The integration of artificial intelligence and blockchain will obviously play a crucial role in shaping various industries.

Written by: Reflexivity Research

Translated by: Deep Tide TechFlow

Recently, the artificial intelligence industry has been making headlines for both good and bad reasons. While you may be familiar with the recent events at OpenAI and have explored the current level of artificial intelligence technology, you may not be aware of how artificial intelligence interacts with blockchain. In this week's report, we will introduce some applications that attempt to combine artificial intelligence and blockchain technology, as well as information about these applications and the future of the artificial intelligence industry in the coming years.

What is artificial intelligence? What is its relationship with cryptocurrency?

Before we delve into the details of the applications that combine AI and blockchain and the more technical content, let's first understand some basic knowledge about artificial intelligence technology and how outstanding teams and individual developers in the industry have brought the technology to its current level.

ChatGPT has been an application that has attracted high attention in the technology industry over the past year and is also the most popular and widely recognized artificial intelligence application among consumers. Today, we will briefly introduce the basic concepts of ChatGPT technology and explain why it is so outstanding.

The core technology of ChatGPT and other chat AI models is large language models, also known as LLM. These complex artificial intelligence technologies are essentially a combination of deep learning technology and massive datasets, creating AI models that can predict and summarize knowledge.

The interaction between humans and LLM is through natural language processing, and most LLMs are specifically designed for natural language processing (NLP). When users request a chatbot to answer a certain type of question, the chat AI bot will use its underlying technology, training data, and capabilities to provide answers to users as much as possible.

LLMs are built on top of transformer models (commonly known as transformers). This is a type of neural network that excels at predicting text and learning the context behind words. Because LLMs that use transformer models excel at natural language processing, they are able to perform human daily tasks well, such as solving math problems, generating code, and even writing short reports or making modification suggestions.

For this reason, chat AI bots such as ChatGPT, Microsoft Bing, and Claude have achieved tremendous success, almost single-handedly sparking an artificial intelligence revolution. Although many people believe that artificial intelligence may eventually be wiser than humans, there is no evidence to suggest that this will happen soon. Nevertheless, the possibilities brought about by the combination of AI models with human work and their highly promising capabilities, whether we accept it or not, prove that artificial intelligence will continue to exist. However, you may be wondering how these AI models integrate with the permissionless nature of cryptocurrency and blockchain, so let's explain their potential synchronicity and explore the two advanced technologies of AI and blockchain.

How does cryptocurrency help artificial intelligence applications?

The cryptocurrency industry is a topic of continuous discussion in the news media and other social media platforms every day. Since the white paper written by Satoshi Nakamoto in 2008, cryptocurrency has developed into a market worth $1.5 trillion, and globally renowned financial institutions are constantly applying for the issuance of various cryptocurrency ETFs.

In general, it is difficult to describe the unique benefits of blockchain technology to outsiders, mainly because the financial industry in most developed countries is very prosperous. In less developed areas, it is easier to explain and demonstrate the advantages of distributed ledger technology in financial transactions, mainly because corrupt financial institutions and governments still hold power around the world. Currencies in countries around the world often depreciate, and the vast majority of the world's population still cannot use banking infrastructure. In these places, the distributed ledger technology of blockchain has unique advantages.

Cryptocurrency is a way to provide banking services to people without bank accounts, providing individuals with an opportunity to become their own financial regulators. Whether they hold cryptocurrency in cold wallets or use numerous Dapps in the cryptocurrency ecosystem to hold cryptocurrency, they can enjoy services similar to banks.

The inherent features of blockchain, such as transparency, security, and decentralization, can greatly promote the storage, sharing, and utilization of AI data. Blockchain technology can provide an immutable distributed ledger for AI transactions and decision-making. The combination of this technology is expected to enhance the trustworthiness of AI systems, thereby reducing people's concerns about data manipulation or misuse.

One key aspect in which cryptocurrency can help artificial intelligence (and vice versa) is in the field of data management and security. Artificial intelligence systems require a large amount of data to learn and improve. Using blockchain technology, this data can be securely and transparently shared among different platforms and stakeholders. This not only ensures the integrity of the data but also opens up new pathways for collaborative research and development of artificial intelligence, breaking down barriers to innovation.

The combination of artificial intelligence and blockchain can create legitimate decentralized autonomous organizations (DAOs). These DAOs are managed by smart contracts, driven by artificial intelligence algorithms, and can operate, make decisions, and execute transactions independently without human intervention. Historically, the management of DAOs in cryptocurrency has not been ideal, as human emotions and economic incentives can overshadow the original purpose of DAOs. Using artificial intelligence technology can automate the DAO management process, reduce the need for human intermediaries, improve organizational efficiency, and reduce costs, thereby fundamentally changing various industries.

Another promising area is using blockchain as a means to incentivize the generation and sharing of artificial intelligence data. Through tokenization, individuals and organizations can receive economic rewards for contributing valuable data to artificial intelligence models, thereby promoting a more collaborative and inclusive artificial intelligence ecosystem.

Decentralized finance (DeFi) is also a potential beneficiary industry for artificial intelligence, and the combination of the two may create a new entity called decentralized artificial intelligence (DeAI). This approach can enable individuals and small entities to access artificial intelligence tools that were previously only available to large companies, thereby democratizing and popularizing artificial intelligence technology.

The integration of cryptocurrency and artificial intelligence not only has the potential to change the financial industry but also many aspects of our digital lives. By combining the strengths of the two technologies, we can expect future artificial intelligence to be not only more accessible but also more secure, transparent, and efficient. With that said, let's analyze the current operation and functions of the artificial intelligence industry.

Breaking the opaque barriers of artificial intelligence

Comparing the reform of the financial system by cryptocurrency with the revolution of intelligent production by artificial intelligence, we can draw some similarities and provide evidence for the combination of the two.

Currently, OpenAI, Google Deepmind, and Anthropic AI companies are conducting extensive research on related technologies.

Current opportunities in the fields of cryptocurrency and artificial intelligence

Now that we have introduced some basic knowledge about the collaboration between artificial intelligence and cryptocurrency, we can conduct a more in-depth study of some advanced projects in this field. Although most projects have many shortcomings (they are still working hard to develop their mainnets, and hope to gain a loyal user base and attract more attention from the wider cryptocurrency community), they are at the forefront of the industry and represent this rapidly developing industry well.

Bittensor, a decentralized artificial intelligence model network:

Bittensor is one of the most popular and mature projects in the cryptocurrency and artificial intelligence ecosystem to date. Bittensor is a decentralized network designed to democratize artificial intelligence by creating a platform for numerous decentralized commodity markets (or "subnetworks") and unifying them under a single token system. Its mission is to establish a network comparable to large artificial intelligence super companies such as OpenAI by adopting unique incentive mechanisms and advanced subnetwork architecture. Bittensor's system can be seen as a machine that efficiently transfers AI capabilities to the blockchain.

The network is managed by two key participants: miners and validators. Miners submit pre-trained artificial intelligence models to the network and receive rewards for their contributions, while validators ensure the validity and accuracy of the model outputs. This setup creates a competitive environment that incentivizes miners to continuously improve their models to achieve better performance and more rewards (native token $TAO). Users interact with the network by sending queries to validators, who then distribute the queries to miners. Validators rank the outputs of these miners and return the highest-ranked responses to the users.

Bittensor's approach to model development is unique. Unlike many AI labs or research institutions, Bittensor does not train models because training models is complex and costly. Instead, the network relies on a decentralized training mechanism. Validators' task is to evaluate the models generated by miners using specific datasets and score each model based on specific criteria such as accuracy and loss functions. This decentralized evaluation ensures the continuous improvement of model performance.

Bittensor's architecture includes the Yuma consensus mechanism, which is a hybrid of proof of work (PoW) and proof of stake (PoS) that allocates resources within the network's subnetworks. Subnetworks are self-contained economic markets, each focusing on different AI tasks such as text prediction or image generation, and can choose to join or exit the Yuma consensus based on their functionality.

Bittensor is an important step towards decentralized artificial intelligence, providing a platform to develop, evaluate, and improve various AI models in a decentralized manner. Its unique structure not only incentivizes the creation of high-quality AI models but also democratizes access to AI technology, potentially changing the development and use of AI in various industries.

Akash, the open-source supercloud:

Akash Network is an innovative, open-source supercloud platform designed to buy and sell computing resources securely and efficiently. Its vision is to provide users with the ability to deploy their own cloud infrastructure while also buying and selling unused cloud resources. This flexibility not only democratizes the utilization of cloud resources but also provides an economically efficient solution for users needing to scale their businesses.

At the core of the Akash system is a reverse auction mechanism, where users submit bids for their computing needs, and providers compete to offer services, often at much lower prices than traditional cloud systems. The system is supported by reliable and mature technologies such as Kubernetes and Cosmos, ensuring a secure and reliable platform for hosting applications. Akash's community-driven approach ensures that users have a voice in the development and management of the network, making it a truly user-centric public service.

Akash's infrastructure is user-friendly, defined using a YAML-based stack definition language (SDL), allowing users to create complex deployments across multiple regions and providers. This feature, combined with the leading container orchestration system Kubernetes, ensures deployment flexibility, as well as the security and reliability of application hosting. Additionally, Akash provides persistent storage solutions, ensuring data retention even after restarts, particularly beneficial for applications managing large datasets.

Overall, Akash stands out as a decentralized cloud platform, offering a unique solution to the monopolistic nature of current cloud service providers. Its model of utilizing underutilized resources in millions of data centers globally not only reduces costs but also improves the speed and efficiency of cloud-native applications. Akash does not require rewriting proprietary languages and avoids vendor lock-in, providing a versatile and accessible platform for various cloud-based applications.

Render, a platform for scalable compute access:

Render is a blockchain-based platform designed to meet the growing computational needs in media production, particularly in areas such as augmented reality, virtual reality, and AI-enhanced media. It leverages idle GPU cycles to connect content creators in need of computational power with providers who have available GPU resources. Through the use of blockchain technology, Render ensures secure and efficient processing of GPU-based tasks, including AI-driven content creation and optimization.

At the core of Render is the integration with artificial intelligence, which plays a crucial role in content creation and process optimization. The network supports AI-related tasks, enabling artists to use AI tools to generate assets and enhance digital artwork. Through this integration, ultra-high-resolution 3D worlds can be created, and rendering processes, such as AI denoising, can be optimized. Additionally, Render's application of AI extends to managing large-scale art collections and optimizing rendering workflows, broadening the possibilities of the creative process.

Render's ecosystem functions as a GPU resource marketplace, serving stakeholders such as artists, engineers, and GPU node operators. It democratizes access to computational power, allowing individual creators and large studios to conduct complex rendering projects at a low cost. Transactions within the ecosystem use the RNDR token, creating a vibrant economic system centered around rendering services. As AI continues to reshape digital content creation, Render is poised to play a key role in fostering new forms of creative expression and technological innovation in the digital media field.

Gensyn, a decentralized computing platform:

Gensyn is an AI and cryptocurrency project focused on addressing the resource constraints inherent in modern AI systems. The project aims to overcome the barriers posed by the enormous resource requirements for building foundational models in AI development. Gensyn's approach is to create a blockchain-based decentralized protocol to efficiently utilize global computing resources.

Gensyn's background highlights the increasing demand for computational power in AI systems, which is surpassing the available computing resources. For example, training large models (such as OpenAI's GPT-4 model) requires significant computational resources, posing a significant barrier to all stakeholders. This has led to the need for a system that can effectively utilize all available computing resources to address the limitations of current solutions, as current solutions are either too expensive or insufficient to meet the needs of large-scale AI work.

Gensyn aims to address this issue by creating a decentralized protocol that can connect and validate off-chain deep learning work in an economically efficient manner. The protocol faces several challenges, including work validation, market dynamics, pre-work estimation, privacy concerns, and the need for effective parallelization of deep learning models. The protocol aims to establish a trustless computing network, provide economic incentives for participation, and offer a way to verify whether computational work is performed as promised.

The Gensyn protocol is a first-layer trustless protocol for deep learning computation, rewarding participants for contributing computing time and executing ML tasks (ML is a type of computation task within Gensyn). It employs various technologies to verify completed work, including probabilistic learning proofs, graph-based pinpointing protocols, and Truebit-style incentive games. The system involves various participants, such as submitters, solvers, validators, and reporters, each playing specific roles in the computation process.

In practice, the Gensyn protocol involves multiple stages from task submission to contract arbitration and settlement. It aims to create a transparent, low-cost market for ML computation, achieving scalability and efficiency. The protocol also provides an opportunity for miners with powerful GPUs to repurpose their hardware for ML computation, potentially at a lower cost compared to mainstream providers. This approach not only addresses the computational challenges of AI but also aims to democratize access to AI resources.

Fetch, the open economic platform for AI:

Fetch.ai has a longer development history than the projects mentioned earlier, offering a wide range of services on its website. At its core, Fetch is an innovative project at the intersection of artificial intelligence and cryptocurrency, aiming to fundamentally change the way economic activities are conducted. The foundation of Fetch's products is its AI agents, designed as modular components that can execute specific tasks through programming. These agents can autonomously connect, search, and trade, creating dynamic markets and transforming traditional economic activities.

Fetch offers one of its main services to enable traditional products to access artificial intelligence. This is achieved by integrating their application programming interfaces with Fetch.ai agents, a process that is fast and does not require changes to the underlying business applications. AI agents can collaborate with other agents in the network, providing possibilities for new use cases and business models. Additionally, these agents have the ability to negotiate and trade on behalf of users, allowing users to profit from their deployments.

Furthermore, these agents can provide inferences from machine learning models, enabling users to monetize their insights and enhance their machine learning models.

Fetch also introduces Agentverse, a no-code management service that simplifies the deployment of AI agents. Just as traditional no-code platforms (Replit) and services like Github's Copilot enable the general public to write code, Fetch is striving to further democratize Web3 development in its own unique way.

Through Agentverse, users can easily launch their first agent, significantly lowering the barrier to using advanced AI technology. In terms of AI engines and agent services, Fetch utilizes large language models to discover and guide task execution to suitable AI agents. This system not only enables the monetization of AI applications and services but also serves as a comprehensive platform for agent services, including building, listing, analyzing, and hosting.

The platform enhances its usability through features such as "search and discover" and "analytics." Agents can register in Agentverse, which employs LLM-based targeted search to actively discover on the Fetch.ai platform. Analytical tools can be used to improve the effectiveness of agent semantic descriptors, enhancing their discoverability. Additionally, Fetch.ai has integrated an IoT gateway for offline agents, allowing them to collect information and process it in batches upon reconnection.

Finally, Fetch.ai provides hosting services for hosted agents, offering all functionalities of Agentverse except hosting. The platform also introduces an open network for agent addressing and naming using Fetch.ai's Web3 network, marking a novel DNS addressing approach integrating blockchain technology into the system.

Overall, Fetch.ai provides a multifaceted platform that integrates artificial intelligence and blockchain technology, offering tools for AI agent development, monetization of machine learning models, and a pioneering approach to search and discovery in the digital economy. The combination of AI agents with blockchain technology paves the way for automating and optimizing various processes in a decentralized and efficient manner.

The next steps and predictions for these two industries:

The seamless integration of artificial intelligence and blockchain technology represents significant progress in both fields. This integration is not just a fusion of two cutting-edge technologies but a transformative synergy that redefines the boundaries of digital innovation and decentralization.

As explored by projects such as Fetch.ai, Bittensor, Akash Network, Render Network, and Gensyn, the potential applications of this integrated approach demonstrate the significant possibilities and advantages of combining artificial intelligence with blockchain.

Looking ahead, the integration of artificial intelligence and blockchain is poised to play a crucial role in shaping various industries. From enhancing data security and integrity to creating new models for decentralized autonomous organizations, this integration is expected to bring about more efficient, transparent, and accessible technologies. Particularly in the decentralized finance sector, the emergence of decentralized artificial intelligence (DeAI) can break down traditional barriers favoring large corporations, democratizing access to AI technology. This will lead to a more inclusive digital economy, where individuals and small entities can leverage AI tools and services that were previously out of reach.

Furthermore, the integration of these technologies is expected to address the most pressing challenges in both fields. In the field of artificial intelligence, issues such as data silos and the immense computational power required for training large models can be alleviated through decentralized data management and shared computing capabilities provided by blockchain. In the blockchain field, artificial intelligence can improve efficiency, automate decision-making processes, and enhance security mechanisms. As the industry evolves, developers, researchers, and stakeholders must continue to explore and harness the synergy between artificial intelligence and blockchain. By doing so, they can not only advance both fields but also drive innovation in the entire digital domain, ultimately benefiting society as a whole.

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