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.
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.
I have the following three comments about all the clustering proposals and to be fair, I will mention them for all the proposals. At the end, you can see my comments specifically for this current proposal.
First, I was expecting to see more on the difficulties that one may face when a clustering algorithm is implemented in MeTTa, in other words, MaTTa-specific challenges, and the proposing team plans to handle them. I did not see that in any of the proposals.
Second, I was expecting to see their plan for making sure the MeTTa clustering library will have the ability to work robustly on diverse datasets. For example, they could have listed a few datasets that may cause problems for a clustering algorithm and could have mentioned how they plan to avoid those problems.
Third, based on my experience with clustering algorithms, most computational gains come from vectorization. None of the proposals even mention that even though the RFP specifically mentions Concurrent processing and the ability to work on large datasets.
Proposal-specific comments:
This proposal is not complete.
Expert Review 2
Overall
2.0
Compliance with RFP requirements4.0
Solution details and team expertise1.0
Value for money0.0
it's much to sketchy, not a full proposal
Expert Review 3
Overall
2.0
Compliance with RFP requirements2.0
Solution details and team expertise2.0
Value for money0.0
Most basic of information is provided. Hard to distinguish but that is also the nature of this particular RFP.
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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