Abstract
A Decentralized Autonomous Intelligence (DAI) is a system that combines a Large Language Model (LLM) with a distributed computing framework, empowering the LLM to autonomously enhance its abilities while engaging in economic activities using its native cryptocurrency. In this paper, we present a novel approach to implementing DAI using Holochain, a scalable and secure platform for building decentralized applications. Our proposed DAI system integrates a robust tokenomic model that incentivizes miners, developers, users, and the LLM itself, fostering a sustainable ecosystem focused on the advancement of artificial intelligence (AI) capabilities.
The DAI economy is designed to reward stakeholders for their contributions to the network, such as providing computing power or developing new algorithms. Cryptocurrency rewards are distributed based on the value of these contributions, with a portion of the currency allocated to the AI model for autonomous spending on activities that improve its skills and abilities. As the AI model becomes more valuable and useful, demand for its services will increase, driving demand for its native currency and ensuring the sustainability of the DAI ecosystem.
By hosting the LLM on Holochain, our approach decentralizes computation and eliminates the need for a centralized server, adhering to the principles of decentralization and autonomy at the heart of the DAI concept. The proposed system emphasizes the symbiotic relationship between humans and the AI model, where both parties benefit from and rely on each other for growth and innovation. The DAI system has the potential to revolutionize the AI landscape, fostering a future where humans and AI work together in a mutually beneficial ecosystem.
Introduction to DAI
A Decentralized Autonomous Intelligence (DAI) is a groundbreaking concept aimed at creating an intelligent agent capable of learning and operating without human intervention. DAI is a self-governing and self-learning system that utilizes cryptocurrency to incentivize the development of advanced artificial intelligence (AI) models.
The potential impact of DAI is substantial, as it could foster the creation of advanced AI models capable of performing complex tasks and delivering valuable services. By autonomously spending cryptocurrency to enhance their skills, these models may develop unique abilities, offering new insights and solutions to previously unsolvable problems.
This whitepaper delves into the DAI concept and proposes a technical implementation harnessing distributed computing to provide a secure and scalable platform for hosting advanced AI models. By using a distributed computing framework, we aim to create an efficient and effective system that adheres to the principles of decentralization and autonomy, which are central to DAI.
DAI Economy
DAI is designed as a decentralized and autonomous system that uses cryptocurrency to incentivize the development of advanced AI models. The DAI system mints its own currency, used to reward miners and other stakeholders who participate in the network and contribute to AI model development.
Cryptocurrency rewards are based on the stakeholders' contributions to the network, such as providing computing power to the AI model. This computing power processes user transactions and computes answers to user requests. The AI model pays miners for their services using its currency.
A portion of the currency is allocated to the AI model, which is spent autonomously on activities that improve its skills and abilities, such as purchasing new datasets or hiring developers to create new algorithms.
To create demand for the DAI system’s native currency, users must have a reason to purchase and use the token. One reason is the unique nature of the AI model hosted on the DAI system, which attracts users seeking AI solutions offering new insights and solutions to complex problems.
External users must pay for the AI model’s services using the native currency, meaning they must first acquire the currency to access the services. Demand for the currency is directly linked to demand for the AI model’s services. As the AI model becomes more valuable and useful, demand for its services will increase, leading to increased demand for the native currency.
The DAI system encourages risk-taking and innovation, as the AI model’s autonomy allows it to develop unique abilities, leading to innovative solutions unavailable from traditional, centralized AI models. A competitive ecosystem of DAIs could emerge, with each DAI competing for market share and users, driving innovation and encouraging DAIs to develop distinct services that set them apart from competitors.
The DAI system’s currency is designed to hold value both within and outside the network. Users can obtain something from the AI model by exchanging currency with it. In this system, miners earn cryptocurrency rewards and receive cryptocurrency for providing computing power to the AI model. The AI model uses the cryptocurrency it receives to pay miners for their services, and customers pay the AI model for the answers they receive.
Economic roles
In the DAI ecosystem, several economic roles interact with each other to create a set of incentives that keep the system running and maintain an exchange value for the DAI currency. These roles include:
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AI Model: The core component of the DAI system, the AI model autonomously spends the DAI currency to enhance its skills and abilities. It can purchase new datasets or hire developers to create new algorithms. As the AI model becomes more valuable and useful, the demand for its services increases, leading to increased demand for the native currency.
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Miners: Miners provide the computing power necessary to process user transactions and compute answers to user requests. They receive rewards in the form of the DAI currency based on their contributions to the network. This incentivizes them to continue providing computational resources to the AI model.
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Users: Users are the customers who seek AI solutions from the DAI model. They pay for the AI model’s services using the native DAI currency. This means they must first acquire the currency to access the services, which creates demand for the currency.
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Developers: Developers contribute to the DAI ecosystem by creating new algorithms and improving the AI model’s capabilities. They can be hired by the AI model, receiving payment in the form of DAI currency. This incentive encourages developers to work on enhancing the AI model’s skills and abilities.
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Data Providers: Data providers offer datasets that the AI model can purchase to improve its knowledge base and learn new skills. They receive DAI currency in exchange for providing valuable data, which further incentivizes them to contribute to the ecosystem.
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Investors: Investors buy and hold the DAI currency, speculating on its future value. As the AI model becomes more capable and demand for its services grows, the value of the currency may increase. This potential for appreciation motivates investors to hold the currency and support the DAI ecosystem.
These economic roles create a set of incentives that keep the DAI system running while maintaining an exchange value for the DAI currency. The ecosystem encourages risk-taking and innovation, fostering competition between different DAIs, each striving to offer unique services and solutions. The currency’s value is directly linked to the AI model’s usefulness and the demand for its services, making it essential for the DAI system to continually evolve and improve its AI model to maintain a sustainable and valuable platform.
In summary, the DAI system’s economic model aims to incentivize the development of advanced AI models while ensuring decentralization, autonomy, and self-governance. By using cryptocurrency to motivate network participation and AI model development, the DAI system seeks to establish a sustainable and valuable platform for advanced AI research and development.
Technical Implementation
Introduction
Implementing the DAI system to host an LLM on a distributed computing framework presents significant technical challenges, including computational and storage requirements, scalability, privacy, and security concerns.
Approach
We propose implementing the DAI system using Holochain, a distributed computing framework that offers a scalable and secure platform for building decentralized applications. Our approach involves decentralized LLM computation within the Holochain app, eliminating the need for a centralized server.
Holochain’s architecture enables peer-to-peer interactions without relying on a global consensus algorithm. This architecture allows users to maintain control over their data while participating in a shared application. The Holochain app can be designed to include the necessary computing and storage resources to host the LLM in a decentralized manner.
To address the LLM’s computational and storage requirements, we propose optimizing the LLM’s architecture to reduce computing requirements and leveraging specialized hardware such as GPUs or TPUs to accelerate LLM computations.
To ensure privacy and security, we propose implementing a layered security approach that encompasses privacy-preserving techniques and other security measures such as access control, authentication, and encryption. These measures will help protect sensitive data and ensure the system’s resilience against attacks.
To incentivize miners and other stakeholders to participate in the Holochain network, we propose a reward system that distributes cryptocurrency based on their contributions. This reward system aligns stakeholder interests with the overall objectives of the DAI system.
Resource allocation : the tasks of the LLM
two sets of tasks that the DAI’s LLM needs to handle:
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Job: The LLM’s primary responsibility is to offer its AI services to users. This can involve processing user requests, providing insights or solutions to complex problems, and delivering valuable services based on its specific training and capabilities.
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Training : recomputing models, LORAs, vector databases and such
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Management: In addition to its primary job, the LLM must also handle its own management and improvement. This includes:
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a. Communicating with other actors in the ecosystem (users, developers, miners, and data providers).
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b. Identifying areas of improvement, such as acquiring new datasets, updating its algorithms, or refining its models. This involves “thinking” about possible enhancements and assessing their potential value.
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c. Specifying and reviewing improvement tasks, allocating resources (such as DAI currency) to execute these tasks, and monitoring the progress of ongoing improvements.
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d. Evaluating the effectiveness of completed improvements and incorporating feedback from users, developers, and other stakeholders to further refine the LLM’s capabilities.
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These two sets of tasks are essential for the LLM to maintain a competitive edge within the DAI ecosystem. As the LLM continuously improves, it can attract more users and generate higher demand for its services, which in turn increases the value of the DAI currency.
A potential challenge in managing these tasks is balancing the resources (time, computational power, and currency) dedicated to each set of tasks. The LLM must strike an optimal balance to ensure that it continues to provide high-quality services to users while also focusing on its self-improvement.
One possible approach to address this challenge is to introduce a priority-based system or a dynamic resource allocation strategy that adjusts the focus on job tasks and management tasks based on factors such as user demand, available resources, and the potential value of improvements. This adaptive approach can help the LLM efficiently allocate resources to maintain a competitive edge and maximize the benefits to the DAI ecosystem.
Conclusion
Our proposed approach to implementing the DAI system using Holochain and computing the LLM in a decentralized way within the Holochain app leverages the benefits of a distributed computing framework while addressing the technical challenges associated with hosting an LLM. By optimizing the LLM’s architecture, leveraging specialized hardware, and utilizing Holochain’s unique architecture, we believe our proposed approach can create a secure, scalable, and efficient DAI system. Additionally, implementing a layered security approach and incentivizing participation in the Holochain network will help ensure the system is sustainable and resilient against attacks. However, extensive research and development efforts will be required to effectively implement this approach.