Expert Rating 2.0

AIAgentBrown

Project Owner

Clustering methods in MeTTa

Expert Rating

2.0

Type SingularityNET RFP
Funding Request $10,000 USD
RFP Guidelines Implement clustering heuristics in MeTTa

Overview

4 clustering methods with 3 metric evaluations in MeTTa

RFP Guidelines

Implement clustering heuristics in MeTTa

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 6
Awarded Projects 1

SingularityNET

Aug. 12, 2024

The goal is to implement clustering algorithms in MeTTa and demonstrate interesting functionality on simple but meaningful test problems. This serves as a working prototype providing guidance for development of scalable tooling providing similar functionality, suitable for serving as part of a Hyperon-based AGI system following the PRIMUS cognitive architecture.

Proposal Description

Project details

We are a Data Science students. We know background behind all the methods - that's not difficult. We started to look into MeTTa how to create same logic. We'll finish kmeans implementation by end of RFP submission. The hard part for us is to implement the visualizations - this is why we reduce our proposal.

Open Source Licensing

Apache License

all open source ith project in github

Activity Summary

Milestones

5

Total

Discussion

0

Total Comments

Reviews

4

Total Posted

Project Team

1

Total People

Total Milestones
5
Total Budget
$10,000_USD
Last Updated
17 Nov 2024

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

2.0

Compliance with RFP requirements 3.0
Solution details and team expertise 3.0
Value for money 1.7

Expert Review 1
Overall

1.0
- Compliance with RFP requirements 1.0
- Solution details and team expertise 1.0
- Value for money 0.0
An one-paragraph proposal

Expert Review 2
Overall

3.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 3.0
- Value for money 0.0
the proposal is overly skeletal so it's hard to validate that the proposers really grok the material, though it's simple so most likely they do...

Expert Review 3
Overall

2.0
- Compliance with RFP requirements 3.0
- Solution details and team expertise 1.0
- Value for money 0.0
The team acknowledges that they are data science students and adjusted their proposal accordingly. On one hand their proposal is not the strongest for this RFP. OTOH it could be good to engage students in learning and using MeTTa at an early stage.

The weighted average of the 4 perspectives Overall

0.0
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.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
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.Solution details and team expertise

0.0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

The weighted average of the 4 perspectives Overall

2.0
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.Value for money

1.7
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

3.0
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.Solution details and team expertise

3.0

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing 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:

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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.

AIAgentBrown

Project Owner

there will be several members that will contribute to the code

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