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Learning Dynamic Models

$95,100.00 Requested
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Problem:

Dynamic models are essential for trading, weather, video, and many other applications, but do not yet exist on key marketplaces such as SingularityNET.

挑戦: Dapps, Products & Integrations
not approved impact proposal Requested 1.211% of the fund.
コミュニティ・アドバイザー・レビュー:
4.58 (12)
Yes Votes:
₳ 23,510,793
No Votes:
₳ 35,668,796
Unique Wallets:
202

エクスペリエンス

Photrek is an engineering services firm specializing in developing Machine Intelligence for Complex Systems. Photrek’s current projects include designing low-cost environmental sensors, leading peer-reviewed publications, developing risk-aware algorithms, and DAO governance.

ソリューション

Photrek will upgrade the Coupled VAE (CVAE) algorithm it is developing for the SingularityNETs (SNET) AI Marketplace to incorporate a dynamic model. CVAE is a risk-aware learning method.

  • Video cover image

[IMPACT] Please describe your proposed solution.

Photrek proposes to host a Dynamic Coupled Variational Autoencoder (DC-VAE) algorithm on the SingularityNet Marketplace [1]. A Variational Autoencoder [2,3] merges the capabilities of deep learning and probabilistic programming. The algorithm provides a method to learn sophisticated models while retaining enough simplicity to enable interpretable manipulation of the model. Photrek invented a risk-aware process called Nonlinear Statistical Coupling [4] which it has applied to the VAE algorithm. As reported in the journal Entropy [5], this innovation significantly improves the robustness of the model and the data generated using the model. Photrek is proposing to integrate its newest design, incorporating dynamic models, onto the SNET marketplace.

This project has a substantial pedigree, originating from 20 years of foundational research on the application of complex systems theory to decision-making by Dr. Nelson and Dr. Thistleton [6]. That research includes a variety of collaborations, including the most recent effort to develop an open-source community called Machine Intelligence for Complex Systems.

Three funded projects have provided the resources to enable a successful development and integration of the DC-VAE algorithm. The Catalyst Fund 7 project “Forecasting Cardano Native Tokens” [7] provided the opportunity to demonstrate Photrek’s risk-aware algorithms to the SNET community. From those conversations, two SNET Deep Fund 1 projects were awarded. Photrek is currently working on hosting a) a risk-aware assessment of machine learning algorithms [8] that output forecasted probabilities and b) a risk-aware data generator [9] that uses the CVAE to ensure the generation of data with robust properties. The third phase of the project, which we are requesting Catalyst funds to complete, is to host a Dynamic CVAE. The DC-VAE algorithm will enable the generation of time-series simulations that can control for the degree of risk awareness desired by the user.

The CVAE algorithm [10], which Photrek is currently preparing for the SNET marketplace, was developed by the open-source community Machine Intelligence for Complex Systems (MICS). CVAE utilizes the Google TensorFlow library [11]. The risk-aware innovations are based on a Nonlinear Statistical Coupling python library [12] invented by Dr. Nelson and developed by MICS. The design work for the DC-VAE is funded by a SingularityNet grant. This Catalyst grant will fund the development and integration of the algorithm. As part of the effort, Photrek will explore the possibility of migrating the architecture to the PyTorch [13] and NeuroProphet [14] libraries.

[1] SingularityNet Marketplace, <https://beta.singularitynet.io/>.

[2] D.P. Kingma, M. Welling, Auto-Encoding Variational Bayes, in: Int. Conf. Learn. Represent. (ICLR), ArXiv1312.6114v10, 2014: https://arxiv.org/pdf/1312.6114.pdf .

[3] D.P. Kingma, M. Welling, An introduction to variational autoencoders, Found. Trends Mach. Learn. 12 (2019) 307–392. <https://doi.org/10.1561/2200000056>.

[4] K.P. Nelson, S. Umarov, Nonlinear statistical coupling, Phys. A Stat. Mech. Its Appl. 389 (2010) 2157–2163. <https://doi.org/10.1016/J.PHYSA.2010.01.044>.

[5] S. Cao, J. Li, K.P. Nelson, M.A. Kon, Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder, Entropy 2022, Vol. 24, Page 423. 24 (2022) 423. <https://doi.org/10.3390/E24030423>.

[6] W.J. Thistleton, J.A. Marsh, K. Nelson, C. Tsallis, Generalized Box-Müller method for generating q-Gaussian random deviates, IEEE Trans. Inf. Theory. 53 (2007) 4805–4809, <https://ieeexplore.ieee.org/abstract/document/4385787>.

[7] B. Zubillaga and K. Nelson, “Forecasting Cardano Native Tokens”, Cardano Catalyst Proposal, <https://cardano.ideascale.com/c/idea/383178>.

[8] K. Nelson and W. Thistleton, SingularityNet DF1 Risk-Aware Assessments for AI Applications, SingularityNet Deep Fund Proposal, <https://proposals.deepfunding.ai/graduated/accepted/7f31c306-2605-4f73-9a49-8e2793a75eec>

[9] K. Nelson and W. Thistleton, SingularityNet DF 1 Risk-Aware Data Generator for SingularityNet Applications, SingularityNet Deep Fund Proposal, <https://proposals.deepfunding.ai/graduated/accepted/5860fb2a-57c5-4230-9638-9284299e12db>

[10] K. Chen, J. Clements, D. Svoboda, X.Y. Hong, C. Wloka, W. Thistleton, K. Nelson, Photrek/Coupled-VAE, (2021). <https://github.com/Photrek/Coupled-VAE>.

[11] TensorFlow, <https://www.tensorflow.org/>.

[12] J. Clements, D. Svoboda, X.Y. Hong, W. Thistleton, K. Nelson, Photrek/Nonlinear-Statistical-Coupling, (2021), <https://github.com/Photrek/Nonlinear-Statistical-Coupling>.

[13] PyTorch, <https://pytorch.org/>.

[14] NeuralProphet, <https://neuralprophet.com/html/index.html>.

[IMPACT] Please describe how your proposed solution will address the Challenge that you have submitted it in.

As the SingularityNet Marketplace migrates to Cardano, it's important to have foundational Machine Intelligence applications that will attract a high volume of API calls. Each API call will stimulate AGIX transactions. While the marketplace is currently on the ERC20 network, the AGIX coin is now a Cardano native token and the marketplace is being transitioned to the Cardano network. Photrek is seeking to establish a fundamental capability for a variety of AI applications. Deep Learning has contributed to world-record performance in classification and generation tasks, while Probabilistic Programming has established rigorous methods for constructing interpretable models. The combination, Deep Probabilistic Programming, is a foundational method to learn interpretable models. Photrek adds to this approach the ability to incorporate risk-awareness into the training of these models.

[IMPACT] What are the main risks that could prevent you from delivering the project successfully and please explain how you will mitigate each risk?

The main risks for the Learning Dynamic Models project are:

a) identifying potential customers for the algorithm

b) determining an effective design for the dynamic model

c) successfully completing the Python development

d) successfully integrating the algorithm into the SNET marketplace

Customer Development Risk - Photrek is engaged with three communities to mitigate the risk of customer engagement. Within the Catalyst community, Photrek has identified organizations such as Drip Dropz and Liqwid that are keenly interested in analytical forecasting for the purpose of mitigating decision risks. Within the SNET community, Photrek has had lengthy discussions with three potential customers. These include SNET itself which is engaged in developing models of environmental sustainability; Rejuve, a spin-off developing models of longevity based on health monitoring; and SingularityDAO, which is focused on algorithmic financial trading. Finally, Photrek has had extensive conversations with financial institutions impacted by extreme weather events. These include Insurance brokers, commodities traders, and energy suppliers.

Design Risk - Photrek is mitigating the algorithmic design risk in two ways. 1) Its SNET DF1 contract includes a component to complete the DC-VAE design, so we anticipate having extensive analysis of the design completed, and 2) Photrek coordinates an open-source collaboration called Machine Intelligence of Complex Systems. This community includes academic scientists investigating the best approaches to learning dynamic models.

Code Development Risk - Like the design risk, Photrek will manage the code development risk in two manners. Photrek employs an exceptional team of Python developers who have contributed to a variety of machine learning projects. This team will be building from the successful architecture of the CVAE using TensorFlow, which outside investigators have already begun testing [1]. Furthermore, Photrek continues to develop alternative approaches. For instance, on its Catalyst Fund 7 project, we are investigating the use of NueroProphet and the PyTorch platforms.

Integration Risk - Photrek plans to mitigate the risk of successfully integrating the algorithm into the SingularityNet marketplace prior to the start of this project. Photrek has already met with the SNET marketplace engineering team. Photrek has funding to integrate two other algorithms onto the marketplace which will give the team the experience needed to integrate the DC-VAE algorithm.

[1] F. Wu, Group Fairness for Learning Representation on Coupled Variational Autoencoder, https://windhaunting.github.io/assets/docs/COMPSCI689_project_FubaoWu.pdf.

[FEASIBILITY] Please provide a detailed plan, including timeline and key milestones for delivering your proposal.

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[FEASIBILITY] Please provide a detailed budget breakdown.

The budget is based on the principals Nelson and Thistleton working with a data scientist and web developer. The cloud computing services necessary for customers to process calls on the SingularityNet marketplace is estimated as 25% of the project costs.

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[FEASIBILITY] Please provide details of the people who will work on the project.

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Project Roles:

  • Principal Investigator
  • Customer Discovery
  • Dynamic Model Design

Nelson is an innovative leader in the research, development, and deployment of systems for complex decision-making. Proven record of creative research, team building, and customer-focused development spanning cyber-security algorithms, multi-sensor systems, machine intelligence algorithms, and decentralized governance. Co-inventor of the Coupled Variational Autoencoder designed to improve the learning of robust, accurate models. Served on Cardano Catalyst Circle, a problem-sensing team guiding the development of one of the world’s largest DAOs.

<https://www.linkedin.com/in/kenric-nelson-ph-d-7495b77>

Image File

William Thistleton

Assoc. Professor

SUNY Polytechnic Institute

Project Roles:

  • Co-Principal Investigator
  • Dynamic Model Scientist
  • DC-VAE Developer

Associate Professor of Mathematics teaching classes in analysis, probability, statistics, design of experiments, and data science. Returned Peace Corps Volunteer. SUNY Online Teaching Ambassador. Research and consulting in Machine Learning, Quantum Annealing, and Data Science. Develops and delivers workshops for teachers, employees, and students.

<https://www.linkedin.com/in/william-thistleton-874b8010>

The project will employ a data scientist and a web developer. Community members with experience developing machine learning algorithms and/or integrating applications into web marketplace services will be invited to apply.

[FEASIBILITY] If you are funded, will you return to Catalyst in a later round for further funding? Please explain why / why not.

Photrek is seeking to develop a commercial service of innovative machine learning algorithms on the SingularityNet Marketplace. We anticipate 2-3 more Catalyst rounds focused on SNET machine learning development to have a fully functioning service that will be self-sustaining.

[AUDITABILITY] Please describe what you will measure to track your project's progress, and how will you measure these?

Customer engagement will be measured by:

a) the number of conversations regarding customer requirements

b) the number of customer-recommended applications

c) the number of customers using the application

Development progress will be measured by:

a) the number of dynamic models evaluated

b) the applicability of the model to the analysis of cryptocurrency markets

Integration progress will be measured by:

a) the functioning of the algorithm on the SingularityNet marketplace

b) the number of customer API calls

[AUDITABILITY] What does success for this project look like?

Successful completion of the project will include:

a) development of novel insights from potential customers regarding the use cases for learning dynamical models;

b) designing a dynamical model that cryptocurrency analysts can use to generate datasets and predict trends; and

c) initial set of customers who provide a trial exploration using the SingularityNet marketplace.

[AUDITABILITY] Please provide information on whether this proposal is a continuation of a previously funded project in Catalyst or an entirely new one.

The Learning Dynamic Models project will build from the following Catalyst and SingularityNet proposals.

Catalyst F7 Forecasting Cardano Native Tokens, which is part of the AI &amp; SingularityNet challenge. This project will be completed in July 2022. The project was used to introduce the concepts of risk-aware machine learning to the SingularityNet community.

SingularityNet DF1 Risk-Aware Assessments for AI Applications - This project will develop a SingularityNet marketplace application that provides an assessment of risk impacts on the performance of a machine learning algorithm’s probabilistic forecasts.

SingularityNet DF 1 Risk-Aware Data Generator for SingularityNet Applications - This project will develop a SingularityNet marketplace application that generates static datasets such as images that are robust against outlier risks.

Sustainable Development Goals (SDG) Rating

8 - Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all

9 - Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation

コミュニティ・アドバイザー・レビュー

Impact / Alignment

4.5 / 5
4 レビュー

Does the proposal effectively addresses the challenge?

コミュニティーレビュー (4)

Commenter gravatar

This idea aims to upgrade the Coupled VAE (CVAE) algorithm to incorporate a dynamic model. I found it quite promising. However, the way this idea is presented is pretty hard to follow for me. The author gave a lot of information that could play as the foundation for this algorithm. But I expected to see more details about this algorithm itself such as how this would work, how to attract the target audience and the impact of it on Cardano . And also, this proposal should have included a picture of this project when going live. Those things would provide a better understanding of this project. One thing that I really appreicate in this proposal is about the risk. There are four risks metioned which seem very vital but there are also specific plans to mitigate them. The solution given gave me the confidence that those risks are solvable.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4079
Total QA Ratings
3
QA Rating Outcome
Good
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"From my limited understanding of math and statistics, the proposal is essentially a risk prediction algorithm in a decentralized AI system. So this proposal is suitable for the Dapps challenge. After watching the Youtube link (attached to the proposal), reading a bit about Dr. Nelson's research, and from my naive knowledge of dynamic systems and complexity, the simplest way I can think of to describe the proponent's technique is to place more weight on negative outliers. Although, I still can't say much about the technique in a scientifically rigorous way. But in my opinion, I like the idea of focusing on bad outcomes. Because it will hold you not falling into overconfidence (avoid bad but rare outcomes) which, to me, is why traditional predictions do not work. In this section, the proposer is introduced and clearly explains their solution for the forecast problem. Every statement has a scientific citation. And It also has some links to give more insight into the idea of the proposal. All these make the proposal very professional and convincing. And all 4 main risks and the solution for them are mentioned and provided in clear explanations for each one. Overall, the proponent did a good job in this part with clear information."

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4045
Total QA Ratings
5
QA Rating Outcome
Good
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Developing this proposal will be adding to an existing dApp and helping to develop future dApps who take advantage of the specific AI learning mechanism presented here. This proposal, if successful, will help to create a dramatic increase in API calls to the singularity net which is a part of the card on OWN Network. This AI algorithm would be a huge benefit to have hosted on cardano. It could help grow the network by providing a research-based AI tool that would have wide-ranging effects in the future. Much of this cutting-edge AI research has limited immediate benefit, but as it becomes more publicly available, and more people grow more aware of it, this becomes a huge and impactful resource to have in our ecosystem. While SingularityNet has not yet moved to Cardano, it is in the process of transitioning. This proposal will create a specific AI framework that would be available on the singularity net Marketplace. The team behind this has done the work to ensure that this proposal will have Maximum Impact.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_3016
Total QA Ratings
5
QA Rating Outcome
Good
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The proposal aims to upgrade the CVAE algorithm (risk aware learning method) in SingularityNet. The team has worked out very well why this service is an added value for the community. Thus, this proposal definitely fits the goals of the campaign. What I like is that the proposer has created a detailed video on the topic. Even though it is very long, as an interested person you get a very good understanding of the complexity and meaningfulness of this proposer. Based on the information given, I would like to see the project funded and expect a positive impact.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_2450
Total QA Ratings
4
QA Rating Outcome
Good
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Feasibility

4.5 / 5
4 レビュー

Given experience and plan presented is likely that this proposal will be implemented successfully?

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The proposer has well discribed the team member. We have their names, pictures, roles, backgrounds, previous experience and aslo the Linkedin profiles to get to know more. I think they would successfully complete their responsibility. It is said that the team would employ a data scientist a web developer. I hope that they will fit in well with others so what the project will be carefully handled. The plan is devided into 6 phases with specific deliverables and timeline but it would be better if it is more details of each phases. On the financial breakdown, it seems unclear to me whether those part are the staff's salary or they include anything esle. I think the author should have provided a more specific budget breakdown in order to give a better understanding of how the moeny would be spent.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4079
Total QA Ratings
3
QA Rating Outcome
Good
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"The project plan and budget breakdown are described very intuitively with a table. This table can help readers easily capture important information (Phase, Deliverable, Delivery month), and track progress and costs faster. The budget also has an additional explanation to better explain how the budget is calculated. All information about the experience of both proponents is provided fully and clearly. There is a background summary related to the project and there is also a Linkedin link attached if the reader wants to know more. In short, the proponents have mentioned all the needed information for this part."

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4045
Total QA Ratings
5
QA Rating Outcome
Good
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The feasibility of this proposal is rated as high with a score of 5. This is because the team is well qualified, capable and suited for this work. The budget makes sense and is detailed enough to assess if this is feasible. The breakdown of work is also detailed enough to have a solid idea whether or not this is feasible. The team has provided sufficient detail for assessment, and a sufficient basis in their impact solution for this proposal to be fully assessed. There are sufficient attachments and links to be able to learn more about this specific AI and there is sufficient research and background information on the proposer and their plans.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_3016
Total QA Ratings
5
QA Rating Outcome
Good
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On a positive note, the team has successfully won proposals several times and has already delivered the implementation. This proposal also describes in full depth the information related to the plans and requirements needed to achieve the set goals. but the details of every step are not entirely clear. The team behind the proposal is well versed in the field and has the necessary skills and capacity to execute the project. The budget is understandable and stated in hourly rates. However, with the budget amount, I would be even more interested to know what specific subtasks each stage includes. Only then is it possible to evaluate whether the budget is justified or not. It should be noted that the possibility of involving relevant partnerships and collaborations should be considered. In summary, this proposal is very well prepared and based on the team and the information provided, I believe it is highly likely to be successfully implemented.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_2450
Total QA Ratings
4
QA Rating Outcome
Good
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Auditability

4.8 / 5
4 レビュー

Does the proposal provides sufficient information to assess and audit progress and completion?

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This part of the proposal was well presented to me. The proposer provided KIPs that could be the guides to measure the project's progress. They are all well chosen, clear and measurable. The success picture seems pretty good and achievable to me. But there is one suggestion to give even more strength to this auditability which is including a specific number for a KIP such as "1000 customers in the first period of going live" or so on. It would help to easily imagine the picture.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4079
Total QA Ratings
3
QA Rating Outcome
Good
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"Proponents provide both qualitative (numbers on some aspects of customer behavior and the number of models evaluated) and quantitative (function and application of the algorithm) to track progress. of project. Both types of indexes are logical and project-related. With a successful picture, proponents put forward three main elements to describe the picture. It involves understanding the customer, the crypto-analytic model, and the ability to develop new clients). Overall, this section has been provided with all necessary and reasonable information by the proponents."

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_4045
Total QA Ratings
5
QA Rating Outcome
Good
人間性の確認

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Auditability suffers here just a little bit due to a small lack of detail. The Proposal is completely clear in terms of what they are attempting to accomplish. and there are metrics that can be measurable and will be useful for the Catalyst Community to ascertain the Project's progress. They will measure customer engagement, development progress, and integration. However There is not enough detail given as to how this team intends to be transparent to the community. Do they plan to promote or release progress reports through their social media channels? Will they provide monthly update reports for the community? Will they attend Town Halls? The answers to these questions would help provide a higher auditability score, but overall The proposal is clear and auditible.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_3016
Total QA Ratings
5
QA Rating Outcome
Good
人間性の確認

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The proposal contains relevant information about what to expect, about the goals set and the basis for monitoring and evaluating the progress and development of the project. Also mentioned is a Github source where the community can review and track the progress of the proposal. Overall, the team shows a well-done preparation for the plan of the project.

Assessment Quality Assurance

Assessment Quality Assurance is an offered role to veteran in the Cardano Project Catalyst Community. The purpose is to review PA assessments of proposals, providing a second layer of Quality Assurance.

Assessor ID
z_assessor_2450
Total QA Ratings
4
QA Rating Outcome
Good
人間性の確認

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EP1: 'd' Parameter
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