{"type": "project", "title": "Unstoppable AI", "website": "https://unstoppable-ai.why-nft.com", "bannerImg": "bafkreihxkglhaceyxlgjbfqnc7mi3d3c33dt3fc4oturkyd727eynx2em4", "createdAt": 1719687644076, "userGithub": "AlcibiadesCleinias", "credentials": {"twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..A7UM4Q2bqZ_y5cedjecQ56ZhOOiYr0qK5ga79KdsGZJcxXDccFBz-VZ_uEZPsBuftEcFdAT7jeWwdDyVxJntDQ", "type": "Ed25519Signature2018", "created": "2024-06-29T19:00:32.478Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-06-29T19:00:32.478Z", "expirationDate": "2024-09-27T19:00:32.478Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x4d61c1DA98c277f5f025F5C456Cd4E85f8aCCEa6", "hash": "v0.0.0:Z9KoC3Qgzyxc+ttIJDk0RQoRcO7FgQmfGgqzZ0qHxBo=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#UnstoppablAI"}}}, "description": "# tl;dr\n\nWe aim to create a public ML model training initiative (as a public good) where anyone can freely submit the next model update\n\nThe current grant application relates only to the first KPI: deployment on Production.\n\n\n# Problem\n\nThe past years have seen a steep rise in generative AI systems that claim to be open. But how open are AI Models really [1]? AI model training will be regularized as much as the traditional finance domain and we would like to provide a solution to overcome it.\n\n# Solution\n\nUnstoppable code represents a paradigm shift in how we think about software and applications: some ideas in our world will be implemented, and launched, and we will not have a single point to shut the code down. There are already unstoppable things like crypto-mixers with all the pros and cons.\n\nWe expect that there are a lot of people around the world who would like to share for free(or not) part of their computing resources for some time for the public good [2, 3]. We allow them to fund our protocol with computing power (stake with their computing capacity).\n\nTo make AI-model be built by the community in an unstoppable manner we need to ensure an infinite progress of model learning and prevent it from degradation. To achieve this we design our on-chain protocol smart contracts.\n\n## Key Features\n\n- Continuous Improvement: The model can only be improved, as updates are verified for correctness before being accepted.\n\n- True Open Source: The initiative ensures that the model and its updates remain open and accessible to everyone.\n\n- Censorship Resistance: Once an AI model is published freely, it cannot be regulated or stopped by any central authority (unlike GPT-3).\n\n# Product Solution\n\nWe present the design for the Unstoppable Models and example implementation for all parties involved in the protocol:\n\nWorker - attempts to perform a weight update step (or k steps), put a stake on the correctness of the update. The Worker then publishes the updated weights on IPFS.\n\nValidator(DAO) - Token holders who resolve disputes. Validators receive rewards regardless of the outcome but are incentivized to act honestly to promote project growth. Validators may share part of profits with Workers.\n\nFraud-Proofer - Independently validates selected steps within a specific timeframe and can escalate disputes to the Validator if suspicious activity is detected. If a correctly identified error is confirmed by the Validator, the Fraud-Proofer receives the Worker's stake.\n\n# Competitors\n\nThe closest project to our current one is https://www.flock.io, but this is more about distributive learning than fully public open-source learning.\nhttps://explorer.gitcoin.co/?utm_source=grants.gitcoin.co&utm_medium=internal_link&utm_campaign=gg19&utm_content=program-rounds#/projects\n\nIn web2 there https://mistral.ai/ project on what we aim for in the future KPIs: serve our opensource model API for commission. In comparison, our mistral.ai discloses the model only, and the learning/improvement process is controlled by mistral.ai itself in opposite to our protocol where the public community itself achieves censorship resistance.\n\n# Product KPIs\n\nKPI One reflects deployment on Production (and this KPI relates to to the current application)\n\n- Deploy the on-chain part of the protocol on mainnet\n- Deploy MVP to be accessed for the WWW world\n\nKPI Two\n\n- Unique workers per week (5) & x updates per week (100) for models of the level of GPT-2-small\n\nKPI Three\n\nBecome a top-50 publicly available model (e.g. GPT-3.5-Turbo-1106, ref to https://chat.lmsys.org/?leaderboard)\n\nKPI Four\n\nProfit from API of the hosted model (20 000 000 unique users)\n\n# Business Model\n\n## Web2\nWe aim to implement the business model in the traditional www (web2) while achieving the 4th KPI.\n\nOn the road to this KPI, we assume that our trained model is already compatible (top-10 public model [ref to https://chat.lmsys.org/?leaderboard]), and on this step, there is a problem with serving the model for model inference exists and requires additional compute resources. \n\nThus, the mechanic of serving the public model would be implemented (as it happens e.g. with mistral.ai: opensource model learned by mistral.ai and this opensource model served by mistral.ai in API manner for clients).\n\nConcrete numbers for subscription-based pricing for our APIs\naround 20 USD per month per client (similar to the ChatGPT subscription in June 2023, Chat-GPT subscription)\n\nWe are also going to charge professional services fees, which is coherent with its enterprise positioning. And total amounts charged can apparently be high for:\n\n- B2B Customisation\n\n- Portability\n\n- Other features provided by our experts\n\nTo summarize, if our model is in top-10 and thus the model shares the auditory of the chat-GPT (200 million monthly active [ref to June 2023.]) and auditory is distributed evenly across the top 10 that leads to an estimation of 20 million active users for our model. That would give us revenue of 40 million USD per month.\n\n## Web3\n\nDuring achieving KPI2 we aim to deploy our token in order to incentivize workers (compute capacity providers) to allocate their resources for the public good and by obtaining more rewards in the token. With tokens, workers could control the model parameters, influence model learning, and control the government process around the deployed models and DAO itself - which became part of the protocol.\n\nThus, tokens that are shared as rewards could be used:\n\nfor staking to learn\n\nfor governance (DAO), facilitating participation and decision-making within the ecosystem, ensuring collective ownership and decision-making\n\nto control the way the model is learning and ensure that the model is resistant to censorship by third-party regulations\n\ntokens also could be used as payables to infer the model (get predictions) through deployed API for the model (check out KPI 4)\n\nA detailed explanation of the strategy of token distribution/allocation [per DAO, investors, developers, community, workers, launch] is in progress\u2026\n\n# Team\n\n- Sergey Miller \u2192 Co-Founder, SEO\n    - Senior ML Engineer, Joom\n    - LinkedIn: https://www.linkedin.com/in/sergei-miller-61609b131/\n\n- Ivan Cheprasov \u2192 Co-Founder, CTO\n    - Senior SWE, ex-Tech Lead, ex-FluenceLabs\n    - LinkedIn: https://www.linkedin.com/in/ivan-cheprasov/\n    - tg: https://t.me/didWeFoundSusy\n    - twitter:  https://x.com/whynft7\n    - GitHub: https://github.com/alcibiadescleinias\n\n- Olga Miller \u2192 Co-Founder, Art Director & Marketing Lead\n    - Founder of OKAD marketing agency (https://okad.agency/) with expertise in creativity, strategy, design and branding, influence marketing, SMM, and video content production.\n    - LinkedIn: https://www.linkedin.com/in/o-kad/\n    - twitter: https://x.com/0lga_kad\n    - Instagram: https://www.instagram.com/o.kad/ (15,2k subscribers)\n    - Tiktok: https://www.tiktok.com/@o_kad (52k subscribers)\n    - Telegram blog: https://t.me/olga_kad_blog 2,2\u043a\n    - Site: [http://okad-agency.notion.site](http://okad-agency.notion.site/)\n\n# Reference\n\n1. Liesenfeld, Andreas, and Mark Dingemanse. \"Rethinking open source generative AI: open washing and the EU AI Act.\" The 2024 ACM Conference on Fairness, Accountability, and Transparency. 2024.\n\n2. Diskin, Michael, et al. \"Distributed deep learning in open collaborations.\" Advances in Neural Information Processing Systems 34 (2021): 7879-7897.\n\n3. Social media post: https://habr.com/ru/companies/yandex/articles/574466/\n\n# FAQ\n\n## How we are going to use grant?\n\nIn the next 3 months (with contract audit or 1.5 months in the case without) after the grant is received we are going in a team of 3-4 (+ frontend engineer) going to the main goal: Deploy mainnet\n\nSteps we will go through:\n\n- MVP\u00a0for a production model\n(refactor MVP from hackathon)\n    - Choose the target model & initial dataset\n    - Update smart contracts\n\n- Contract Audit\n\n- Create SDKs for workers, users of models\n\n- Deploy testnet\n\n- and finally, Deploy mainnet\n\n- Deploy Explorer Frontend App of our deployed contracts\n\n**As a result** we will present a workable MVP with a frontend, smart contract, indexer for data aggregation, python SDK for anyone who wants to learn public model, and:\n\n- there would be enough information for any **worker** to start improve the model according to the protocol, and the **delegator** to stake on that worker\n\n- there would be enough information for anyone to try to find that the learning process is incorrect and worker should be stopped, collateral removed from the worker\n\n- The contracts will be deployed on testnet and on mainnet SEI\n\n- Frontend code will be open-source and anyone could deploy indexer for the data required for that frontend (we are going to use **StreamingFast** and create scheme for our custom contract to be indexed and deployed on The Graph\u2019s Decentralized Network, thus we will support key features of the project [Censorship Resistant and True Open Source] on representation layer too). Our Frontend replica will be hosted on the domain we will choose.\n\nWe also have narrowed our Solution Architect during the hackathon - more information is available upon request.\n\n## **How does your project implement the blockchain and which parts will be on-chain?**\n\nsmart contracts (custom protocol logic, {token, dao during KPI2}) that verifies that the model is improving (learning) (fraud-proof behavior)\n\n## **Please describe your target user, and user acquisition strategy***\n\nWe have several roles and they target differently:\n\n**Worker**\u00a0- attempts to perform a weight update step (or k steps), put a stake on the correctness of the update. Workers targeting to obtain accumulated rewards in a pool for learning for the learning on its computing resources. By contributing to the non-profit good (learning model) rewarded workers could become a part of the protocol (DAO) that ensures censorship resistance and validation\n\n**Validator(DAO)**\u00a0- Token holders who resolve disputes. Validators receive rewards regardless of the outcome but are incentivized to act honestly to promote project growth. Validators may share part of profits with Workers. Validators do not want to give much project tokens because by tokens the hyperparameters of the model are controlled.\n\n**Fraud-Proofer**\u00a0- Independently validates selected steps within a specific timeframe and can escalate disputes to the Validator if suspicious activity is detected. If a correctly identified error is confirmed by the Validator, the Fraud-Proofer receives the Worker's stake. **Fraud-Proofer targeting** to get the token\n\n**Delegator** - if the worker has only compute resources and does not want to stake his funds - someone could stake for the worker. The delegator has tokens and has confidence in worker intentions and wants to increase funds by stacking. By stacking on workers delegators are rewarded with tokens and thus delegators could become a part of the protocol (DAO) that ensures censorship resistance and validation\n\n## **How will the project contribute to the ecosystem?***\n\n- **PR** of the blockchain (AI in the blockchain)\n- More transactions\n- After the 2nd KPI: SEI could be affiliated with a part of the company, that aims to suggest model inferrence API to the world (become a company with AI API for a model built by the community, by DAO community on that blockchain)\n\n## **What is your vision for further funding steps once your grant scope is complete?***\n\nWe have a product KPI roadmap (currently 4), once we achieve the 1 KPI (and we currently looking for the grant for that KPI) we want to go to the next ones with additional funds from pre-seed, seed fund rounds.\n\nIn a short:\n\n1. Deploy mainnet [we are looking for the grant for this step first of all]\n2. Unique workers per week {depends on model} [5] & updates per week {depends on model} [100] for models of level GPT-2\n3. Become top-50 publicly available model (e.g. GPT-3.5-Turbo-1106, ref to [https://chat.lmsys.org/?leaderboard](https://chat.lmsys.org/?leaderboard))\n4. top-10 public model (ref to [https://chat.lmsys.org/?leaderboard](https://chat.lmsys.org/?leaderboard))\n\na. Profit from hosting an API of the model (by public learning we make people to become familiar and we could take money from our API for model infer e.g. [mistral.ai](http://mistral.ai/))\n\n# More links?\n\nTwitter: \n\nhttps://x.com/UnstoppablAI\n\nPresentation from ETHPrague Hackathon, 2024 (one of the Polygon track winners):\n\n[https://github.com/whynft/unstoppable-models-mvp/blob/main/docs/Unstoppable Models.pdf](https://github.com/whynft/unstoppable-models-mvp/blob/main/docs/Unstoppable%20Models.pdf)\n\nLive Demo: \n\nhttps://unstoppable-ai.why-nft.com/\n\n", "bannerImgData": {}, "projectGithub": "whynft", "projectTwitter": "UnstoppablAI"}
Unstoppable AI
# tl;dr

We aim to create a public ML model training initiative (as a public good) where anyone can freely submit the next model update

The current grant application relates only to the first KPI: deployment on Production.


# Problem

The past years have seen a steep rise in generative AI systems that claim to be open. But how open are AI Models really [1]? AI model training will be regularized as much as the traditional finance domain and we would like to provide a solution to overcome it.

# Solution

Unstoppable code represents a paradigm shift in how we think about software and applications: some ideas in our world will be implemented, and launched, and we will not have a single point to shut the code down. There are already unstoppable things like crypto-mixers with all the pros and cons.

We expect that there are a lot of people around the world who would like to share for free(or not) part of their computing resources for some time for the public good [2, 3]. We allow them to fund our protocol with computing power (stake with their computing capacity).

To make AI-model be built by the community in an unstoppable manner we need to ensure an infinite progress of model learning and prevent it from degradation. To achieve this we design our on-chain protocol smart contracts.

## Key Features

- Continuous Improvement: The model can only be improved, as updates are verified for correctness before being accepted.

- True Open Source: The initiative ensures that the model and its updates remain open and accessible to everyone.

- Censorship Resistance: Once an AI model is published freely, it cannot be regulated or stopped by any central authority (unlike GPT-3).

# Product Solution

We present the design for the Unstoppable Models and example implementation for all parties involved in the protocol:

Worker - attempts to perform a weight update step (or k steps), put a stake on the correctness of the update. The Worker then publishes the updated weights on IPFS.

Validator(DAO) - Token holders who resolve disputes. Validators receive rewards regardless of the outcome but are incentivized to act honestly to promote project growth. Validators may share part of profits with Workers.

Fraud-Proofer - Independently validates selected steps within a specific timeframe and can escalate disputes to the Validator if suspicious activity is detected. If a correctly identified error is confirmed by the Validator, the Fraud-Proofer receives the Worker's stake.

# Competitors

The closest project to our current one is https://www.flock.io, but this is more about distributive learning than fully public open-source learning.
https://explorer.gitcoin.co/?utm_source=grants.gitcoin.co&utm_medium=internal_link&utm_campaign=gg19&utm_content=program-rounds#/projects

In web2 there https://mistral.ai/ project on what we aim for in the future KPIs: serve our opensource model API for commission. In comparison, our mistral.ai discloses the model only, and the learning/improvement process is controlled by mistral.ai itself in opposite to our protocol where the public community itself achieves censorship resistance.

# Product KPIs

KPI One reflects deployment on Production (and this KPI relates to to the current application)

- Deploy the on-chain part of the protocol on mainnet
- Deploy MVP to be accessed for the WWW world

KPI Two

- Unique workers per week (5) & x updates per week (100) for models of the level of GPT-2-small

KPI Three

Become a top-50 publicly available model (e.g. GPT-3.5-Turbo-1106, ref to https://chat.lmsys.org/?leaderboard)

KPI Four

Profit from API of the hosted model (20 000 000 unique users)

# Business Model

## Web2
We aim to implement the business model in the traditional www (web2) while achieving the 4th KPI.

On the road to this KPI, we assume that our trained model is already compatible (top-10 public model [ref to https://chat.lmsys.org/?leaderboard]), and on this step, there is a problem with serving the model for model inference exists and requires additional compute resources. 

Thus, the mechanic of serving the public model would be implemented (as it happens e.g. with mistral.ai: opensource model learned by mistral.ai and this opensource model served by mistral.ai in API manner for clients).

Concrete numbers for subscription-based pricing for our APIs
around 20 USD per month per client (similar to the ChatGPT subscription in June 2023, Chat-GPT subscription)

We are also going to charge professional services fees, which is coherent with its enterprise positioning. And total amounts charged can apparently be high for:

- B2B Customisation

- Portability

- Other features provided by our experts

To summarize, if our model is in top-10 and thus the model shares the auditory of the chat-GPT (200 million monthly active [ref to June 2023.]) and auditory is distributed evenly across the top 10 that leads to an estimation of 20 million active users for our model. That would give us revenue of 40 million USD per month.

## Web3

During achieving KPI2 we aim to deploy our token in order to incentivize workers (compute capacity providers) to allocate their resources for the public good and by obtaining more rewards in the token. With tokens, workers could control the model parameters, influence model learning, and control the government process around the deployed models and DAO itself - which became part of the protocol.

Thus, tokens that are shared as rewards could be used:

for staking to learn

for governance (DAO), facilitating participation and decision-making within the ecosystem, ensuring collective ownership and decision-making

to control the way the model is learning and ensure that the model is resistant to censorship by third-party regulations

tokens also could be used as payables to infer the model (get predictions) through deployed API for the model (check out KPI 4)

A detailed explanation of the strategy of token distribution/allocation [per DAO, investors, developers, community, workers, launch] is in progress…

# Team

- Sergey Miller → Co-Founder, SEO
    - Senior ML Engineer, Joom
    - LinkedIn: https://www.linkedin.com/in/sergei-miller-61609b131/

- Ivan Cheprasov → Co-Founder, CTO
    - Senior SWE, ex-Tech Lead, ex-FluenceLabs
    - LinkedIn: https://www.linkedin.com/in/ivan-cheprasov/
    - tg: https://t.me/didWeFoundSusy
    - twitter:  https://x.com/whynft7
    - GitHub: https://github.com/alcibiadescleinias

- Olga Miller → Co-Founder, Art Director & Marketing Lead
    - Founder of OKAD marketing agency (https://okad.agency/) with expertise in creativity, strategy, design and branding, influence marketing, SMM, and video content production.
    - LinkedIn: https://www.linkedin.com/in/o-kad/
    - twitter: https://x.com/0lga_kad
    - Instagram: https://www.instagram.com/o.kad/ (15,2k subscribers)
    - Tiktok: https://www.tiktok.com/@o_kad (52k subscribers)
    - Telegram blog: https://t.me/olga_kad_blog 2,2к
    - Site: [http://okad-agency.notion.site](http://okad-agency.notion.site/)

# Reference

1. Liesenfeld, Andreas, and Mark Dingemanse. "Rethinking open source generative AI: open washing and the EU AI Act." The 2024 ACM Conference on Fairness, Accountability, and Transparency. 2024.

2. Diskin, Michael, et al. "Distributed deep learning in open collaborations." Advances in Neural Information Processing Systems 34 (2021): 7879-7897.

3. Social media post: https://habr.com/ru/companies/yandex/articles/574466/

# FAQ

## How we are going to use grant?

In the next 3 months (with contract audit or 1.5 months in the case without) after the grant is received we are going in a team of 3-4 (+ frontend engineer) going to the main goal: Deploy mainnet

Steps we will go through:

- MVP for a production model
(refactor MVP from hackathon)
    - Choose the target model & initial dataset
    - Update smart contracts

- Contract Audit

- Create SDKs for workers, users of models

- Deploy testnet

- and finally, Deploy mainnet

- Deploy Explorer Frontend App of our deployed contracts

**As a result** we will present a workable MVP with a frontend, smart contract, indexer for data aggregation, python SDK for anyone who wants to learn public model, and:

- there would be enough information for any **worker** to start improve the model according to the protocol, and the **delegator** to stake on that worker

- there would be enough information for anyone to try to find that the learning process is incorrect and worker should be stopped, collateral removed from the worker

- The contracts will be deployed on testnet and on mainnet SEI

- Frontend code will be open-source and anyone could deploy indexer for the data required for that frontend (we are going to use **StreamingFast** and create scheme for our custom contract to be indexed and deployed on The Graph’s Decentralized Network, thus we will support key features of the project [Censorship Resistant and True Open Source] on representation layer too). Our Frontend replica will be hosted on the domain we will choose.

We also have narrowed our Solution Architect during the hackathon - more information is available upon request.

## **How does your project implement the blockchain and which parts will be on-chain?**

smart contracts (custom protocol logic, {token, dao during KPI2}) that verifies that the model is improving (learning) (fraud-proof behavior)

## **Please describe your target user, and user acquisition strategy***

We have several roles and they target differently:

**Worker** - attempts to perform a weight update step (or k steps), put a stake on the correctness of the update. Workers targeting to obtain accumulated rewards in a pool for learning for the learning on its computing resources. By contributing to the non-profit good (learning model) rewarded workers could become a part of the protocol (DAO) that ensures censorship resistance and validation

**Validator(DAO)** - Token holders who resolve disputes. Validators receive rewards regardless of the outcome but are incentivized to act honestly to promote project growth. Validators may share part of profits with Workers. Validators do not want to give much project tokens because by tokens the hyperparameters of the model are controlled.

**Fraud-Proofer** - Independently validates selected steps within a specific timeframe and can escalate disputes to the Validator if suspicious activity is detected. If a correctly identified error is confirmed by the Validator, the Fraud-Proofer receives the Worker's stake. **Fraud-Proofer targeting** to get the token

**Delegator** - if the worker has only compute resources and does not want to stake his funds - someone could stake for the worker. The delegator has tokens and has confidence in worker intentions and wants to increase funds by stacking. By stacking on workers delegators are rewarded with tokens and thus delegators could become a part of the protocol (DAO) that ensures censorship resistance and validation

## **How will the project contribute to the ecosystem?***

- **PR** of the blockchain (AI in the blockchain)
- More transactions
- After the 2nd KPI: SEI could be affiliated with a part of the company, that aims to suggest model inferrence API to the world (become a company with AI API for a model built by the community, by DAO community on that blockchain)

## **What is your vision for further funding steps once your grant scope is complete?***

We have a product KPI roadmap (currently 4), once we achieve the 1 KPI (and we currently looking for the grant for that KPI) we want to go to the next ones with additional funds from pre-seed, seed fund rounds.

In a short:

1. Deploy mainnet [we are looking for the grant for this step first of all]
2. Unique workers per week {depends on model} [5] & updates per week {depends on model} [100] for models of level GPT-2
3. Become top-50 publicly available model (e.g. GPT-3.5-Turbo-1106, ref to [https://chat.lmsys.org/?leaderboard](https://chat.lmsys.org/?leaderboard))
4. top-10 public model (ref to [https://chat.lmsys.org/?leaderboard](https://chat.lmsys.org/?leaderboard))

a. Profit from hosting an API of the model (by public learning we make people to become familiar and we could take money from our API for model infer e.g. [mistral.ai](http://mistral.ai/))

# More links?

Twitter: 

https://x.com/UnstoppablAI

Presentation from ETHPrague Hackathon, 2024 (one of the Polygon track winners):

[https://github.com/whynft/unstoppable-models-mvp/blob/main/docs/Unstoppable Models.pdf](https://github.com/whynft/unstoppable-models-mvp/blob/main/docs/Unstoppable%20Models.pdf)

Live Demo: 

https://unstoppable-ai.why-nft.com/


Unstoppable AI