A groundbreaking application that integrates LLMs with MeTTa's symbolic reasoning via metta-motto, enabling dynamic, context-aware, and reasoning-driven language interactions for virtual agents in SophiaVerse.
Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.
Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.
Proposal Description
Project details
The NeotericOS Language Action Model is a groundbreaking approach to virtual agent interaction that bridges symbolic reasoning and generative AI through the MeTTa-Motto integration. Unlike traditional conversational AI, this model provides a multi-layered system for virtual agents to:
Understand and Interpret Complex Commands
Transform natural language commands into executable symbolic representations
Use MeTTa's knowledge graph to contextualize and expand user intentions
Enable multi-step goal decomposition with transparent reasoning
Dynamic Action Planning
Generate executable action plans based on symbolic constraints
Maintain a dynamic knowledge base of agent capabilities, context, and learned skills
Support hierarchical goal achievement with justifiable reasoning chains
Contextual Learning and Adaptation
Incrementally update agent knowledge through interactions
Learn and store new skills, social protocols, and contextual understanding
Provide transparent mechanisms for skill acquisition and modification
While proposal aligns conceptually with the RFP, the approach is vague. Needs significantly more specificity to evaluate its viability.
Expert Review 2
Overall
4.0
Compliance with RFP requirements5.0
Solution details and team expertise3.0
Value for money4.0
Expert Review 3
Overall
5.0
Compliance with RFP requirements5.0
Solution details and team expertise5.0
Value for money4.0
It seems reasonable to use semantic parsing to turn commands into plans which are then represented in MeTTa and then executed to drive actions in SV or other virtual worlds
Doing this with full awesome functionality is a big project but doing it in a simple way seems a good integration demo involving LLM hyperon and virtual world
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.
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