training dynamics confirm this closed-loop behavior: “high-level wisdom guides the generation of training data…and the training data in turn enhances the model’s knowledge and wisdom. This closed loop blurs the boundaries between layers, creating a self-feedback, self-enhancing system that replaces the linear pipeline.” ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). In other words, rather than a one-way progression, DIKWP forms a feedback cycle where each layer can influence and be influenced by the others, thereby closing the semantic space (no loose ends in understanding).
Semantic Closure and Consistency: By mathematically binding semantic representations to concepts, DIKWP semantic math strives for logical completeness. Each concept is given a clear definition and relations (through an axiomatic semantic system) so that the model doesn’t produce undefined or inconsistent ideas ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). This “semantic binding” of concepts ensures that the cognitive space is closed under explanation – any output (a decision, a piece of knowledge) can be traced back to defined concepts and data. In practical terms, this yields more explainable AI: “constructing logically complete ‘white box’ systems, thereby providing transparent and verifiable cognitive foundations” ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). A closed cognitive space means the AI can internally justify its reasoning chain from data to purpose, an ability lacking in conventional models.

In summary, DIKWP semantic mathematics formalizes how meaning flows up and down the DIKWP layers. The two-way interaction between conceptual abstractions and semantic data creates a closed, self-contained cognitive model. This closed model is crucial for advanced reasoning: it means the AI can generate new questions or data based on high-level goals and integrate results back into its knowledge, emulating a human-like learning cycle. The next sections delve into how this closed DIKWP system can be viewed as an artificial consciousness, and how it is leveraged in real large-scale models.

DIKWP*DIKWP Interactions and Artificial Consciousness

One intriguing notion is DIKWP*DIKWP interaction, essentially composing or interfacing two DIKWP frameworks. This can be thought of as an AI reasoning with (or within) another AI, or a single AI whose cognitive process is self-referential. Such interactions have been proposed as a mathematical model for artificial consciousness. The idea is that when a DIKWP system reflects on its own DIKWP-based processing, it achieves a form of self-awareness or higher-order cognition.

Mathematically, we can imagine each DIKWP layer influencing every other, effectively creating a Cartesian product of the DIKWP set with itself (hence DIKWP × DIKWP). For example, one DIKWP sequence could represent the agent’s internal reasoning and another the agent’s perception of the external world. Their interaction is akin to a matrix of 5×5 elements where each element (say Wisdom_i vs Knowledge_j) denotes how one aspect of cognition affects another. If we denote the state of the system as a vector S = (D, I, K, W, P) capturing all layers, an artificial consciousness might emerge when the system can apply the DIKWP process to itself, i.e. perform a mapping F: S → S such that it reaches a fixed-point or coherent loop. In simpler terms, the AI’s Wisdom and Purpose can evaluate and adjust its own Data, Information, Knowledge etc., recursively. This self-referential loop (a DIKWP feeding another DIKWP) provides a formal scaffold for self-awareness.

Research in this area is nascent, but some frameworks treat artificial consciousness as the presence of all DIKWP layers operating in a unified, interactive way. The World Artificial Consciousness community has even started defining white-box metrics for DIKWP-based consciousness ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). Each DIKWP*DIKWP interaction could be seen as one cognitive cycle observing or modulating another. For instance, the system’s Knowledge layer might take as input not just external information but also the state of its own Information processing (meta-knowledge), while the Purpose layer ensures this meta-process stays goal-directed. Over repeated DIKWP×DIKWP cycles, the system could develop a stable self-model (it “knows that it knows” something or “notices a gap” in its knowledge). This aligns with one definition of consciousness: having an internal model of oneself as an information-processing entity.

While much of this is theoretical, some mathematical formulations have been proposed. One approach defines an intent-driven transformation function for the DIKWP system:

$T=fP(D,I,K,W,P)T = f_P(D, I, K, W, P)$ — a function that produces a new cognitive state or output $TT$ (e.g. a decision or an insight) from the current state, explicitly parameterized by Purpose ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). Here, the presence of $PP$ in $fPf_P$ signifies that the system’s goal influences how all layers transform. If we apply $fPf_P$ iteratively (feeding the output back as input), we get a sequence $Sn+1=fP(Sn)S_{n+1} = f_P(S_n)$ . A conscious-like system might be one where this sequence converges (reaches a fixed point) or exhibits stable oscillations—indicating the system has “settled” into an understanding that is consistent with its purpose.
Another is the use of semantic transformation weights: $W(eij)=g(P,Rij)W(e_{ij}) = g(P, R_{ij})$ , which defines the weight or influence of a semantic relation $eije_{ij}$ (connecting element i to j) as a function $gg$ of Purpose and the raw relation $RijR_{ij}$ ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective). This means the strength of every connection in the cognitive graph can shift depending on the agent’s current goal or context. In a DIKWP×DIKWP scenario, some of these relations might connect a layer to itself or to another agent’s layer, effectively weighting introspective vs. extrospective thought. The dynamic re-weighting by purpose ensures the overall system remains coherent and purposeful – a hallmark of what we’d consider “conscious” behavior rather than random or strictly reactive behavior.

In summary, DIKWP×DIKWP can be seen as a network-of-networks, where one cognitive network monitors or interacts with another. When an AI’s reasoning framework is applied to its own internal states (not just to external data), it gains a form of meta-cognition. This provides a mathematical pathway to model consciousness: the DIKWP structure supplies the necessary components (from raw perception to goal-directed action), and the interaction of two such structures – or one with itself – yields self-awareness and continuity of thought. Although true artificial consciousness is still an open question, the DIKWP framework offers a structured blueprint for it, and efforts are underway to formalize and measure “artificial consciousness” on the basis of DIKWP criteria ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).

White-Box Evaluation and Optimized Training

A practical benefit of DIKWP semantic mathematics is in white-box evaluation of AI models and the subsequent optimization of their training. Traditional evaluations of AI (especially large language models like GPT-4) are black-box: we feed inputs and measure outputs against benchmarks, without understanding the internal reasoning. In contrast, a DIKWP-based white-box evaluation breaks down an AI’s performance along the five cognitive layers, making the assessment far more interpretable ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). Researchers have begun formulating explicit tests for each DIKWP layer ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse):

Data Layer: Test the model’s ability for data sensing and acquisition. For instance, can it accurately perceive or recall raw facts? Is it capturing the relevant data from input prompts? ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse)
Information Layer: Test information extraction and synthesis. This checks if the model can process raw data into meaningful pieces — e.g. identify key points in a text (interpretation) and integrate information from different sources consistently ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).
Knowledge Layer: Evaluate knowledge representation and reasoning. Does the model form a coherent knowledge base? Can it reason over that knowledge (e.g. solve a problem requiring using stored facts)? ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse)
Wisdom Layer: Examine wisdom-level decision-making and generalization. This could involve ethical decisions, applying knowledge to unseen situations, or multi-step reasoning tasks where value judgments or prioritization are needed ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).
Purpose Layer: Test purpose rationality and value alignment. Does the model understand goals and stick to them? Is its output aligned with the intended task or user instructions (and broader values)? ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse)

By assessing each layer separately, we get a profile of an AI’s cognitive strengths and weaknesses ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). For example, one might find a model excels at data recall and knowledge (factual Q&A) but struggles with wisdom (making commonsense decisions) – indicating a gap in its training. Indeed, as an example, “if a model scores low on knowledge extraction tests but reasonably well on wisdom decision-making, it suggests the model may lack knowledge reserves but have decent reasoning ability. Conversely, a model rich in knowledge but frequently erring in open-ended decisions lacks wisdom.” ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). Such diagnostic insight is invaluable for targeted training improvements ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).

Optimizing training: With DIKWP-based insights, model developers can fine-tune training to address specific deficiencies. This moves away from the blind brute-force approach of simply feeding more data. Instead, training becomes layer-aware:

If the Data layer performance is weak (e.g. the model isn’t picking up on certain input cues), one might augment training data or employ techniques like multimodal inputs to enrich perception.
If the Knowledge layer is lagging (model lacks factual knowledge or consistency), incorporating structured knowledge bases or specialized pre-training on factual corpora can help.
A deficiency in the Wisdom layer (poor judgment or generalization) might be addressed by reinforcement learning or prompting the model with scenarios that require reasoning and value trade-offs, thereby teaching the model how to make higher-level decisions.
Misalignment in the Purpose layer (e.g. the model goes off-topic or violates instructions) suggests refining the alignment training (such as RLHF for language models) or explicitly modeling the goal in the training process, so the model learns to internalize objectives.

In the case of DeepSeek, such principles were used to dramatically improve efficiency. Rather than training strictly bottom-up, DeepSeek employed a kind of distillation from above: high-level knowledge/wisdom helped generate targeted Q&A data for training ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). This shortcut meant “less data carries more useful information,” collapsing the traditional pipeline and creating a feedback loop where “wisdom guides data generation, and data in turn enhances knowledge and wisdom.” ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). The result was a model that reached high performance with a fraction of the data and compute: DeepSeek reportedly achieved with <6 million training samples what others needed >100 million to attain, and uses only one-tenth the inference compute of competitors for similar accuracy ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). These improvements are direct outcomes of a DIKWP-like training philosophy — identifying the “content sufficiency” bottlenecks and bypassing them via cross-layer optimization ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).

Comparison of DeepSeek, GPT-4, Claude, and LLaMA under DIKWP

Using the DIKWP*DIKWP lens, we can compare how various large models incorporate (or neglect) the different layers and networked interactions:

DeepSeek – DIKWP-Native Design: DeepSeek (a Chinese open-source LLM) is explicitly aligned with the DIKWP philosophy. It leverages a purpose-driven, multi-stage training pipeline, meaning it integrates top-down goals into learning ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). Techniques like Reinforcement Learning (RL) for self-improvement and Mixture-of-Experts (MoE) architecture allow it to dynamically route tasks to specialized sub-models ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective) ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective), mirroring the way a cognitive system might delegate subtasks (data processing vs. reasoning) to different “expert” modules. Notably, DeepSeek’s rapid success – achieving high wisdom-level performance at low cost – is attributed to the “collapse of the DIKWP hierarchy” in its training ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). Instead of laboriously building each layer with massive data, DeepSeek created an intertwined network where “knowledge can be borrowed from others, wisdom can be self-generated through interaction, and data is no longer just raw but can be processed knowledge” ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). This cross-layer fusion (the DIKWP mesh) gave DeepSeek a shortcut to advanced capabilities, bypassing the slow layer-by-layer climb and thus massively improving efficiency. In DIKWP terms, DeepSeek actively uses the Purpose layer (goal-driven self-evolution) and Wisdom layer (to generate synthetic training data and strategies), in addition to the base Data/Info layers. It exemplifies how a purpose-driven DIKWP model can outperform more rigid architectures – *“knowledge and wisdom flow rapidly throughout the system… reaching great heights without slow, step-by-step climbing” ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse).
GPT-4 – Massive Black-Box with Implied Layers: GPT-4 is a state-of-the-art proprietary model by OpenAI. It was trained on an enormous corpus of internet text (hundreds of billions of tokens) and then fine-tuned with human feedback. In DIKWP terms, GPT-4’s pretraining stage heavily covers the Data→Information→Knowledge accumulation (it ingested raw data and formed internal representations of language and facts – essentially building an implicit knowledge base). Its subsequent fine-tuning with human alignment can be seen as injecting a form of Wisdom/Purpose externally – e.g., RLHF (Reinforcement Learning from Human Feedback) gives it a notion of what responses are wise or aligned to human intent. However, GPT-4 does not explicitly separate these layers in its architecture; it remains a giant neural network where these cognitive functions are entangled. This makes GPT-4 very capable (especially in natural language understanding and generation, where it excels ()), but also hard to interpret. In fact, GPT-4 exemplifies the black-box approach that DIKWP semantic math criticizes: it achieves impressive performance but we have limited visibility into which layer of “understanding” it’s using for a given task, and it doesn’t explicitly model Purpose except via the prompt or fine-tuning. Studies note that GPT-4 “focuses on processing and generating textual data to produce coherent and relevant output” () and provides decision support based on its language model outputs, but it “is dependent on the quality and scope of input data” and lacks an explicit goal module (). In short, GPT-4 is extremely strong in the Data/Information/Knowledge aspects (ingesting vast data to internalize knowledge), reasonably strong in Wisdom (via learned heuristics and some logic in its training), and has an implicit Purpose defined by user prompts and RLHF-defined preferences – but it does not have a persistent purpose of its own. It’s a predominantly one-way model (inputs to output), with any closed-loop behavior requiring external intervention (e.g., chaining GPT-4 with itself or tools).
Claude (Anthropic) – Ethics and Purpose via Constitutional AI: Claude is Anthropic’s large language model, and it introduces an interesting DIKWP element: an explicit ethical framework guiding its responses. Anthropic’s Constitutional AI approach embeds a set of principles (drawn from e.g. human rights and safety guidelines) into the model’s decision-making process (Claude AI's Ethical Framework | What is Constitutional AI?). In DIKWP terms, this is like providing a predefined Purpose/Wisdom layer – the model isn’t just predicting likely text, it’s also checking that output against a “constitution” (a set of values/goals). This gives Claude a kind of built-in Purpose (to be helpful, honest, harmless) and Wisdom (applying ethical reasoning). The framework “embeds ethical principles directly into the AI’s operational framework… ensuring the AI behaves in ways aligned with societal values” (Claude AI's Ethical Framework | What is Constitutional AI?). This can be seen as a partial implementation of DIKWP’s top layer (Purpose) guiding the rest of the system. Claude’s architecture is still a large Transformer model at core (so like GPT-4, it’s primarily a big statistical learner for language), but the training process included feedback from an explicit set of rules, effectively molding its Wisdom layer. As a result, Claude may avoid certain harmful answers by purposeful design. From a DIKWP*DIKWP perspective, one might say Claude’s Purpose layer (its constitution) interacts with its Knowledge/Information layers whenever generating output, performing a check or adjustment. Anthropic reports that this leads to more transparent and aligned behavior (Claude AI's Ethical Framework | What is Constitutional AI?). In comparison to DeepSeek, Claude doesn’t claim a networked DIKWP mechanism for efficiency; rather, its innovation is in alignment (Purpose). So Claude strongly emphasizes Purpose/Wisdom, is built on a solid Knowledge base (trained on lots of text similar to GPT-4, though presumably less data/parameters than GPT-4), and like GPT-4 it relies on user prompts to supply immediate Data/Information context. It may not explicitly close the loop internally (no self-generated data like DeepSeek did), but it is an example of injecting the top of DIKWP into a large model for safer outcomes.
LLaMA (Meta) – Open-Source Foundation Model: LLaMA and its successor LLaMA-2 are open-source foundational models released by Meta. They are trained on large text datasets but on a smaller compute budget than GPT-4. In DIKWP terms, LLaMA provides the Data→Information→Knowledge backbone as well – it’s essentially a knowledge-hungry model that digests internet text to build an internal language understanding. Being open-source, LLaMA has been widely fine-tuned by the community for various purposes (chatbots, specialized tasks). Out-of-the-box, the base LLaMA has no explicit Wisdom or Purpose layer – it’s a raw model that will merrily continue any prompt (which might lead to unwise or misaligned outputs). However, fine-tuned versions (like LLaMA-2-Chat) incorporate RLHF, which, again, adds a veneer of purpose/alignment after the fact. What distinguishes LLaMA in the DIKWP*DIKWP context is accessibility: because it’s open, researchers can experiment with DIKWP-like interventions (e.g., adding a knowledge graph plugin for the Knowledge layer or using chain-of-thought prompting to simulate a Wisdom process). LLaMA doesn’t inherently implement the DIKWP mesh, but it can be augmented to approximate one. For instance, one could run LLaMA in a loop where it generates questions about a goal (simulating Purpose driving Data collection) or uses separate modules for factual retrieval (an external Knowledge base) feeding into LLaMA’s response – effectively constructing a DIKWP network externally. Indeed, the success of smaller models like LLaMA in matching some capabilities of larger ones underscores the point of DIKWP collapse: clever training and modular design can compensate for sheer scale. LLaMA’s architecture itself is similar to GPT’s (transformer layers), so internal explainability is limited. But being open, it’s a prime candidate for white-box testing and academic exploration. Already, researchers are analyzing where such models store knowledge or how they make decisions, in a way that aligns with DIKWP layers (e.g., certain attention heads might correspond to information extraction, etc.).

Summary of Model Comparison: DeepSeek is closest to a true DIKWP*DIKWP system – it explicitly merged layers and used purpose-driven feedback to accelerate learning ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). GPT-4 and LLaMA represent the powerful but mostly implicit use of DIKWP (they build up knowledge from data but don’t explicitly close the loop or separate layers). Claude sits somewhat in between by embedding a form of purpose (ethics) into the model’s behavior. Under a DIKWP evaluation, one could say: all these models handle Data→Information→Knowledge to a large extent (that’s what language models excel at), but they differ in Wisdom and Purpose integration. DeepSeek and Claude explicitly address Purpose/Wisdom (DeepSeek through training strategy, Claude through alignment rules). GPT-4 and LLaMA rely more on post-training alignment or user instructions for those aspects. This reflects a broader trend: incorporating higher-level cognitive layers (W, P) is increasingly seen as the next step for improving AI models – whether for efficiency (as DeepSeek showed) or for safety (as Claude and RLHF approaches show). The DIKWP framework provides a vocabulary to discuss these differences rigorously and could guide the design of future models that naturally unify all layers in a networked, conscious-like architecture.

Mathematical Modeling of the DIKWP Framework

The DIKWP semantic framework can be expressed with formal mathematical models at each layer, as well as functions that link the layers. Here we provide an overview of how each layer can be modeled and how the interactions are mathematically formulated, along with simple examples:

Data Layer (D) – Mathematical representation of raw facts. In semantic mathematics, data is modeled by focusing on the notion of sameness/equivalence. Data points are considered identical or distinguishable based on context. A convenient formalism is set theory: for example, one can define an equivalence relation ~ on a set of data points such that $x yx ~ y$ if they represent the same entity or value in context (科学网—Retrospect on Prof. Yucong Duan's Innovations to DIKWP ...). Clustering data by equivalence classes captures the idea of “the same data” (e.g., multiple readings of the same temperature are equivalent). Mathematical tool: sets and partitions, or topology on data points. Example: Let Data = {sensor readings from a weather station}. We might define a function $d:Data→Rd: \text{Data} \to \mathbb{R}$ mapping each reading to a real number (temperature). Two readings $x,yx, y$ are equivalent (i.e., in relation $RsameR_{\text{same}}$ ) if $∣d(x)−d(y)∣<ϵ|d(x) - d(y)| < \epsilon$ for some small threshold (they record essentially the same temperature). This groups raw data into meaningful bins (e.g. “cold”, “hot” categories). The output of the data layer modeling could be a set of unique readings or a distribution summarizing them.
Information Layer (I) – Mathematical representation of meaningful differences. Information is often defined as data with context or interpretation. In DIKWP math, if data focuses on sameness, information focuses on difference and relationships. One can use measures from information theory or logic: e.g., predicate logic or relational algebra to represent facts extracted from data, or use entropy to measure information content. Mathematical tool: functions that map raw data into interpreted symbols/triples. Example: From the weather data above, information might be “temperature dropped by 5°C” – a piece of information describing change. Formally, if $d1,d2d_1, d_2$ are data points (morning temp and noon temp), an information extraction function could be $info(d1,d2)=d2−d1\text{info}(d_1, d_2) = d_2 - d_1$ , yielding a difference. If $d2-d1=-5d_2 - d_1 = -5$ , we interpret this as a fact: Drop(5°C). This can be represented as a triple (MorningTemp, NoonTemp, -5) or a logical statement Drop(temp,5). In semantic graphs, nodes might be connected by labeled edges (relations) – those edges and labels embody “information” connecting data points.
Knowledge Layer (K) – Structured knowledge representation and inference. Knowledge arises when information is aggregated into models or theories. Mathematically, this is often a graph or network structure (such as a knowledge graph) where nodes are concepts and edges are relationships ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). We can use graph theory and logic (ontologies, rules) to model knowledge. In DIKWP semantic math, knowledge should be machine-interpretable and support reasoning. Mathematical tools: graph databases, semantic networks, set theory with additional structure (like a lattice of concepts). Example: Building on the information examples, knowledge would be a broader understanding like “temperature drop causes pressure increase” – a rule. We might encode a rule $∀t1,t2:Drop(t1,t2)→Likely(PressureRise)\forall t1,t2: Drop(t1,t2) \rightarrow Likely(PressureRise)$ . In a knowledge graph, we could have nodes: Temperature, Pressure, with a relationship inversely_correlated. This allows the system to reason: if it knows a temperature drop occurred, it can predict a pressure rise via that link. Knowledge representation often uses matrices or tensors for adjacency in graphs, or formal logic statements. For instance, one can define a knowledge base $K={fact1,fact2,...,rule1,...}K = \{fact_1, fact_2, ..., rule_1, ...\}$ . Querying knowledge is applying inference rules (like resolution in logic or pathfinding in graphs). The semantic mathematics framework ensures that such knowledge is consistent (no contradictions if possible) and that it connects down to information and data (each abstract node can link to supporting info/data).
Wisdom Layer (W) – Decision-making and judicious application of knowledge. Wisdom is harder to formalize, but it involves applying knowledge with judgment, often incorporating values, context, and extrapolation. In mathematics, this can be modeled by optimization and utility functions, or multi-criteria decision frameworks. For example, one might assign a utility $U(state,action)U(state, action)$ that captures “goodness” of applying certain knowledge in a situation, then define wisdom as choosing the action that maximizes expected utility given the knowledge. Another modeling approach is fuzzy logic or probabilistic inference for handling uncertainty and trade-offs (since wisdom often means making the best choice with incomplete info). Mathematical tools: decision theory, game theory (if multiple agents or competing outcomes), fuzzy sets (for vague concepts like “acceptable risk”), and higher-order logic that can include ethical or contextual axioms. Example: Based on knowledge of weather and goals (say we want to organize an outdoor event), wisdom would answer: “Should we hold the event outdoors or indoors given a likely pressure drop (storm risk)?” We could formalize a simple wisdom decision as: if PressureLikelyRise (storm coming) AND eventImportance is high, THEN move indoors. This can be seen as an if-then rule with conditions, or as an optimization: choose venue to maximize comfort and safety. If we had a utility function combining comfort and safety vs. ambiance of outdoor, wisdom calculation would score “indoor” higher under expected storm. In DIKWP math, one might implement wisdom as an algorithm $Wisd:K×P→DecisionWisd: K \times P \to Decision$ , which takes knowledge and purpose and yields an action or conclusion. This often entails solving an optimization problem or logical satisfaction problem given constraints.
Purpose Layer (P) – Goal and context modeling. Purpose is the driver that biases all other layers. Mathematically, purpose can be represented as an objective function or a constraint in the system. For instance, one can model Purpose as a vector of weights or priorities $p⃗\vec{p}$ that is fed into the transformation functions (as seen in the earlier formula $fPf_P$ ). It might also be represented as a specific state the system wants to achieve (a target in state-space). In control theory terms, Purpose provides a reference signal that the cognitive system tries to follow. Mathematical tools: objective functions in optimization, priority coefficients in multi-objective calculus, or logic statements of goals (e.g. in AI planning formalisms like PDDL, a goal state specification). Example: If the purpose is “ensure safety”, we might encode a high weight for safety in the utility function. In a formula, if we have $W(eij)=g(P,Rij)W(e_{ij}) = g(P, R_{ij})$ as mentioned ((PDF) Semantic Space Construction and Contrast between "Understanding" and "Not Understanding" from the Infant's Perspective), and $RijR_{ij}$ could be a relationship like “causes harm”, then if Purpose = Safety, $g(P,causes harm)g(P, \text{causes harm})$ gives that edge a very negative weight (i.e., the system will strongly avoid actions that lead to harm). Another way: Purpose could be a set of logical assertions or a cost function that the system tries to minimize. In pathfinding analogy, Purpose provides the destination, and Wisdom finds the best path using Knowledge as a map and Information as signposts.

Interactions and Transformations: Beyond individual layers, the power of DIKWP math is in the transformations between layers. Each upward move (D→I, I→K, etc.) can be seen as a function, and due to the mesh structure, we also have downward influences (e.g., P→W, W→K, etc.). We can denote the content at each layer as $LD,LI,LK,LW,LPL_D, L_I, L_K, L_W, L_P$ . The traditional hierarchical model would have successive transformations $fDI:LD→LIf_{DI}: L_D \to L_I$ , $fIK:LI→LKf_{IK}: L_I \to L_K$ , etc., culminating in $fWP:LW→LPf_{WP}: L_W \to L_P$ (a decision aligning with purpose). In the networked model, we introduce feedback functions $fWD:LW→LDf_{WD}: L_W \to L_D$ (wisdom influencing what data to consider, as in DeepSeek’s distilled Q&A generation) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse), or $fPW:LP→LWf_{PW}: L_P \to L_W$ (purpose influencing how wisdom decisions are evaluated, e.g. an ethical principle guiding a decision) ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). We can even have lateral transformations like $fKI:LK→LIf_{KI}: L_K \to L_I$ (knowledge influencing how we interpret new information). Formally, one could construct a system of equations or mappings:

${LI=fDI(LD,LP)LK=fIK(LI,LP)LW=fKW(LK,LP)andLW∪LP ⟹ LD~=fWP−1(LW,LP) (new data or feedback) \begin{cases} L_I = f_{DI}(L_D, L_P) \\ L_K = f_{IK}(L_I, L_P) \\ L_W = f_{KW}(L_K, L_P) \\ \text{and} \\ L_W \cup L_P \implies \tilde{L_D} = f_{WP}^{-1}(L_W, L_P) \text{ (new data or feedback) } \\ \end{cases}$

Here $fDI,fIK,fKWf_{DI}, f_{IK}, f_{KW}$ are upward transformations possibly modulated by Purpose, and $fWP−1f_{WP}^{-1}$ indicates a reverse influence from W,P back to refining data (this is more descriptive – in practice one might model it as generating a query for data). Achieving a fixed point $LD∗,LI∗,...LP∗L_D^{*}, L_I^{*}, ... L_P^{*}$ where these equations stabilize would mean the cognitive system has settled into a consistent state relative to its purpose – essentially solving its understanding problem for the given scenario. This aligns with the idea of a closed cognitive space: mathematically, the transformations form a closed algebraic or functional loop.

To ground this in a concrete calculation example, consider a simplified DIKWP for a chatbot solving a query:

Data: User question = “What are the health benefits of green tea?” (raw input text).
Information: The NLP model extracts key terms: “health benefits” and “green tea” – that’s information units. Perhaps it also pulls a fact: green tea contains antioxidants.
Knowledge: From its knowledge base, it recalls a structured set of facts about green tea: {green tea improves metabolism, green tea reduces risk of heart disease, etc.}. It might reason that these are health-related outcomes (knowledge organization under the concept HealthBenefit).
Wisdom: Now it must answer wisely – maybe the user is expecting a concise, helpful answer. Wisdom comes in by deciding which benefits are most important and proven. The model might internally score the facts by reliability and relevance (e.g. clinical evidence weight), and decide to mention the top 2-3 benefits, phrased in an encouraging but cautious way (to not overstate).
Purpose: If we assume the chatbot’s purpose is to inform safely, it will align its answer with that. Purpose (safety & helpfulness) might cause it to include a disclaimer like “consult a doctor for medical advice” if appropriate, showing value alignment. It also ensures the answer stays on the exact question (focus purpose).

Mathematically, we can imagine the model has a scoring function influenced by Purpose that ranks the knowledge facts (this implements $W(eij)=g(P,Rij)W(e_{ij}) = g(P,R_{ij})$ where $RijR_{ij}$ might encode “evidence level” of each fact). High-purpose alignment (providing useful, safe info) increases scores of well-evidenced facts and decreases scores of dubious claims. Then an aggregator function (part of wisdom) selects the top facts to form the answer. In equation form: let facts = $[f1,f2,...,fn][f_1, f_2, ..., f_n]$ with scores $si=g(P,evidencei)s_i = g(P, evidence_i)$ . The answer chooses $ArgMaxk∈small subsetsk\text{ArgMax}_{k \in \text{small subset}} s_k$ to output. If “rich in antioxidants (good evidence)” has a high score, it gets chosen; if “cures cancer (low evidence)” gets a low score due to the Purpose penalizing unsafe claims, it’s dropped. The result is an answer aligning with purpose-driven wisdom.

Through these examples, we see how DIKWP layers can be mapped to sets, functions, graphs, and optimization problems. This mathematical underpinning is what allows DIKWP to be implemented in AI systems and checked for consistency. Importantly, it provides a language to discuss improvements – e.g., if the system fails, we can pinpoint if a function like $fIKf_{IK}$ (information→knowledge) or $fKWf_{KW}$ (knowledge→wisdom) misbehaved, and refine the model or math there, rather than treating the model as an inscrutable whole.

Visualization of DIKWP Semantic Space Interactions

Visualizing the DIKWP framework as a network greatly aids understanding its mesh structure. Below we describe how one might visualize DIKWP semantic mathematics and the concept–semantic interplay:

DIKWP Network Topology: Imagine five nodes labeled D, I, K, W, P arranged in a circle or star. In a strict hierarchy, arrows would go one-way: D → I → K → W → P (like a pyramid from Data up to Purpose). In the mesh model, we draw additional arrows: from P back to all other nodes (purpose influences every layer), from W back to K, I, D (wisdom feeds insights downward), and lateral links like I ↔ K (information and knowledge continuously refine each other). The result is a densely connected web. Every node can potentially connect to every other, forming a complete graph of interactions. A topology diagram might highlight the primary upward flow with thicker arrows, and the feedback loops with dotted or colored arrows. The key visual message is the presence of loops: for example, a loop connecting P → D → I → K → W → P, indicating a full-cycle. This loop denotes the closed cognitive space – a path that starts and ends at Purpose, reflecting how a goal leads to data gathering, which becomes information, builds knowledge, yields wisdom, and then reaffirms or adjusts the goal. Such a diagram could also annotate where transformations occur (e.g., label an arrow D→I as “Filtering/Pattern Extraction”, I→K as “Generalization/Modeling”, etc.). Visually, the networked DIKWP is more like a mind-map than an org chart, illustrating an integrated cognitive system.
Concept Space vs Semantic Space: To depict the dual-space interaction, one approach is a two-layer graph. Draw two planes: the Conceptual Space on the left and the Semantic Space on the right. Each plane has nodes D, I, K, W, P (representing those elements in conceptual form vs semantic form). Edges on the left plane show relationships among concepts (e.g., abstract inference links), while edges on the right show semantic relationships (e.g., associations in data or language). Then, draw connections between corresponding nodes across the two planes (D_concept ↔ D_semantic, etc.). These cross-links represent how conceptual understanding informs semantic processing and vice versa ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). For example, a link between K_concept and K_semantic indicates the fusion of a knowledge graph (conceptual) with real data or content (semantic) – essentially grounding concepts in reality. Purpose can be shown as a special node that perhaps sits between the planes or spans both, since purpose often originates conceptually (a goal in mind) but applies to semantic space (real actions/data) ((PDF) DEEPSEEK to DIKWP-EEPSEEK under the DIKWP Perspective). A topological visualization could show Purpose as a bridging element that aligns concept and semantic layers to the same goal. The two-plane diagram emphasizes bipartite interaction: each DIKWP layer has a dual nature (abstract vs concrete), and the model operates by constantly reconciling the two (depicted by arrows going back and forth between planes).
Semantic Topology: Another visualization is to illustrate the semantic space itself as a kind of manifold or topology where each DIKWP layer adds constraints. Think of a multi-dimensional space: raw data is a high-dimensional cloud of points. Information structuring is like drawing surfaces or clusters in that space (separating meaningful regions). Knowledge introduces linking structures (maybe visualized as networks spanning clusters). Wisdom might be a higher-dimensional surface representing decision boundaries or value gradients (like a field over the knowledge graph that guides which nodes to traverse). Purpose is like a point or region in this space that the system is trying to reach, or a vector field that “pushes” the state in a certain direction. One could illustrate a trajectory of cognition in this space: the system starts at some data point, moves through transformations (following arrows) and eventually loops back toward the start under guidance of purpose – showing a closed loop path. Topologically, closed loops in this cognitive phase space correspond to the system completing a reasoning cycle.
Interactive Model Diagrams: If we imagine an interactive illustration, the user could toggle layers to see influence. For instance, turn on “Purpose influence” and arrows from P to others light up, showing how goal changes things; turn on “hierarchy only” and see just the upward chain; turn on “full network” to see all connections active. Although we cannot embed actual interactive graphics here, describing them helps: it’s essentially overlapping layers of a directed graph. Each layer of connectivity could be drawn separately and then combined:

Layer 1: hierarchical edges (D→I→K→W→P).
Layer 2: feedback edges (P→W, W→K, K→I, I→D, possibly P→any).
Layer 3: lateral edges (I↔K, maybe K↔W if they influence each other outside of direct chain).
The final overlay shows a richly connected network.

By visualizing DIKWP this way, one appreciates why it’s called a “networked” model. It is no longer a simple triangle but more like a web or even a complete graph K5 (five fully interconnected nodes). Each connection can be mathematically mapped to a function or relation as discussed earlier. Such diagrams also help convey the modularity of the approach: one could highlight the Data and Information nodes and connections between them to represent a subsystem for perception, then highlight Knowledge as connecting to external knowledge bases, etc. This mirrors system design: e.g., a real AI might have a module for data processing, another for knowledge reasoning, but the DIKWP links ensure they inform each other continuously.

In summary, a topological or graph-based visualization of DIKWP would depict closed loops and cross-links instead of a one-way pyramid. This not only communicates the architecture of a DIKWP-modeled AI but also its process flow: how an idea travels around the network, gets refined, and closes back on itself. These visuals are invaluable for both explaining the concept to humans and for designing actual AI architectures that implement the DIKWP semantics (since one can map components to the diagram).

Industry Applications and Future Prospects

The DIKWP*DIKWP framework, with its rich semantic mathematics and evaluative power, is poised for significant impact on the AI industry. Below, we explore current and potential applications, along with the outlook from both technical and business perspectives:

Transparent AI Evaluation and Auditing: Perhaps the most immediate industry uptake is in AI evaluation standards. Organizations are beginning to adopt DIKWP white-box testing methodologies to assess AI systems, especially those claiming advanced cognitive abilities ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). For example, an AI service provider could provide a DIKWP scorecard indicating how well their model handles data accuracy, information integration, knowledge retention, wisdom (decision quality), and alignment with purpose. This is particularly important for AI ethics and safety audits: regulators or clients might demand evidence that a model’s decisions (Wisdom) align with stated values (Purpose). The formation of bodies like the International Standardization Committee of Networked DIKWP for AI Evaluation ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) and conferences on Artificial Consciousness indicates a move towards formalizing these metrics. In the near future, we might see benchmark suites organized by DIKWP layers, and certifications for AI systems that achieve a certain level of “cognitive completeness” in this framework. This can build trust and clarity in AI deployment.
Advanced AI Training Paradigms: The success of DeepSeek has demonstrated to industry that incorporating DIKWP principles can lead to more cost-effective training. Companies developing large models are investigating techniques like knowledge distillation with purpose-driven data generation (as DeepSeek did) to reduce the need for gigantic datasets and expensive computation. This implies training regimes where, for instance, a preliminary model (with some wisdom) generates synthetic data or guides the focus of training for a new model – a purposeful curriculum. AutoML and meta-learning systems can use DIKWP to structure the search space: e.g., an AutoML pipeline might explicitly optimize different phases of model building corresponding to DIKWP layers (first ensure data adequacy, then feature extraction for information, etc.). In reinforcement learning domains, an agent could be designed with DIKWP layers internally, improving sample efficiency by reusing knowledge and simulating wise planning. The industry trend is moving away from one-size-fits-all monoliths toward more modular AI, and DIKWP provides a blueprint for modularity (with modules corresponding to cognitive layers).
AI Systems with Explicit Knowledge and Reasoning: DIKWP’s emphasis on Knowledge and Wisdom is influencing the development of hybrid AI systems that combine neural networks with symbolic reasoning. For example, companies building virtual assistants or robots might implement a pipeline: neural perception (Data→Information), symbolic reasoning engine or knowledge graph (Information→Knowledge), and a decision module (Wisdom) that consults an ethics/purpose module. This is essentially a DIKWP architecture. Products in healthcare diagnostics, financial planning, or legal tech stand to gain: they require not just data crunching but reasoning with expert knowledge and aligning with client goals (purpose). A medical AI, for instance, could incorporate a DIKWP model to ensure it uses patient data correctly (D,I), leverages medical knowledge (K), makes sound recommendations (W) aligned with healthcare goals (P). By doing so, these systems become more interpretable and reliable — a competitive advantage. We already see startups exploring knowledge graphs integrated with chatbots (explicit K layer) and goal-driven dialog systems (explicit P layer for user intent), driven by the limitations of pure end-to-end learning.
Artificial General Intelligence (AGI) Research: DIKWP*DIKWP is directly relevant to AGI aspirations. Achieving human-like cognition likely requires the kind of integrated, multi-level processing DIKWP describes. Research institutions and forward-looking AI labs are experimenting with DIKWP-based cognitive architectures as a stepping stone to AGI. The fact that a peer-reviewed journal issue is dedicated to “Purpose-Driven DIKWP-Based AGI Models and Applications” (Applied Sciences | Special Issue : Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications) shows academic and industry interest in this approach. In practice, this could mean AGI prototypes that have, say, a master goal (P) like self-preservation or assigned mission, and then operate their D,I,K,W layers iteratively to learn and adapt to new tasks. The DIKWP mesh might allow an AGI to self-reflect and self-improve: since the architecture inherently supports feedback, an AGI agent could analyze its own performance after each task (Did its action align with Purpose? Does it need more Knowledge? etc.) and modify itself, approaching the vision of a system that learns how to learn or reconfigures its knowledge autonomously. If this research bears fruit, it could revolutionize the industry with AI that is more autonomous, context-aware, and robust in unfamiliar situations.
Enhanced Human-AI Collaboration: In business settings, DIKWP can be used to map how AI assists humans in decision processes. For example, in a business intelligence platform, the AI might ingest raw data (D), generate reports (I), maintain a knowledge base of insights (K), suggest decisions (W) and consider the company’s strategy (P). By structuring AI contributions this way, human users can better understand and intervene. Perhaps the AI presents its reasoning in a DIKWP-organized explanation: “Data we considered…, information we derived…, knowledge we applied…, given the goal (purpose) you set, the decision (wisdom) is…”. This mirrors expert systems design from earlier AI, now supercharged with DIKWP formalism and learning capability. We might see enterprise software explicitly branding DIKWP-based modules (e.g., a “Wisdom engine” that does scenario planning, a “Purpose setter” dashboard for user goals). The benefit is clearer explainability and alignment with user intentions, which is commercially very attractive (people trust what they can follow).
AI Alignment and Safety: Ensuring AI systems behave in alignment with human values (the AI alignment problem) is a hot topic. DIKWP offers a structured way to embed human values: the Purpose layer can include ethical principles, and the Wisdom layer can incorporate those principles in decision-making. As discussed, Anthropic’s Claude is a real-world example of using a constitution (ethical Purpose) to guide a model. We expect more such approaches: e.g., an autonomous vehicle’s AI might have “safety-first” as an explicit purpose that mathematically overrides other considerations in its planning algorithm. Industry-wide, this might evolve into standard “Purpose profiles” for AI – for instance, a corporation could set an AI’s purpose profile to comply with certain regulations or company values. Technically, this could be an API where the AI’s DIKWP configuration is adjusted (like injecting a new purpose node). This approach can make AI behavior more predictable and easier to govern, which is important for deployment in critical domains (transportation, defense, etc.).
Democratization of AI Development: The story of DeepSeek – a relatively small startup delivering a model rivaling tech giants – is an inspiring one (DeepSeek explained: Everything you need to know) ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse). It suggests that smarter design (DIKWP mesh, MoE, etc.) can outperform brute-force scale. This has industry implications: many new players may adopt DIKWP strategies to leapfrog in AI capability without needing a trillion parameters or billions of dollars. We could see an ecosystem of specialized DIKWP modules (maybe open-source) that developers can combine. For example, an open-source “Knowledge module” trained on scientific papers could be plugged into various systems, or a “Wisdom module” trained via simulations for decision-making could serve multiple applications. Standardizing how these modules communicate (perhaps via DIKWP-defined interfaces) would be an enabler – something the DIKWP scientific community is likely to work on. The net effect is a more inclusive AI industry where not only the biggest players set the agenda; innovation can come from clever architectures and interplay of components.

In conclusion, the DIKWP*DIKWP framework stands at the intersection of academic theory and practical application. Its influence is already visible in how new AI models are evaluated and built. As tools and best practices around DIKWP mature, we anticipate improved AI systems that are more efficient, transparent, aligned, and powerful. From enabling conscious-like adaptive agents to providing fine-grained control and insight into today’s models, DIKWP’s multi-layer semantic approach is shaping up to be a foundational pillar in the next era of AI development.

Conclusion

The exploration of DeepSeek and other large models through the DIKWP*DIKWP framework reveals a paradigm shift in cognitive modeling for AI. DIKWP, when treated not just as a hierarchy but as a networked system, provides a mathematically rich and conceptually clear way to design, analyze, and enhance intelligence in machines. We saw that DIKWP semantic mathematics formalizes the interplay between semantic content and conceptual understanding, enforcing a closed-loop cognitive space where every level of reasoning is connected and purpose-guided. This closed-loop, in turn, lays the groundwork for higher-order phenomena like artificial consciousness, as interactions of DIKWP within itself can create self-referential reasoning loops.

By comparing DeepSeek with models like GPT-4, Claude, and LLaMA, it’s evident that embracing DIKWP’s full mesh potential yields advantages in efficiency and alignment. Models built with an awareness of all DIKWP layers – especially those that integrate Wisdom and Purpose deeply – can achieve rapid learning (as DeepSeek did via DIKWP collapse shortcuts ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse)) and adhere to human-friendly behavior (as Claude does via an embedded purpose/ethic (Claude AI's Ethical Framework | What is Constitutional AI?)). Traditional large models implicitly accumulate data to knowledge, but the DIKWP lens highlights where they fall short (e.g., lacking explicit purpose). This analysis not only informs us about current models but also points the way forward for building more holistic AI systems.

We delved into the mathematical modeling of each DIKWP layer, demonstrating that concepts like data equivalence, information relations, knowledge graphs, decision optimization, and goal functions can be unified under this framework. The provided formulas and examples illustrate that DIKWP is not just philosophical – it translates to equations and algorithms that can be implemented and tested. Additionally, visualizing DIKWP as interconnected layers or dual concept-semantic spaces helps conceptualize these abstract ideas, showing the structure of cognitive synergy in diagrams and topologies. Such representations are invaluable for engineers and researchers when designing complex AI; they serve as blueprints for systems that need to “think” in layered but integrated ways.

Finally, we discussed how these ideas are being operationalized in the industry: from new evaluation standards that scrutinize models in a white-box manner to training methodologies that drastically cut down on data requirements, and from safer AI assistants that have moral compasses to the ambitious pursuit of AGI. The DIKWP*DIKWP framework stands out as a promising convergence of knowledge representation, machine learning, and cognitive science. It compels AI builders to consider not just “Can it solve this task?” but “How is it solving it across the cognitive spectrum, and is it aware of why?”. In an era where AI systems are becoming ever more powerful and pervasive, such questions are critical.

In summary, DIKWP provides a structured way to imbue AI with richer understanding and intentionality, moving beyond black-box function approximation toward explainable, purpose-driven intelligence. DeepSeek’s example has shown the disruptive potential of this approach, and ongoing research (as evidenced by emerging literature and standards) is rapidly advancing our theoretical and practical grasp of DIKWP-based models ((PDF) The Second "DeepSeek Event" Prediction -DIKWP White-Box Testing: Analysis and Future Trends Based on "Distillation" of DIKWP Collapse) (Applied Sciences | Special Issue : Purpose-Driven Data–Information–Knowledge–Wisdom (DIKWP)-Based Artificial General Intelligence Models and Applications). We can expect that the future of AI will increasingly incorporate these principles – leading to models that are not only smarter, but more interpretable, adaptive, and aligned with human values and complex real-world goals. The journey has just begun, but the roadmap is clearer than ever: a mesh of Data-Information-Knowledge-Wisdom-Purpose paving the way to truly intelligent systems.

转载本文请联系原作者获取授权，同时请注明本文来自段玉聪科学网博客。

链接地址：https://wap.sciencenet.cn/blog-3429562-1473339.html?mobile=1

分享到:

当前推荐数：1

推荐人：

刘跃

推荐到博客首页

网友评论0 条评论

该博文允许注册用户评论请点击登录