Introduction

The Devolved AI project is a fascinating initiative that aims to decentralize artificial intelligence (AI) and make it more accessible and equitable.

Through the use of decentralized networks, such as Bittensor, Devolved AI seeks to democratize access to computing power and machine learning (ML) models.

Devolved AI is a concept based on the distribution of AI across a decentralized network. It proposes a model where computing resources and machine learning models are spread across a global network of contributors.

Unlike traditional centralized models, where computing resources and data are concentrated in the hands of a few large companies, Devolved AI allows for broader distribution and collective participation.

Architecture and Operation

The operation of Devolved AI relies on networks like Bittensor, where various "miners" provide their computing resources.

These miners can be individuals or organizations contributing to the network's computing power. The AI models are hosted on this network and are accessible to all participants.

Everything is built on the Argochain, a Layer 1 at the core of Devolved AI, developed using Substrate technology, which acts as much more than just a framework. As a reminder, Substrate is the open-source framework written in the Rust language, developed by Parity Technologies, a company founded by the eminent Gavin Wood.

Devolved AI relies on a central system that coordinates a multitude of operations, from implementing the Proof of Value (PoV) protocol to facilitating governance decisions in a "democratic" manner.

The modular design of Substrate allows customization of blockchain architecture, adapting to new requirements and evolving as the Devolved AI platform grows. This makes it an ideal choice for constantly evolving technological needs.

Participants are rewarded for their contributions to the network with Argocoin tokens, creating an incentive system to maintain and grow the network.

Now, here is an overview of the project’s architecture:

Overall, Argochain is a balanced blend of technical sophistication and user-friendly design, maintaining transparency and security while offering the scalability needed for a growing AI ecosystem.

Proof of Value (PoV)
The PoV protocol in AI represents a dynamic system where rewards are not static but adapt fluidly based on the needs and contributions of the community.

This protocol uses complex algorithms to dynamically adjust the distribution of rewards, ensuring that every contribution to the AI ecosystem is appropriately valued.

PoV is designed to significantly enhance community engagement. It recognizes and rewards contributions in a way that aligns with the evolving needs of the ecosystem, encouraging active participation and continuous contributions from all community members.

Through this protocol, Devolved AI ensures that the value of each contribution is accurately reflected and rewarded, fostering a dynamic and engaged user base.

This PoV consensus combines the advantages of both PoW and PoS while avoiding the pitfalls of both.

Athena LLM
Yes, you read that right, we're talking about the Greek goddess of war. In any case, Devolved AI represents her this way:

More seriously, what are we talking about?

Athena is an LLM, which stands for "Large Language Model."

A large language model (or LLM) is a type of AI program capable of, among other things, recognizing and generating text. LLMs are trained on massive datasets.

Simply put, an LLM is a computer program that has received enough examples and substantial (and reliable) data to be able to recognize and interpret human language or other types of complex data. Many LLMs are trained on data collected from the internet, among other sources.

However, the quality of the samples affects the LLM's ability to learn natural language, so the developers of an LLM can use a better-structured dataset.

Some examples of LLMs
Examples of actual LLMs include:

• ChatGPT (OpenAI)

• Bard (Google), Llama (Meta)

• Bing Chat (Microsoft)

GitHub's Copilot is another example (though it applies to coding rather than natural human language).

LLMs can be trained for various tasks. One of their most well-known uses is in generative AI: they are capable of producing text in response to a prompt or question.

Any complex and large dataset can be used to train LLMs, including programming languages. Some LLMs can assist programmers in writing code. They can write functions on demand or complete a program based on code that has been given to them as a starting point. LLMs can also be used for:

• Sentiment analysis

• DNA research

• Customer service

• Chatbots

• Online search, and much more!

Use Case: Luna Chatbots

Rest assured, we’re not talking about LUNA Classic! Instead, this is a practical application of Devolved AI.

There are two operating modes in this tool:

• In "standard" mode, Luna’s first version functions as an advanced chatbot, providing users with intelligent and responsive interactions.

• The "reward" mode introduces an innovative approach to AI training and user engagement.

In this mode, users are presented with two different responses to their questions. By selecting the best response, users actively participate in the training process of our AI. This interaction not only improves Luna 1's learning abilities but also allows users to directly contribute to its evolution. As compensation for their valuable contributions, users are rewarded with Argocoins (AGC).

Beyond Luna version 1, Luna version 2 is expected to launch this year. Luna version 2 marks a bold entry into the competitive AI landscape, challenging established players like OpenAI and Anthropic. This version aims to use unique data from the community, reaching unparalleled levels of learning, personalization, and user engagement.

With Luna 2, Devolved AI aspires to redefine chatbot standards and establish itself as a key competitor in the industry.

Real-Life Use Cases

Today, the project targets real-world use cases. The idea is to use AI in medicine, education, research, real-world assets (RWA), content creation, and gaming. As seen previously with Athena, the impact of this project directly enhances analysis capabilities.

Here are some additional real-world applications of AI powered by Devolved AI:

Medicine
AI helps accelerate the discovery of new drugs and improves treatment personalization, reducing healthcare costs by streamlining clinical trials. It will also optimize diagnostic accuracy and predictive analysis for disease prevention, improving patient monitoring.

Education
Devolved AI aims to revolutionize (though it’s not the only one) the learning environment by providing AI-powered tools to enhance current teaching and learning methods. It will play a role in updating knowledge and curricula by analyzing educational data to refine learning outcomes and identify gaps. Language learning, supported by real-time translation and pronunciation tools, is expected to progress rapidly. Lastly, global collaboration and learning communities, mediated by AI platforms, will be strengthened through interactive and gamified learning environments.

For the curious, here are a few more application cases:

Research
AI accelerates the pace of discoveries across disciplines, combining large datasets, execution speed, and precision in research results. It also enhances interdisciplinary collaborations. Predictive modeling and simulations will be refined, aiding researchers in producing more valuable insights. Automated document analysis and data extraction (detecting correlation patterns and helping generate hypothesis tests) will also be a significant advantage.

RWA (Real-World Assets)
Devolved AI aims to assist and support investors in decision-making by providing valuable insights into outcomes, trends, or future behaviors with the help of AI.

Content Creation and Gaming
Content creators' intellectual property will be protected, opening new paths for creativity and collaboration between creators.

This is a project with growing potential, full of ambition. What’s interesting is its clear focus on the positive impacts in real-life scenarios, making it one of the first AI projects to emphasize tangible benefits for various stakeholders.

Advantages of Devolved AI
This project offers four key advantages: accessibility, security, resilience, and innovation.

By making AI models available on a decentralized network, Devolved AI allows people worldwide to access advanced AI tools without needing expensive infrastructure, democratizing access to AI. Decentralization enhances security and privacy by avoiding data centralization at a single vulnerable point. Lastly, a decentralized network is less prone to massive outages, increasing its robustness and resilience.

Opening access to AI models also encourages innovation and collaboration between researchers and developers globally.

Challenges of Devolved AI

Devolved AI will need to overcome several significant obstacles, organized around four main themes:

1. Governance Managing a decentralized network presents unique challenges in terms of governance and regulation. Establishing robust governance protocols is crucial to ensure that the network operates ethically, transparently, and effectively. This includes community decision-making, conflict management, and setting rules to protect users' interests.

2. Quality and Consistency of AI Models Ensuring the quality and consistency of AI models on a decentralized network is no easy task. Mechanisms must be put in place to guarantee that AI models remain reliable and accurate despite their distributed nature. Quality control, error management, and continuous improvement of models are essential to maintain user trust and ensure effective learning.

3. Incentive System Creating an effective incentive system that encourages the contribution of miners and participants is crucial for the health of the network. Such a system must be fair and transparent, rewarding contributions to the development and training of AI models proportionately to maintain engagement and activity within the community.

4. Interoperability Distributed AI must be able to work with other models and systems. Ensuring interoperability between different protocols, infrastructures, and blockchains is essential to promote collaboration and avoid technological silos. This will accelerate innovation while preventing incompatibilities between systems.

A Promising but Young Project Despite its great potential, Devolved AI is still a young project, with much to prove in terms of robustness and long-term viability. If it can successfully navigate these challenges and uphold its ambitions of decentralization and transparency, it could indeed compete with giants like Google and Microsoft. However, it remains to be seen whether it will be able to deliver on its promises in such a competitive sector.

It's an innovative and appealing project, but it will require vigilance regarding its evolution and future success.

Partnerships and Roadmap

Partnerships

The project is progressing well and is supported by several strategic partners, including nuco.cloud and Stratos:

• Stratos specializes in DePIN (Decentralized Physical Infrastructure Networks), aiming to offer fully decentralized infrastructures.

• Nuco.cloud focuses on cloud computing.

These partnerships complement Devolved AI's offerings across all technical components of AI, from machine learning to the infrastructure necessary for its growth. Both Stratos and nuco.cloud also have their tokens: STOS and NCDT, respectively.

Roadmap

Visualizing a roadmap and the associated milestones is always interesting, as it allows for a thorough analysis of the project’s logic and the potential adherence to timelines. Understanding the roadmap helps stakeholders assess the project's direction and anticipate future developments.

The project is ambitious, and the targets set can be coherent, even if such projects may accumulate delays. This is often a criticism of projects that do not adequately account for potential setbacks.

In any case, Devolved AI is focusing on the upgrades of their LLM, Athena, in the early phases of the project's development.

Tokenomics

The token is Argocoin (AGC). The maximum supply is listed as 200 million AGC tokens on CoinGecko; however, the website devolvedai.com mentions a maximum supply of 100 million.

Here are some elements regarding the tokenomics distribution as of now:

43%: Reserved for liquidity

2%: Reserved for treasury

2%: Reserved for operational expenses

21%: Reserved for PoS rewards

25%: Reserved for PoV rewards

7%: Reserved for the DAO

In my opinion, this is a solid tokenomics structure that shows the project’s commitment to its community.

No tokens have been allocated to the founder or the team, nor has there been any presale. This is referred to as a "Fair Launch." The founder purchased AGC at the same time as the entire community.

Additionally, to demonstrate his commitment to the project, the founder received 2.5 million AGC as a reward for his contributions but decided to burn these tokens (worth approximately $150,000), which is a sign of trust.

The AGC token serves multiple purposes: it acts as a governance token, a payment currency for AI usage, a reward for community contributions, and the currency for the Argochain blockchain to pay transaction fees.

Conclusion

The Devolved AI project represents an ambitious vision for the future of AI. By leveraging the power of decentralized networks, it promises to make AI more accessible, equitable, and resilient.

While the challenges are many, the potential rewards for society as a whole could be substantial.

As with any project of this nature within a new narrative, it's essential to remain vigilant and thoroughly research projects in advance.

Many projects of this kind will emerge soon, and Devolved AI will need to find the necessary resources and innovations to stand out from the competition. Its blockchain architecture is a significant advantage, as it is comprehensive, and there is a genuine search for added value within the ecosystem.

It's also important to keep in mind that competition is necessary for all projects to excel and drive innovation.

The AI market is vast, and there will inevitably be a multitude of players and offerings, which is quite healthy. However, always remain vigilant, and remember to DYOR (Do Your Own Research)!

One can only hope that this technology is not monopolized by a few major players like the GAFAM!