This RFP seeks proposals that experiment with concept blending techniques and formal concept analysis (including fuzzy and paraconsistent variations) using the MeTTa programming language within OpenCog Hyperon. The goal is to explore methods for generating new concepts from existing data and concepts, and evaluating these processes for creativity and efficiency.
This RFP seeks proposals that experiment with concept blending techniques and formal concept analysis (including fuzzy and paraconsistent variations) using the MeTTa programming language within OpenCog Hyperon. The goal is to explore methods for generating new concepts from existing data and concepts, and evaluating these processes for creativity and efficiency.
Proposal Description
Company Name (if applicable)
SubThought Corp
Open Source Licensing
Custom
Open source Code will be bundled and provided in the final solution. No restrictions on re-use of open source components.
No due diligence was done by the Expert Reviewer: quote, "Not wading through a 1.5 hour video to find relevant/targeted material." Such is life. It doesn't matter.
I'm reminded of the cartoon "Expert Answers Simple but Wrong (go left) Complex but correct (go right) with most people going left. (Sigh). Brief Synopsis: LLMs teach us about feature detectors (i.e., tokenizers) and associative memory. AlphaGo & AlphaZero combined associative memories with state space search. This proposal combines logico-hierarchical memory units (named schemes, aka neural propositions) with associative memory and multistrategy search in both action space (via actuations) and knowledge space (via transmutations) to perform continuous online learning (without separate training and test phases). The Genesis video explains the scheme data structure used with robotic embodiments to do proprioception, interception, perception, and recollection. The Coordination video explains activation, association, belief propagation, problem solving (action synthesis and selection) and reasoning (operation synthesis). Applications in localization and mapping, language acquisition, and story understanding are explained. The elements of AGI are not simplistic and cannot be glossed over. But these elements are easily understood to those who have open minds and patience. No problem if this is not awarded. The research is solid and will be independently recognized, whether by this organization or another. Thanks for taking the time to actually review the work before rendering a decision.
Expert Ratings
Reviews & Ratings
Group Expert Rating (Final)
Overall
1.0
Feasibility1.0
Desirabilty1.0
Usefulness1.0
Please create account or login to write a review and rate.
Not wading through a 1.5 hour video to find relevant/targeted material.
Expert Review (anonymous)
Final Group Rating
Rating Categories
Compliance with RFP requirements
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.
Solution details and team expertise
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.
Value for money
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.
About Expert Reviews
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.
A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.
Over 20 years in AI design & development including language development and Cognitive Architecture design patterns. See youtube.com/@CognitiveArchitectures for details.
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok
SubThought
Project Owner Feb 2, 2025 | 5:39 AMEdit Comment
Processing...
Please wait a moment!
No due diligence was done by the Expert Reviewer: quote, "Not wading through a 1.5 hour video to find relevant/targeted material." Such is life. It doesn't matter. I'm reminded of the cartoon "Expert Answers Simple but Wrong (go left) Complex but correct (go right) with most people going left. (Sigh). Brief Synopsis: LLMs teach us about feature detectors (i.e., tokenizers) and associative memory. AlphaGo & AlphaZero combined associative memories with state space search. This proposal combines logico-hierarchical memory units (named schemes, aka neural propositions) with associative memory and multistrategy search in both action space (via actuations) and knowledge space (via transmutations) to perform continuous online learning (without separate training and test phases). The Genesis video explains the scheme data structure used with robotic embodiments to do proprioception, interception, perception, and recollection. The Coordination video explains activation, association, belief propagation, problem solving (action synthesis and selection) and reasoning (operation synthesis). Applications in localization and mapping, language acquisition, and story understanding are explained. The elements of AGI are not simplistic and cannot be glossed over. But these elements are easily understood to those who have open minds and patience. No problem if this is not awarded. The research is solid and will be independently recognized, whether by this organization or another. Thanks for taking the time to actually review the work before rendering a decision.