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STRUCTURED SPECIFICATION

“What would it take to build the Structured Solution Platform as a minimum viable product (MVP)?”

This specification is designed to address all of the possible requirements necessary to make Structured Speculation a viable product.

Outlined below is a set of Conditions of Satisfaction (CoS) which serve merely as a place to start the discussion. The recommendations for developing a platform as outlined use readymade technologies and tools. It is very possible that more streamlined routes may become available as with expert industry feedback. Ongoing research and innovation will fine-tune this specification until a minimum viable product is produced.

It is expected that design requirements will further crystallize as the Structured Speculation development team explores how all of these components will fit together efficiently and securely.

The technologies outlined are possibilities, but any solution implemented will require a great deal of customization and development which is still to be determined.

INTENTION

Structured Speculation will enable people to model, engineer, and achieve their goals with interactive technology.

The platform is an innovative way to solve problems collaboratively while building bridges between diverse perspectives.

Structured Speculation’s most immediate benefits are in project management, where SSPEC anticipates paths which allow for creativity and innovation as well as cost-effectiveness.

Product

Structured Speculation (SSPEC) Platform:

Developed as a collaboration between the OpenAI Generative Pre-trained Transformer 3 (GPT-3), LDA, and Loci frameworks for real time relational data integration.

Components

The frontend is in the form of an interactive web application which helps people query and harness the power of Structured Solutions.

The backend consists of a set of REST APIs that provide access to SPECULATE, transactional data, and metadata information.

It provides a user with multiple querying options for both results regarding Structured Solutions, as well as raw datasets about specific outputs from their experiments.

To be viable, the Structured Speculation ecosystem will require an adaptable grid computing model similar to algorithms used for BOINC. These APIs provide a UI and logic interface which can be hosted on the Ethereum blockchain or distributed centrally among nodes. The mechanisms for linking contingencies to real-world actions are deep learning algorithms which power the Structured Solution.

The approach is Architecturally diversified: GPT-3 from Open AI running on a GPU is using LDA, Loci model, and recursive searches across various categories and specializations. The COS are tracked through Smart Contracts, logged on blockchain for accountability and transparency or a Blockchain which has Stakeholders that have appropriate authorities required for decision making at each node within accordance with diverse stakeholder policies.

The mechanisms for linking contingencies to real-world actions are deep learning algorithms which power the Structured Solution.

The Loci Framework identifies, matches, and links specific contingent events to groups of people with appropriate skills willing to work toward the outcome.

Contingent events are created as Conditions of Satisfaction which require a specific set of services or resources for completion. A contractor pledges to evaluate each event on a case-by-case basis in order of priority and provide those services only if they end up being necessary.

All transactions go through Smart Contracts using blockchain/crypto assets so that performance can be transparent and accountable.

Key Features:

SPECULATE  is an innovative, interactive technology which can be used to solve problems collaboratively. It is a way of modeling and achieving goals with artificial intelligence while building bridges between disparate perspectives.

The platform was created for project management by anticipating paths that allow for creativity and innovation as well as cost-effectiveness.

Key features are its practical applications in solving problems creatively while simultaneously increasing collaboration among those who see each other’s different points of view.

This year will see the introduction of SSPEC Release 1 – SPECULATE   Predictive Engine for Collaboration Utilizing Learning Algorithms to Engage.

SPECULATE will provide an innovative new tool involving generative deep learning models from the world’s leading artificial intelligence company Open AI (GPT-3), latent Dirichlet allocation (LDA), linked through a Loci framework

Conceptual outline:

GPT-3 is a machine learning tool which deploys and trains on large data sets of diverse text types to create accurate, scalable algorithms that enable the generation of unstructured text from any input.

This capability allows SPECULATE to provide a wide range of contextually specified solutions based on novel text generated by GPT-3 – with no prior explicit training needed.

Running this technology on a Loci framework, GPT-3 is able to synthesize insights about major world issues by mining sources, the Web and social media. In conjunction with SPECULATE’s enterprise level capability to sort unstructured information in real time into categorical buckets, GPT-3 can remotely harness the processing power of a network node for any data types without incurring local storage or computational overhead costs.

The training process for this technology is wide-ranging and complicated, but once it’s complete, it allows a method of generating outcomes that are both universal and contextual. This detail has been shown to be crucial as new challenges rise because what we don’t know impacts us differently than what we do know (particularly globally) due to our unique socio-economic profiles.

The effect is compounded when unknown complexities arise—which will happen infrequently at first but increase dramatically over time as machines learn how to rebuild themselves faster than humans can manage:

“Comprehending which tasks machines should have access to takes careful consideration.”

As these emerging artificial intelligence technologies progress from demonstrating partial competence in narrow domains towards general artificial intelligence – they will become capable of undertaking tasks which had originally been deemed too difficult for automation or could not reasonably be carried out by machines.”

Unconscious bias and uninformed assumptions make efforts made currently seem rudimentary; and extrapolation logical,

Each model is connected through a Loci framework, which unites them together as one system. It will use these generative deep learning models to provide innovative new predicative tools systems with collaborative capabilities.

This technology allows people, identities, organizations and groups of various kinds to engage collaboratively by listening openly, articulating ideas clearly and encouraging input from other voices who have unique perspectives across many different disciplines in problem solving.

The potential for future multi-platform integration enabled by these trends will represent formidable challenges given past experiences with AI systems improperly using marginally related personal content within the same platform where personally identifiable information was not redacted beforehand.

During Structured Speculation, an individual or group unpacks all that they know about problems being solved; selecting relevant bits of information such as potential options with specific benefits or drawbacks, costs etc., then SPECULATE tries out a set of possible outcomes with no restrictions or limitations.

The outputs generated include what leads up to success: goals and objectives overviewed at each stage along the way satisfying the CoS conditions – complete blueprints for successful endings – even when those results are improbable or seemingly impossible in reality; all stakeholders involved are accurately identified early on; risks are outlined and success is assured one problem at a time.

During this process knowledge may be made explicit rather than implicit because collaborators share their thoughts and visions collaboratively, and then open themselves up to constructive feedback that can enrich their perspective beyond what they could do alone

Engineering

Deep neural networks send inputs into random output layer enabling thousands of functional combinations.

We train these networks by utilizing data from cases marked as successes (a.k.a. counterfactuals). These predict how likely something would succeed if all factors were perfect) so long as its plausible given time constraints).

Counterfactually trained Deep Networks enable us to anticipate paths toward positive futures which don’t exist yet but seem within reach given enough effort—sparking creativity and innovation among diverse communities engaged through techniques like Structured Speculation/SPECULATE

Proposed Conditions of Satisfaction

This section describes some practical considerations necessary for implementing the Connected Community Hub with an artificial intelligence engine based on Generative Pre-trained Transformer 3 (GPT-3) from Open AI.

  • The desired outcome is a full set of shared resources generated via GPT-3 which empowers collaborators through deep learning capabilities, while providing robust interactions between diverse stakeholders, and software tools which can help realize visions and manage goals as though they have been achieved already. And finally e tools that allow Conditions of Satisfaction (CoS) to be identified so future success is assured one step at a time.
  • A two way bridge connecting perspectives across disciplines will allow knowledge made explicit rather than implicit within this system. Collaboration among diverse peoples produce innovative ideas that are realistically achievable without sacrificing sustainability or safety standards

This proposal is a starting point to gather all requirements necessary for a cohesive project management solution, including any patent pending designs. and a platform for innovation and incubation.

The Structured Speculation Connected Community Hub with Artificial Intelligence (including the SPECULATE engine,) must be able:

  • To propose a goal as though it has already been achieved, and use that goal as the basis to generate possible conditions that will be met when the goal is achieved.
  • To reverse-engineer the necessary steps for each condition to hold true, identifying actions and partnerships which pave way for completion and simultaneously account for unpredicted factors.
  • To put forth ideas collaboratively and share them publicly so they contribute directly towards resolution.
  • To combine and track proposed outcomes, resources and pathways into Structured Solutions; single logical units containing the comprehensive strategic outline. This includes specific goals, timeline(s) and milestones.
  • To also create step-by-step pathways with clear indicators of progress along the way.
  • To Identify and communicate with project collaborators for fulfillment on the execution of Structured Solutions.
  • To facilitate partnerships and contracts through networks of experts or partnerships; to assemble team members with knowledge in one or many disciplines required to complete these projects collaboratively; to bring people together from varying backgrounds, professions, nationalities etc.–Those who may never have come into contact had it not been for our shared interest in solving problems.
  • To foster a collective intelligence capable of finding new ways of seeing old problems or new solutions to fresh challenges
  • To leverage public and privately held databases using a Loci framework which locates data using tracking objects and tracks them throughout their life-cycle. This happens without specifying predetermined types or classes up front; relations provide abstraction from data structures, separating computational kernels from data management systems but still supporting multiple configurations (blockchain ledger).
  • Using Hashed containers instead distributed arrays allows Loci’s programming model flexibility in terms of scalability across architectures; – relationships created by stakeholders who want relevant information transparently available.

The prerequisite for Structured Speculation is a modular system for viewing and interacting with data based on the principles of Open Semantic Modeling Environments.

  1. Equal consideration of diverse opinions, circumstances, potential outcomes and stakeholder implications,
  2. Project management in approach to provide both predictability and performance flexibility,
  3. A strategy for disrupting forces or conditions which can cause harm to people (including death) as prescribed herein;
    The Structured Speculation approach guides operationalizing concepts and framing solutions by understanding every necessary step before any work begins.
    The key tenets of this speculative design process are not to cede the power of choice but rather strategize each element systematically as outlined by its specific requirements ahead of time – initiating participation and collaboration within diverse perspectives then providing creative ideas on how those individual needs might be best answered collectively with a collective discussion facilitated around these details.

Ready to start Collaborating?

Send us a message stating your interest, background, and what you bring to the project!

[email protected] or visit our website or Slack channel for more information!


The Doctrine for Structured Speculation by Anthonio Pettit is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License under the following terms
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