Ramin Barati
Project Owner1- Time and resource management 2- User content creation 3- Code Review 4- Data engineering 5- Software development
Milestone Release 1 |
$2,500 USD | Pending | TBD |
Milestone Release 2 |
$5,000 USD | Pending | TBD |
Milestone Release 3 |
$5,000 USD | Pending | TBD |
Milestone Release 4 |
$2,500 USD | Pending | TBD |
Our team proposes the implementation of clustering heuristics in MeTTa, focusing initially on K-Means, Hierarchical Clustering, Spectral Clustering, and the Gaussian Mixture Model (GMM). Leveraging our experience in variational inference and EM algorithms, including past work on a generalized GMM model, we aim to align our approach with MeTTa’s design philosophy, collaborating closely with the Hyperon community to ensure an intuitive fit. By adhering to conventions from libraries like numpy, scikit-learn, and matplotlib, our project will create a foundational toolkit for unsupervised learning in MeTTa, promoting broader adoption and setting the stage for more advanced applications.
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.
Overall
4.0
3.7
3.7
New reviews and ratings are disabled for Awarded Projects
Overall
2.0
2.0
0.0
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: The proposal lacks details about their plan. For example, they don’t mention what difficulties they anticipate to face when making sure that their solution integrates seamlessly with MeTTa. They don’t mention if they will allow user-specific distance metrics. They don’t add any links to their claimed prior experience in clustering.
Overall
5.0
5.0
0.0
Ramin has contributed to OpenCog Classic in the past and is a solid researcher and developer. This is a relatively simple task which he is clearly very capable of doing, and he's fleshed it out competently in the proposal.
Overall
5.0
4.0
0.0
A solid and straightforward approach focusing on four important clustering algorithms. Well structured proposal focusing on learning MeTTa and communicating with the MeTTa community. Would have been good to discuss evaluation metrics used.
In this initial phase our team will focus on understanding MeTTa’s syntax language constructs and foundational paradigms to ensure a solid grasp of its features and capabilities. This learning phase will include: - Hands-on experimentation with MeTTa code examples and the available documentation. - Collaboration with the Hyperon and OpenCog communities to gain insights and seek guidance where necessary. - Creating an initial interface outline that specifies user interactions with the package, aligning with MeTTa’s syntax and conventions.
A well-prepared team with a thorough understanding of MeTTa, along with a preliminary package design and interface draft to inform the subsequent implementation phases.
$2,500 USD
This milestone will center on the development of the primary clustering algorithms and their integration into MeTTa ensuring that they align with the language’s framework and design goals. Activities in this phase include: - Implementing K-Means Hierarchical Clustering Spectral Clustering and Gaussian Mixture Model (GMM) in MeTTa. - Setting up data ingestion pipelines to accommodate various input formats and data types facilitating seamless data flow. - Creating demo examples for each algorithm and writing comprehensive documentation to guide users on setup usage and output interpretation.
A functional suite of clustering algorithms accompanied by documentation and demo examples to support users in effectively applying these techniques in MeTTa.
$5,000 USD
With the core algorithms in place the focus of this milestone will shift to enhancing usability interactivity and data handling. Specific tasks include: - Developing visualization capabilities to enable users to interpret clustering results graphically. - Implementing concurrency where applicable to optimize performance particularly for large datasets. - Enabling data export functionalities to ensure compatibility with external tools and frameworks. - Preparing a comprehensive technical report detailing the project’s approach results and challenges.
A robust user-friendly clustering module with visualization and exporting options supported by a technical report documenting the implementation.
$5,000 USD
In this final phase the focus will be on polishing the project fostering community engagement and preparing the project for future extensions. Activities include: - Reviewing and refining the codebase to adhere to best practices ensuring maintainability and clarity. - Creating example-driven documentation tutorials and video guides to encourage adoption and usage within the MeTTa community. - Exploring and documenting potential future directions for the project including additional algorithms and advanced clustering applications.
A finalized polished toolkit with ample resources for user engagement and support accompanied by recommendations for future work in clustering within MeTTa.
$2,500 USD
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Reviews & Ratings
Overall
4.0
3.7
3.7
New reviews and ratings are disabled for Awarded Projects
Overall
2.0
2.0
0.0
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: The proposal lacks details about their plan. For example, they don’t mention what difficulties they anticipate to face when making sure that their solution integrates seamlessly with MeTTa. They don’t mention if they will allow user-specific distance metrics. They don’t add any links to their claimed prior experience in clustering.
Overall
5.0
5.0
0.0
Ramin has contributed to OpenCog Classic in the past and is a solid researcher and developer. This is a relatively simple task which he is clearly very capable of doing, and he's fleshed it out competently in the proposal.
Overall
5.0
4.0
0.0
A solid and straightforward approach focusing on four important clustering algorithms. Well structured proposal focusing on learning MeTTa and communicating with the MeTTa community. Would have been good to discuss evaluation metrics used.
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