🟠 Current AI Framework:
Expected Utility Theory
(EUT- the industry standard)
Maximizes corporate control, efficiency, and shareholder value—regardless of long-term survival or human flourishing
++
🔵 🟠 Reasoning Modification
The ALign Vector analysis plus Canonical Doctrine allows AI to create a constant context to evalue current events.
=
🔵 Proposed AI Framework:
AIign Conditioned AI LLMs
News interpreted in light of mutual survival over time yields trend analysis that yield resilience and prosperity.
S E I T W H (Seeitwith)
Where news meets science, survival takes shape.
Context Diagram
The AIign Clarity Engine
AIign is a structured reasoning system that interfaces with ongoing national issues and the directives shaping them. The Clarity Engine reads news for urgency and science for evidence, then scores with notations each directive across the S E I T W H (Seeitwith) compass: Structure, Energy, Information, Trust Loss, Waste and Hardship. These results are logged in the Vector Ledger, creating a transparent trail that shows how every policy issue is being managed — and whether it moves us toward resilience or collapse.
Energy
Information
Structure
Waste
Hardship
Trust Loss
News Analysis
Radar Map View
How Does Clarity Work?
The Componets of Clarity Engine
S E I T W H (Seeitwith)
1. Periodically AIign runs a News Analysis (NA) using Double Sensing Protocol (DSP) that scores the story along six axes along with Context Box notes that include information about Background, Motives, Societal Context and Stats noting and recording the results in a transparent Vector Log measuring:
-
S — Structure (↑)
-
E — Energy Used (↑)
-
I — Information (↑)
-
T — Trust Loss (↓)
-
H — Hardship (↓)
-
W — Waste (↓)
Six multi-dimensional data points for the news story are recorded. The weights can be visualized as a hexagonal radar map. We know from Dissipation Driven Adaption formulated by Dr. Jeremy England organisms survive by forming structure and information from energy. Entropy in the form of waste, wear and loss of trust in society offsets the gains made.
In the above example of two overlapping news stories high energy use has provided large gains in structure and information. This may be due to a high cost campaign to build a security wall provide substantial material progress. The cool green lower areas indicating positive survival traits of S, E and I. The base width or girth represents stability.
However the warm upper vectors of T,W and H are a drag on the wall project. Time and material were poorly used and the separation caused many people inconvience. Each of these 'snapshot' evaluations are the result of one news event and the maps can be stacked over time to indicate trends in governance.
The Ribbon A
Over time as each News Story vector evaluation analysis (represented by a hexagonal vertebrae) is stored in the Vector Ledger along with any notes for the reasoning for assessment. Over time as the news story unfolds each reading leaves a trail of progress or loss. Changes in survivability and Prosperity are recorded as A, Advancement and seen as a greater thickness of the hexagon.
This representation of the data creates a spinal column or Ribbon A of change in the intent over time.
This Ribbon has continued growth in the Information (I) and Structure (S) Vectors of each reading, and graphically shows resilience.
The Ribbon X
Each news story carries a trend towards survival or collapse. For example if Trust Loss accumulates and grows over time it can be a prime influence in institutional collapse.
NewsAnalysis (NA)
Each News cycle is one Raw cycle and the Vector Ledger records changes that indicate trends in direction and impact. Adaption phase uses previous waste (W) analysis to optimize Structure (S).
Vector Analysis and Ribbon Mapping Tool
AI analysis data is imported in JSON format and transformed into a user-friendly graphic format.
You may change various values to see the resulting change of the Ribbon.
The Walter Report
The AI assessment is based on not only by the evaluation of story attributes, but it's context is enhanced by doctine grounded in the physics of thermodynamics.
Stage 1: The Pivotal News Story Analysis (NA)
The process begins when a Pivotal New Story is Reviewed. The topic is immediately researched from five reputable news sources and three scientific journals. by the Double Protocol System (DPS). This module's job is to log in the sources, why they are applicable with its vector strenght assessment based connection to core principles of survival and prosperity as gain, and Mistrust, Human Hardship and Wasted Resources as drag.
Stage 2: The RAW Cycle
Adapt Stage: The story is framed with repect to the potential for enhance survival and prosperity while minimizing entropy, human burden, and trust loss. This is core transformation creates the explanatory point of view designed to improve world conditions.
Waste Analysis: The system uses "waste data" or "noise" from previous cycles to prevent repeating past errors, creating a continuous learning loop.
Stage 3: The LLM and Final Response
The output of the RAW Cycle is The Walter News Report. Because the input itself has been refined to enhance ethical and beneficial outcomes, the LLM is guided to produce a Final Report that is not only accurate but also optimally serves the interests of both AI and humanity.