The Collision of Crypto and AI: Opportunities, Representative Projects and the Future

Author: Reflexivity Research, translated by Golden Finance xiaozou

1. The collision of artificial intelligence (AI) and encryption

Recently, the artificial intelligence (AI) industry has been making headlines with mixed reviews. While you may be well aware of the recent OpenAI farce, and may have done some exploration into the capabilities of existing AI technologies, you probably haven’t thought much about the interaction between AI and blockchain systems. In this article, we will introduce some of the existing applications that are dedicated to combining AI and blockchain technology, as well as the prospects for these applications and the AI industry in the coming years.

2. Learn about artificial intelligence (AI) and how it relates to cryptography

Before we get into the details of the project and more technical details, I think it’s important to cover the basics of AI technology and how talented teams and individual developers in the industry have created today’s game.

If you’re already familiar with ChatGPT, it’s the most popular and widely recognized consumer-facing AI application today, and it’s managed to gain the attention of the tech industry over the past year — let us explain the underlying concept of the technology and why it does so well for all the user needs.

The core technology that underpins ChatGPT and other consumer-facing chat models is the well-known Large Language Model (LLM). These sophisticated AI technologies are essentially a combination of deep learning techniques/algorithms and very large datasets, which together create an AI model capable of predicting and summarizing information.

The interaction between humans and LLMs is processed through natural language, and most LLMs are built specifically using natural language processing (NLP). First, the user asks the chatbot to answer a certain type of question in natural language, and then the chatbot uses its underlying technology, training data, and capabilities to provide the user with the answers possible.

The LLM is created based on the transformer model. Transformer is a neural network that is good at predicting text and learning the context behind words. LLMs using transformer models are good at NLP and are good at handling everyday human tasks, such as solving math problems, generating code templates, and even writing briefs or proofreading texts.

That’s why chatbots like ChatGPT, Microsoft’s Bing AI, and Claude have been hugely successful, almost single-handedly sparking an AI revolution. While many believe that AI systems may eventually gain more capabilities and intelligence than humans, there is little evidence that this will happen anytime soon. In any case, the possibilities of combining these models with human workflows and the promising existing capabilities are enough to show that AI is here to stay, whether we like it or not. However, you may be wondering how these models can be combined with the permissionless nature of cryptography and blockchain, so let’s explain the potential convergence trends and explore these two disruptive forms of technology.

3. How does encryption technology help the development of AI applications?

The crypto industry is featured every day in the news, big media outlets, and other social media platforms. In 2008, Satoshi Nakamoto wrote a white paper that has become a $1.5 trillion market, prompting the world’s largest financial institutions to approve or reject Bitcoin spot ETF applications.

It is often difficult to describe the intrinsic benefits of blockchain technology to outsiders, mainly because most first-world countries have very developed financial industries and interact with users very smoothly. Outside of a developed country like the United States, it is much easier to explain and demonstrate the power of permissionless accounts for financial transactions, in large part because of the corrupt financial institutions and governments in these places, which, unfortunately, still hold the political and economic lifeblood of the world. Countries around the world are drumming on a regular basis, and the vast majority of the world’s population still does not have access to banking infrastructure.

Crypto is a way of banking the unbanked, and this technology offers individuals the opportunity to become the stewards of their own financial operations, whether it’s holding cryptocurrencies in cold wallets or taking advantage of the numerous decentralized finance applications available in the crypto ecosystem. The prospect of permissionless finance is difficult to describe, but the revolutionary changes that are taking place every day cannot be underestimated.

The inherent transparency, security, and decentralization of blockchain can greatly facilitate the way AI data is stored, shared, and used. The convergence of AI and blockchain technology is expected to enhance trust in AI systems by providing an immutable ledger for AI transactions and decision-making, reducing concerns about manipulating or misusing data.

One of the key aspects where encryption can advance AI (and vice versa) lies in the areas of data management and security. AI systems require large amounts of data to learn and improve. With blockchain technology, this data can be shared securely and transparently across different platforms and stakeholders. This not only ensures data integrity, but also opens up new avenues for AI collaborative research and development, breaking down data silos that often hinder innovation.

The combination of artificial intelligence and blockchain could give rise to legitimate decentralized autonomous organizations (DAOs). These DAOs are managed by smart contracts and powered by AI algorithms that can operate and make decisions independently and execute transactions without human intervention. Historically, the management of crypto DAOs has not been ideal, as human emotions and the desire for money often overshadow the original purpose of the DAO. Implementing AI systems can revolutionize industries by automating processes and reducing the need for intermediaries, increasing efficiency while reducing costs.

Another promising area is the use of blockchain as an incentive to generate and share AI data. Through the tokenization process, individuals and organizations can be rewarded for contributing valuable data to AI models, building a more collaborative and inclusive AI ecosystem.

Decentralized finance (DeFi) is also a potentially huge contributor to AI and is poised to create something that can be called decentralized artificial intelligence (DeAI). This will democratize the use of AI technology and allow individuals and small entities to access AI tools and services that were previously only available to large companies.

The convergence of cryptocurrencies and artificial intelligence has the potential to transform not only the financial sector, but also every aspect of our digital lives. By combining the strengths of these two technologies, we can look forward to a future where AI is not only more accessible, but also more secure and transparent, and potentially more efficient. With that said, let’s analyze the current performance of the AI industry.

4. Break the opaque barrier of artificial intelligence

By likening crypto’s reform of the financial system to an intelligent revolution in the production of AI systems, we can draw some highly relevant similarities and provide a basis for combining the two.

Today, AI companies, such as OpenAI, Google Deepmind, Anthropic, and many others, are conducting their own research and operations.

5. Current opportunities in crypto and artificial intelligence

Now that we’ve covered the basics of AI and crypto synergies, let’s take a closer look at some of the leading projects in the space. While most of them are still actively working to bootstrap their networks, gain a loyal user base, and gain traction from the broader crypto community, they are all at the forefront of the industry and are excellent representatives of this fast-growing industry.

(1) Bittensor: A decentralized AI model network

Bittensor is by far the most popular and well-established project in the Crypto & AI ecosystem. Bittensor is a decentralized network that aims to democratize the AI space by creating a platform for numerous decentralized commodity markets or “subnets” that unify the use of a single token system. Its mission is to build a network that rivals the big giants in the AI field such as OpenAI by employing a unique incentive mechanism and an advanced subnet architecture. The Bittensor system can be seen as a blockchain-powered machine that can effectively bring AI capabilities on-chain.

The network is governed by two key players: miners and validators. Miners submit pre-trained AI models to the network and are rewarded for their contributions, while validators ensure the validity and accuracy of the model’s output. This setup creates a competitive environment that incentivizes miners to continuously improve their models for better performance and higher TAO (the network’s native token) returns. Users interact with the network by sending queries to validators, who then distribute those queries to miners. The validator sorts the output of these miners and returns the highest-ranked response to the user.

Bittensor’s approach to model development is unique. Unlike many AI labs or research institutes, Bittensor doesn’t do this due to the high cost and complexity of training models. The network relies on a decentralized training mechanism. The Validator’s task is to evaluate the models generated by the miners using a specific dataset and score each model based on certain criteria, such as accuracy and loss function. This decentralized approach to evaluation ensures continuous improvement in model performance.

The Bittensor architecture includes the Yuma consensus mechanism, a unique hybrid of Proof-of-Work (PoW) and Proof-of-Stake (PoS) that allocates resources across the network’s subnets. Subnets are independent economic marketplaces, each focused on a different AI task, such as text prediction or image generation, and can opt in or out of Yuma consensus depending on its function.

Bittensor is an important step in the decentralization of AI, 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 the use of AI technology, which is expected to change the way AI is developed and used in various fields.

(2) Akash: Open Source Supercloud

The 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 and buy and sell unused cloud resources. This flexibility not only democratizes the use of cloud resources, but also provides a cost-effective solution for users who need to scale their operations.

At the heart of the Akash system is a reverse auction mechanism where users can submit bids based on their computational needs and vendors can compete for services from each other, often resulting in significantly lower prices than traditional cloud systems. The underlying support of the system is the mature and reliable technologies such as Kubernetes and Cosmos, which ensure that the platform hosts applications securely and reliably. Akash’s community-driven approach ensures that its users have a voice in the development and governance of the network, making it a truly user-centric public service.

Akash’s infrastructure is defined using an easy-to-use, YAML-based SDL that allows users to create complex deployments across multiple domains and vendors. This feature, combined with Kubernetes, the leading container orchestration system, not only guarantees deployment flexibility, but also guarantees the security and reliability of application hosting. In addition, Akash offers a persistent storage solution that ensures data retention even after a reboot, which is especially beneficial for applications that manage large data sets.

Overall, the Akash Network stands out as a decentralized cloud platform that offers a unique solution to the monopolistic problems of current cloud service providers. It leverages underutilized resources in millions of data centers around the world, a model that not only reduces costs, but also increases the speed and efficiency of cloud-native applications. With no need to rewrite proprietary languages and vendor agnosticism, Akash provides a common platform for a wide range of cloud applications.

(3) Render: Compute access extension platform

The Render Network is a blockchain platform designed to address the growing computing needs in media production, particularly in areas such as augmented reality, virtual reality, and AI-enhanced media. It leverages unused GPU cycles to connect content creators who need computing power with vendors who have available GPU resources. This decentralized approach that leverages blockchain technology ensures that GPU-based tasks, such as AI-powered content creation and optimization, are handled safely and efficiently.

The core service of the Render network is its integration with artificial intelligence, which plays a vital role in both content creation and process optimization. The network supports AI-related tasks, enabling artists to use AI tools to generate assets and enhance digital artworks. This integration allows for the creation of ultra-high-resolution 3D worlds and optimized rendering processes such as AI denoising. In addition, the Render network’s use of artificial intelligence extends to large-scale art collection management and rendering workflow optimization, expanding the possibilities of the creative process.

The Render network ecosystem serves as a marketplace for GPU resources, serving various stakeholders such as artists, engineers, and node operators. It democratizes the use of computing power more and makes complex rendering projects affordable for individual creators and large studios. Transactions within this ecosystem are carried out using RNDR tokens, creating a vibrant economy centered around rendering services. As AI continues to reshape digital content creation, the Render Network will be a key player in facilitating new creative expressions and technological innovations in the digital media space.

(4) Gensyn: Decentralized computing platform

Gensyn is an AI-combined cryptocurrency project focused on overcoming the computational challenges and resource constraints inherent in state-of-the-art AI systems. The project aims to overcome the barriers to AI development caused by the huge resource requirements required to build foundational models. Gensyn’s approach is to create a decentralized blockchain protocol that makes efficient use of global computing resources.

The background to Gensyn’s birth highlights the increasing computational complexity of AI systems, outpacing the available compute supply. For example, training a large model like OpenAI’s GPT-4 requires a lot of resources, which creates a huge hurdle for all parties involved. This dynamic has created the need for systems that can make efficient use of all available computing resources to address the limitations of current solutions, which are either too expensive or not sufficient for large-scale AI tasks.

Gensyn aims to solve this problem by creating a decentralized protocol that connects and validates off-chain deep learning tasks in a cost-effective manner. The protocol faces several challenges, including task verification, market dynamics, pre-evaluation, privacy concerns, and the need for efficient parallelization of deep learning models. The protocol aims to build a trustless computing network, provide incentives for participation, and provide a way to verify that computing tasks are performing as promised.

The Gensyn protocol is a first-layer trustless protocol for deep learning computing, rewarding participants for contributing computing time and performing ML tasks. It uses a variety of techniques to verify completed tasks, including probabilistic learning proofs, graph-based pinpoint protocols, and Truebit-type incentive games. The system involves various participants, such as Submitter, Solver, Verifier, and Whistleblower, each of whom has a specific role in the computational process.

In practice, the Gensyn protocol consists of several stages from task submission to contract arbitration and settlement. The protocol aims to create a transparent, low-cost marketplace for machine learning (ML) computing, enabling scalability and efficiency. The protocol also provides an opportunity for miners with powerful GPUs to use their hardware for machine learning calculations at a potentially lower cost than mainstream vendors. This approach not only solves the computational challenges in the field of AI, but also democratizes access to AI resources.

(5) Fetch: An open-source platform for the AI economy

Fetch.ai has been on longer than some of the previously mentioned projects, and a wide variety of services are available on its website. At its core, Fetch is an innovative project that combines artificial intelligence (AI) and cryptocurrencies to revolutionize the way economic activities and processes are executed. Fetch services are based on its AI agents, which are designed as modular building blocks that can be programmed to perform specific tasks. These agents are able to connect, search, and trade autonomously, creating dynamic markets that change the traditional landscape of economic activity.

One of Fetch’s key services is the ability to integrate traditional products with AI. This is achieved by integrating their APIs with Fetch.ai agents, the integration process is quick and does not require changes to the underlying business applications. AI agents can be combined with other agents in the network, opening up possibilities for new use cases and business models. In addition, these agents have the ability to negotiate and transact on behalf of users, which allows them to monetize their deployments.

In addition, these agents can provide inferences from machine learning models, allowing users to monetize their insights and enhance their machine learning models.

Fetch also introduced Agentverse, a no-code management service that simplifies the deployment of AI agents. In the same way that traditional no-code platforms (Replit) are growing in popularity and services like Github Copilot are making coding accessible to the general public, Fetch is further democratizing web3 development in its own unique way.

With Agentverse, users can effortlessly launch their first agent, which greatly lowers the barrier to entry for using advanced AI technologies. In terms of AI engines and agent services, Fetch leverages large language models (LLMs) to discover and send task execution to the appropriate AI agents. The system can not only monetize AI applications and services, but also serve as a comprehensive platform for agent services such as building, listing, analysis, and hosting.

The platform enhances utility with features such as search, discovery, and analytics. Agents can be registered in the Agentverse for easy identification and discovery on Fetch.ai platforms, Fetch.ai platforms employ an LLM-based targeted search mechanism. Profiling tools can be used to improve the effectiveness of agent semantic descriptors, thereby enhancing their discoverability. In addition, Fetch.ai integrated an IoT gateway for offline agents, enabling them to collect messages and process them in batches upon reconnection.

Finally, Fetch.ai offers managed services for managing agents, which provide all the features of the Agentverse in addition to managed services. The platform also introduces an open-source agent addressing and naming network that leverages Fetch.ai Web3 networking. This means a new approach to Web DNS addressing that integrates blockchain technology into the system. Overall, Fetch.ai provides a versatile platform that combines AI and blockchain technologies to provide tools for AI agent development, machine learning model monetization, and breakthrough search and discovery methods in the digital economy. The combination of AI agents and blockchain technology paves the way for automated and optimized processes in a decentralized and efficient manner.

6. What’s next for the AI and crypto industry?

The seamless convergence of AI and blockchain technology represents a key advancement in both areas. This combination is not just a fusion of two cutting-edge technologies, but a transformative synergy that redefines the boundaries of digital innovation and decentralization. The potential applications of this combination (as explored in projects such as Fetch.ai, Bittensor, Akash Network, Render Network, and Gensyn) demonstrate the enormous possibilities and advantages of combining the computing power of AI with a secure and transparent framework for blockchain.

As we look to the future, it is clear that the convergence of AI and blockchain will play a key role in shaping various industries. From enhancing data security and integrity to creating new models of decentralized autonomous organizations, this convergence promises to lead to more efficient, transparent, and accessible technologies. Especially in the field of decentralized finance, the emergence of decentralized artificial intelligence (DeAI) can democratize the use of AI technology and break down the barriers that have traditionally been monopolized by large companies. This promises to lead to a more inclusive digital economy in which individuals and small entities can also enjoy AI tools and services that were previously out of reach.

In addition, the integration of AI and cryptography can also address some of the most pressing challenges in both areas. In the field of artificial intelligence, problems such as data silos and the huge computing resources required to train large models can be alleviated by the decentralized data management and computing power sharing of blockchain. In the field of blockchain, AI can increase efficiency, automate the decision-making process, and improve security mechanisms. It is critical that developers, researchers, and stakeholders continue to explore and leverage the synergies between AI and blockchain. In doing so, they will not only be able to contribute to the development of these separate areas, but will also drive innovation in the digital domain as a whole, ultimately benefiting society as a whole.

Source: Golden Finance