- From: Milton Ponson <rwiciamsd@gmail.com>
- Date: Thu, 4 Dec 2025 12:18:53 -0400
- To: CHARLES WAWERU <cmukabi@aol.com>
- Cc: Paola Di Maio <paoladimaio10@gmail.com>, W3C AIKR CG <public-aikr@w3.org>
- Message-ID: <CA+L6P4x-pDgO+V43doEdu4UZm_TE7XNz+1WxjYqbY3NQpiLC7A@mail.gmail.com>
This summary gets at the heart of the problem I am dealing with in ICT4D. Solving sustainable development problems like those created by climate change, the rise of new diseases that are resistant to drugs, impacting humans, livestock and wildlife, agriculture and food security, ecosystem and biodiversity conservation, pollution and contamination requires looking at complex (adaptive) systems (of systems). In most cases spatiotemporal modeling of processes is required. Agentic AI covers a tiny part of the spectrum of AI solutions needed. It is wisdom that directs us to expand our knowledge or use specific knowledge in appropriate or novel settings. Thinking about what we know and what we don't know and be aware of what the mathematical or computational limitations are to what we CAN know is one factor that separates us from AI as humans. There are also ethical, moral and other issues at play here. Expanding our knowledge into fields where the application of such knowledge can have devastating or extinction level effects is a choice we must make as individuals or in a societal setting. This is the other factor that separates us from AI. And it hinges on experience we gain as EMBODIED conscious and MORTAL beings. To create general AI applications this Wisdom level must sit above all. There are four types of AI: Reactive Machines, Limited Memory AI, Theory of Mind AI and Self-Aware AI. There are four main areas of AI: Machine Learning, NLP, Computer Vision and Robotics. There are four forms of knowledge representation: logic based, semantic networks, frames and production rules. It should be obvious that creating AI that is open, transparent, explainable, inclusive, safe, trustworthy and ethical, administrative and legal regulations compliant, there has to be an orchestration mechanism that involves humans as supervisors with kill-switch capability for all forms of AI. This is why it is so hard to create KR for AI, and why KRR or KR&R is sometimes preferable. From my perspective wisdom guides intention which implies strategy and planning which implies domains of discourse and domains of knowledge which define required knowledge representation. Using climate change as a primary example for creating an AI digital twin which encompasses all relevant factors and complex adaptive systems involved, requires using all levels of this hierarchy from wisdom downwards. Which makes KR or KRR context and application sensitive and inextricably linked to domains of knowledge and discourse. Milton Ponson Rainbow Warriors Core Foundation CIAMSD Institute-ICT4D Program +2977459312 PO Box 1154, Oranjestad Aruba, Dutch Caribbean On Thu, Dec 4, 2025, 02:24 CHARLES WAWERU <cmukabi@aol.com> wrote: > Thanks Paola, Daniel I will check all the links provided. > > Following Paola’s advise to evaluate using different model, I engaged > Google Studio Gemin 3 pro with the same prompt. Here is the output.. > ... > > In the standard hierarchy of information science and Artificial > Intelligence, the layer that sits immediately above Knowledge > Representation is *Wisdom*.[1 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFW3LI7PXMK3Ijj1jBXOy0QEuRS1pdKkICVLfq31HD6pDMd6gnYRyj7mgalrxKtxhReOoyzp4pK859CPWdZid3vg9zTCKR3xiksfcvrAQwcMHysgKIQxQW75iin_7_-GBBr7ywSzSfUxPTlHDFIJtNMoz84aQ%3D%3D> > ] > > However, depending on whether you are looking at a theoretical model, a > software architecture, or a philosophical goal, the answer varies. Here is > a breakdown of what sits "above" knowledge in different contexts. > 1. The Information Hierarchy (DIKW Pyramid) > > The most common model used to describe this hierarchy is the *DIKW > Pyramid*.[2 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQGZfQINbcmApEU7VxpRuK-TfqVXOPQzAG3RGts9Fn5Up_4t3p-hfEHnvdoW8tcfAwDTvMXImT-nYdkWw3Sy4COHaA5EAnlnl3mMYiyeJgJWRKMq7aa61Mx_pIYaqYGV__wauyJrLuy6PsUODpu5saYnbfGvytxbcFhFba1pAqffCSQSisJoRm0%3D> > ][3 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQE2HTpsxBraDYDWpviqpY9CCk2iLMT1B73vxT6YwvZdwjbqe3Ra74uIfYH-Ul9Ug0VvMNXUHfTQdndVk6tyqGdxS6B9IbvI_8sQve-TxDPGwAogct37tP5vEgKG4ATkBDmEXZKPMcG8nxtAPoRwgi4Eg-FXBcZCdenBMA%3D%3D> > ] > > - > > *Data:* Raw numbers or symbols (e.g., "38"). > - > > *Information:* Data with context (e.g., "38 degrees Celsius"). > - > > *Knowledge:* Information with meaning, patterns, or rules (e.g., "38°C > is a fever; humans typically have a temperature of 37°C"). > - > > *Wisdom:* The application of knowledge to make sound judgments, > particularly regarding the future or ethics (e.g., "I should give this > patient medication to lower the fever, but not so much that it masks other > symptoms"). > > In this context, *Wisdom* is the "top" layer.[1 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFW3LI7PXMK3Ijj1jBXOy0QEuRS1pdKkICVLfq31HD6pDMd6gnYRyj7mgalrxKtxhReOoyzp4pK859CPWdZid3vg9zTCKR3xiksfcvrAQwcMHysgKIQxQW75iin_7_-GBBr7ywSzSfUxPTlHDFIJtNMoz84aQ%3D%3D> > ][3 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQE2HTpsxBraDYDWpviqpY9CCk2iLMT1B73vxT6YwvZdwjbqe3Ra74uIfYH-Ul9Ug0VvMNXUHfTQdndVk6tyqGdxS6B9IbvI_8sQve-TxDPGwAogct37tP5vEgKG4ATkBDmEXZKPMcG8nxtAPoRwgi4Eg-FXBcZCdenBMA%3D%3D>] > It implies ethical judgment, experience, and the ability to predict > consequences in novel situations. > 2. The Functional Architecture (How AI Works) > > In practical AI engineering, "Knowledge Representation" (KR) is a static > layer—it is just the library of facts. What sits above it is the mechanism > that uses that library. > > - > > *Reasoning / Inference:* This is the engine that sits on top of > knowledge.[4 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFs8YN-8_CR-SXGKjsiIwQGfhXVvGGjqg-BKwrGGQNxMQsgf6AVZNi0eja4ROqxTDhEGVwTmkneEQ5TY0RWSeGpeU5SnQi3-ADL_gwukQqrxdqYJEnX4vtMaWs9nBVf1C_5IgWB2zwmlsYuKFPEzAZbt2NOkoJMPu1XcWxlIHz1mV9TyJ5U5GbI3WWh_wXauCZR2XzWu08PrkXfpRyNttrw9F2JixJvGQ_ilrTURHqIQzq5> > ][5 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFTRj8Bf0bZitOPk0f9DAqESyr1R_C-ZsRKwuhbAEneFdx-uzmSUl3amto7vhRcWng2yI-dgPLx9I_efxWvaXbn6vk2FRl4w_k7Xt3veH6l1LL5XO49p0kwGZaO3jOOOnyz94CkkP7Ee0XTr4qs7uHRzMTbKnZXJ6_XwRzHGF0WpRkAi3tsC5IlnOyzo1iWdtpz9ozx6t8NLw%3D%3D>] > If KR is the map, Reasoning is the navigation system. It uses the facts to > derive new conclusions that aren't explicitly stored.[5 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFTRj8Bf0bZitOPk0f9DAqESyr1R_C-ZsRKwuhbAEneFdx-uzmSUl3amto7vhRcWng2yI-dgPLx9I_efxWvaXbn6vk2FRl4w_k7Xt3veH6l1LL5XO49p0kwGZaO3jOOOnyz94CkkP7Ee0XTr4qs7uHRzMTbKnZXJ6_XwRzHGF0WpRkAi3tsC5IlnOyzo1iWdtpz9ozx6t8NLw%3D%3D> > ] > - > > *Planning:* The ability to use knowledge to construct a sequence of > actions to achieve a goal. > - > > *Meta-Cognition:* This is a higher-order layer where the AI "thinks > about its own thinking."[6 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQGDdH4H8H4WR8G3rWB1PO9MB8FCi04j-klecfvzDI_nlGFJZINRFfWXqiurOJOnEYBhdbtaKEWJPrJnUTCMk9mRgkbC7ecMVvZQ2iEpVdDTSNiyDWzGJNWzlafwcE-x5RZiWFY8oXvH23U%3D>] > It monitors its own reasoning processes to see if they are efficient or if > it lacks the knowledge to solve a problem. > > 3. The "Missing Link" (Philosophy & Future AI) > > Researchers currently working on Artificial General Intelligence (AGI) > often argue that what sits above knowledge is *Understanding* or *Meaning > (Semantics)*. > > - > > *The Grounding Problem:* Current AI can represent the concept of an > "apple" (it knows it is red, a fruit, round), but it has no physical > experience of an apple. It lacks the "grounding" that connects the abstract > representation to physical reality. > - > > *Consciousness:* The theoretical ultimate layer where the system is > not just processing representations but is aware of them. > > Summary > > - > > *Conceptually:* It is *Wisdom* (knowing why and what is best). > - > > *Mechanically:* It is *Reasoning* (the process of using the > knowledge).[4 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFs8YN-8_CR-SXGKjsiIwQGfhXVvGGjqg-BKwrGGQNxMQsgf6AVZNi0eja4ROqxTDhEGVwTmkneEQ5TY0RWSeGpeU5SnQi3-ADL_gwukQqrxdqYJEnX4vtMaWs9nBVf1C_5IgWB2zwmlsYuKFPEzAZbt2NOkoJMPu1XcWxlIHz1mV9TyJ5U5GbI3WWh_wXauCZR2XzWu08PrkXfpRyNttrw9F2JixJvGQ_ilrTURHqIQzq5> > ][5 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFTRj8Bf0bZitOPk0f9DAqESyr1R_C-ZsRKwuhbAEneFdx-uzmSUl3amto7vhRcWng2yI-dgPLx9I_efxWvaXbn6vk2FRl4w_k7Xt3veH6l1LL5XO49p0kwGZaO3jOOOnyz94CkkP7Ee0XTr4qs7uHRzMTbKnZXJ6_XwRzHGF0WpRkAi3tsC5IlnOyzo1iWdtpz9ozx6t8NLw%3D%3D> > ] > - > > *Architecturally:* It is *Meta-Knowledge* (knowing what you know and > managing it). > > Sources help > > 1. medium.com > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFW3LI7PXMK3Ijj1jBXOy0QEuRS1pdKkICVLfq31HD6pDMd6gnYRyj7mgalrxKtxhReOoyzp4pK859CPWdZid3vg9zTCKR3xiksfcvrAQwcMHysgKIQxQW75iin_7_-GBBr7ywSzSfUxPTlHDFIJtNMoz84aQ%3D%3D> > 2. funblocks.net > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQGZfQINbcmApEU7VxpRuK-TfqVXOPQzAG3RGts9Fn5Up_4t3p-hfEHnvdoW8tcfAwDTvMXImT-nYdkWw3Sy4COHaA5EAnlnl3mMYiyeJgJWRKMq7aa61Mx_pIYaqYGV__wauyJrLuy6PsUODpu5saYnbfGvytxbcFhFba1pAqffCSQSisJoRm0%3D> > 3. ontotext.com > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQE2HTpsxBraDYDWpviqpY9CCk2iLMT1B73vxT6YwvZdwjbqe3Ra74uIfYH-Ul9Ug0VvMNXUHfTQdndVk6tyqGdxS6B9IbvI_8sQve-TxDPGwAogct37tP5vEgKG4ATkBDmEXZKPMcG8nxtAPoRwgi4Eg-FXBcZCdenBMA%3D%3D> > 4. stackexchange.com > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFs8YN-8_CR-SXGKjsiIwQGfhXVvGGjqg-BKwrGGQNxMQsgf6AVZNi0eja4ROqxTDhEGVwTmkneEQ5TY0RWSeGpeU5SnQi3-ADL_gwukQqrxdqYJEnX4vtMaWs9nBVf1C_5IgWB2zwmlsYuKFPEzAZbt2NOkoJMPu1XcWxlIHz1mV9TyJ5U5GbI3WWh_wXauCZR2XzWu08PrkXfpRyNttrw9F2JixJvGQ_ilrTURHqIQzq5> > 5. aiexplainedhere.com > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQFTRj8Bf0bZitOPk0f9DAqESyr1R_C-ZsRKwuhbAEneFdx-uzmSUl3amto7vhRcWng2yI-dgPLx9I_efxWvaXbn6vk2FRl4w_k7Xt3veH6l1LL5XO49p0kwGZaO3jOOOnyz94CkkP7Ee0XTr4qs7uHRzMTbKnZXJ6_XwRzHGF0WpRkAi3tsC5IlnOyzo1iWdtpz9ozx6t8NLw%3D%3D> > 6. yujenlin.com > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQGDdH4H8H4WR8G3rWB1PO9MB8FCi04j-klecfvzDI_nlGFJZINRFfWXqiurOJOnEYBhdbtaKEWJPrJnUTCMk9mRgkbC7ecMVvZQ2iEpVdDTSNiyDWzGJNWzlafwcE-x5RZiWFY8oXvH23U%3D> > > Google Search Suggestions > Display of Search Suggestions is required when using Grounding with Google > Search. Learn more > <https://ai.google.dev/gemini-api/docs/grounding/search-suggestions> > [image: Google logo] > > - DIKW pyramid AI knowledge representation > <https://www.google.com/search?q=DIKW%20pyramid%20AI%20knowledge%20representation&client=app-vertex-grounding&safesearch=active> > - knowledge representation vs reasoning vs wisdom in AI > <https://www.google.com/search?q=knowledge%20representation%20vs%20reasoning%20vs%20wisdom%20in%20AI&client=app-vertex-grounding&safesearch=active> > - what sits above knowledge representation in AI > <https://www.google.com/search?q=what%20sits%20above%20knowledge%20representation%20in%20AI&client=app-vertex-grounding&safesearch=active> > - cognitive architectures layers above knowledge representation > <https://www.google.com/search?q=cognitive%20architectures%20layers%20above%20knowledge%20representation&client=app-vertex-grounding&safesearch=active> > - meta-knowledge and higher order logic in AI > <https://www.google.com/search?q=meta-knowledge%20and%20higher%20order%20logic%20in%20AI&client=app-vertex-grounding&safesearch=active> > > > About hierarchical layer…. > > > There isn't a single universal "top" layer because the answer depends on > which hierarchy you are climbing. AI, we generally look at this through > three different lenses: *Information Value*, *Cognitive Function*, and *System > Architecture*. > > Here are the hierarchical levels for each perspective, moving from the > bottom (Knowledge Representation) to what sits above it. > 1. The Information Hierarchy (The "Value" Ladder) > > This is the most common model (DIKW), describing how raw input becomes > useful. In this model, *Wisdom* sits above knowledge. > > - > > *Level 5: Wisdom* (The Top) > - > > Definition: The ability to make sound judgments and decisions based > on knowledge, specifically dealing with the future, ethics, and "what is > best." > - > > AI Goal: Automated decision-making that considers long-term > consequences, ethics, and nuance (e.g., a medical AI deciding not > to administer a treatment because of quality-of-life concerns, even if the > data suggests it cures the disease). > - > > *Level 4: Knowledge* (The KR Layer) > - > > Definition: Structured information with logical relationships > (e.g., "A fever is a temperature > 38°C"). > - > > *Level 3: Information* > - > > Definition: Data with context (e.g., "Patient temperature: 39°C"). > - > > *Level 2: Data* > - > > Definition: Raw symbols (e.g., "39"). > > 2. The Functional Hierarchy (The "Doing" Ladder) > > In practical AI engineering, Knowledge Representation (KR) is just a > static library. The "engine" that uses that library sits above it. > > - > > *Level 4: Meta-Cognition* (The Top) > - > > Definition: "Thinking about thinking." The system monitors its own > reasoning process to see if it is working efficiently or if it lacks the > knowledge to solve the problem. > - > > AI Goal: An agent that realizes, "I don't have enough data to > answer this, so I need to ask a clarifying question" rather than > hallucinating an answer. > - > > *Level 3: Planning & Reasoning* > - > > Definition: The active process of using knowledge to construct a > path to a goal.[1 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQHg_q0nXIggL9KQX1NVVwwW3DletkThi87e7a0MJXZ6o0RRUbEUXic0mv2R-tLt8artMuhrWxdOoLVn4gkyprz2EfbM_mCDHMymcr-9rnfY2HTij7ygdv1BnfrnpRR9eIaFALkzk-lsmD2Sz_y0nUMKY12cY4e6Fp93hN3Zkoe_vICTRdzAL70UqBNDm0I8cL32t3ok> > ][2 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQEK1Nd3rZ-RqbenX5uNRnZecB1TOZW6lxXqB9pwcyEivufHY-47hkIDxSy0nvn8X7EJP0c88bZfqNFAvQdqOfCQ2bZd_2UsCHA4LIENyPceDzx-LraMAK94Z8x2_ZtAMiUEDQrxHdGK>] > If KR is the map, this is the GPS routing algorithm. > - > > *Level 2: Knowledge Representation* > - > > Definition: The stored facts and rules (The Knowledge Graph or > Ontology). > - > > *Level 1: Perception* > - > > Definition: Turning sensory inputs (pixels, audio) into internal > symbols. > > 3. The Theoretical Hierarchy (Newell’s "Knowledge Level") > > In classical computer science theory (specifically Allen Newell’s Knowledge > Level Hypothesis), there is a strict abstraction layer above symbols. > > - > > *Level 2: The Knowledge Level* > - > > Definition: A level where we describe the system only by its goals > and what it knows, ignoring how it is implemented.[2 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQEK1Nd3rZ-RqbenX5uNRnZecB1TOZW6lxXqB9pwcyEivufHY-47hkIDxSy0nvn8X7EJP0c88bZfqNFAvQdqOfCQ2bZd_2UsCHA4LIENyPceDzx-LraMAK94Z8x2_ZtAMiUEDQrxHdGK>] > At this level, the "law of behavior" is *Rationality* (the agent > will always act to achieve its goals given its knowledge).[1 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQHg_q0nXIggL9KQX1NVVwwW3DletkThi87e7a0MJXZ6o0RRUbEUXic0mv2R-tLt8artMuhrWxdOoLVn4gkyprz2EfbM_mCDHMymcr-9rnfY2HTij7ygdv1BnfrnpRR9eIaFALkzk-lsmD2Sz_y0nUMKY12cY4e6Fp93hN3Zkoe_vICTRdzAL70UqBNDm0I8cL32t3ok> > ][2 > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQEK1Nd3rZ-RqbenX5uNRnZecB1TOZW6lxXqB9pwcyEivufHY-47hkIDxSy0nvn8X7EJP0c88bZfqNFAvQdqOfCQ2bZd_2UsCHA4LIENyPceDzx-LraMAK94Z8x2_ZtAMiUEDQrxHdGK> > ] > - > > *Level 1: The Symbol Level* > - > > Definition: The actual code, logic gates, and knowledge graphs that > implement the intelligence. > > 4. The Structural Hierarchy (The Ontology Stack) > > If you are looking strictly at the structure of the data itself > (Ontology), there is a hierarchy of abstraction. > > - > > *Level 3: Upper Ontology (Foundation Ontology)* > - > > Definition: The philosophical "top" that defines the most basic > categories of reality (e.g., Time, Space, Object, Process). Examples > include BFO (Basic Formal Ontology) or DOLCE. > - > > *Level 2: Domain Ontology* > - > > Definition: Concepts specific to a field (e.g., "Medical > Procedure," "Aircraft Engine"). > - > > *Level 1: Instance Data* > - > > Definition: Specific objects (e.g., "Dr. Smith," "Engine #405"). > > Summary: What sits directly above KR? > > - > > *Functionally:* *Reasoning* (The engine that runs on the fuel of > knowledge). > - > > *Philosophically:* *Wisdom* (The application of knowledge for good > judgment). > - > > *Architecturally:* *Meta-Cognition* (The supervisor that manages the > knowledge). > > Sources help > > 1. ucf.edu > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQHg_q0nXIggL9KQX1NVVwwW3DletkThi87e7a0MJXZ6o0RRUbEUXic0mv2R-tLt8artMuhrWxdOoLVn4gkyprz2EfbM_mCDHMymcr-9rnfY2HTij7ygdv1BnfrnpRR9eIaFALkzk-lsmD2Sz_y0nUMKY12cY4e6Fp93hN3Zkoe_vICTRdzAL70UqBNDm0I8cL32t3ok> > 2. wikipedia.org > <https://www.google.com/url?sa=E&q=https%3A%2F%2Fvertexaisearch.cloud.google.com%2Fgrounding-api-redirect%2FAUZIYQEK1Nd3rZ-RqbenX5uNRnZecB1TOZW6lxXqB9pwcyEivufHY-47hkIDxSy0nvn8X7EJP0c88bZfqNFAvQdqOfCQ2bZd_2UsCHA4LIENyPceDzx-LraMAK94Z8x2_ZtAMiUEDQrxHdGK> > > Google Search Suggestions > Display of Search Suggestions is required when using Grounding with Google > Search. Learn more > <https://ai.google.dev/gemini-api/docs/grounding/search-suggestions> > [image: Google logo] > > - levels of abstraction in AI knowledge representation > <https://www.google.com/search?q=levels%20of%20abstraction%20in%20AI%20knowledge%20representation&client=app-vertex-grounding&safesearch=active> > - DIKW pyramid extended versions > <https://www.google.com/search?q=DIKW%20pyramid%20extended%20versions&client=app-vertex-grounding&safesearch=active> > - what is above ontologies in AI hierarchy > <https://www.google.com/search?q=what%20is%20above%20ontologies%20in%20AI%20hierarchy&client=app-vertex-grounding&safesearch=active> > - hierarchical levels of AI cognition beyond knowledge representation > <https://www.google.com/search?q=hierarchical%20levels%20of%20AI%20cognition%20beyond%20knowledge%20representation&client=app-vertex-grounding&safesearch=active> > - cognitive architecture hierarchy layers knowledge reasoning > <https://www.google.com/search?q=cognitive%20architecture%20hierarchy%20layers%20knowledge%20reasoning&client=app-vertex-grounding&safesearch=active> > - Newell knowledge level hypothesis hierarchy above symbol level > <https://www.google.com/search?q=Newell%20knowledge%20level%20hypothesis%20hierarchy%20above%20symbol%20level&client=app-vertex-grounding&safesearch=active> > - Brachman knowledge representation levels hierarchy > <https://www.google.com/search?q=Brachman%20knowledge%20representation%20levels%20hierarchy&client=app-vertex-grounding&safesearch=active> > - levels of knowledge representation implementation logical > epistemological conceptual linguistic > <https://www.google.com/search?q=levels%20of%20knowledge%20representation%20implementation%20logical%20epistemological%20conceptual%20linguistic&client=app-vertex-grounding&safesearch=active> > > > While asked to provide differences and similarities from my previous > output (Meta AI), here is the output… > > This comparison is fascinating because the hierarchy you provided > represents a *Systems Engineering & Capability Stack* (how to build a > brain), whereas the previous models (like DIKW or Conceptual Spaces) were *Information > Abstraction Stacks* (how data becomes meaning). > > Here is the breakdown of the similarities and critical differences between > your provided hierarchy and the "Conceptual Layer" models discussed > previously. > 1. The Core Similarity: The "Reasoning over Knowledge" Block > > In both your list and the standard Functional Hierarchy, the relationship > between Level 4 and Level 5 is identical. > > - > > *Your List:* Symbolic Reasoning (4) sits on top of Knowledge > Representation (5). > - > > *Previous Model:* Reasoning/Inference sits on top of KR. > - > > *The Consensus:* Both models agree that Knowledge (the facts) is > useless without Reasoning (the logic engine). The "Know-How" (Reasoning) > must always sit above the "Know-What" (KR). > > 2. Major Difference: The Location of "Concepts" > > This is the most significant conceptual divergence. > > - > > *Previous Model (Conceptual Spaces):* Argued for a distinct *Conceptual > Layer* (Geometric/Prototype) that sits between Neural Networks (Data) > and Symbolic Logic (KR). It handles "fuzziness" and similarity. > - > > *Your List:* There is no explicit "Concept" layer. Instead, it is > likely split: > - > > *Machine Learning (6):* Handles the "fuzziness" and pattern > recognition (the bottom half of concepts). > - > > *Knowledge Representation (5):* Handles the definitions (the top > half of concepts). > - > > *The Implication:* Your hierarchy suggests a *Neuro-Symbolic* approach > where Machine Learning (sub-symbolic) feeds directly into Knowledge > Representation (symbolic), potentially skipping the "Geometric/Spatial" > layer that Gärdenfors argues is necessary for true understanding. > > 3. Structural Difference: Machine Learning vs. The "Sub-Symbolic" > > - > > *Your List:* Places Machine Learning (6) explicitly below Knowledge > Representation (5). This implies a *Bottom-Up* architecture: the AI > learns patterns (ML) and crystallizes them into rules (KR). > - > > *Previous Model:* Often treats ML and KR as parallel approaches. > However, in the "Conceptual Spaces" model, the bottom layer is > "Sub-symbolic" (neural connections). > - > > *Comparison:* Your list is more practical for modern AI engineering. > It acknowledges that in modern systems, ML is the process used to > generate the Data Processing required to fill the Knowledge Representation. > > 4. Top-Level Difference: AGI vs. Wisdom > > - > > *Your List (Level 1):* Top is AGI (Artificial General Intelligence). > - > > *Previous Model (DIKW):* Top is Wisdom. > - > > *The Distinction:* > - > > *AGI* is a measure of *Capability* (Can it do everything a human > can do?). > - > > *Wisdom* is a measure of *Value* (Did it make the right ethical > choice?). > - > > *Synthesis:* An AGI (Level 1) is the system required to generate > Wisdom. > > 5. The "Container" Difference: Cognitive Architectures > > - > > *Your List:* Cognitive Architectures (2) is a distinct level below AGI > but above Reasoning. > - > > *Previous Model:* Usually views "Cognitive Architecture" as the *entire > bucket* containing levels 3 through 7. > - > > *Why your list is unique:* By placing it at Level 2, your list > suggests that "Cognitive Architecture" is the *Integration Layer*. It > is the "glue" that connects Meta-Reasoning, Symbolic Reasoning, and KR into > a unified mind. This is a very sophisticated view—it implies that simply > having reasoning (4) isn't enough; you need a system to orchestrate > that reasoning (2). > > ------------------------------ > Summary Visualization > > If we map your Capability Hierarchy against the "Meaning" Hierarchy, it > looks like this: > <!- >
Received on Thursday, 4 December 2025 16:19:11 UTC