jakiro
Source: IOBC Capital
As a new decentralized, open and transparent Internet paradigm, Web3 has a natural opportunity to integrate with AI.Under the traditional centralized architecture, AI computing and data resources are strictly controlled, and there are many challenges such as computing power bottlenecks, privacy leakage, and algorithm black boxes.Web3 is based on distributed technology and can inject new impetus into the development of AI through shared computing power network, open data market, and private computing.At the same time, AI can also bring many empowerments to Web3, such as smart contract optimization, anti-cheating algorithms, etc., to help its ecological construction.Therefore, exploring the combination of Web3 and AI is crucial to building the next generation of Internet infrastructure and releasing the value of data and computing power.
Data-driven: A solid foundation for AI and Web3
Data is the core driving force for the development of AI, just as fuel is to the engine.AI models need to digest a large amount of high-quality data to gain in-depth understanding and powerful reasoning capabilities. Data not only provides a training basis for machine learning models, but also determines the accuracy and reliability of the model.
In the traditional centralized AI data acquisition and utilization model, the following major problems exist:
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Data acquisition costs are high and it is difficult for small and medium-sized enterprises to bear;
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Data resources are monopolized by technology giants, forming data silos;
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Personal data privacy is at risk of leakage and abuse
Web3 can solve the pain points of traditional models with a new decentralized data paradigm.
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Through Grass, users can sell idle networks to AI companies, capture network data decentralizedly, and provide real and high-quality data for AI model training after cleaning and transformation;
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Public AI adopts the “label to earn” model, incentivizes global workers to participate in data annotation through tokens, gathers global professional knowledge and enhances data analysis capabilities;
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Blockchain data trading platforms such as Ocean Protocol, Streamr, etc. provide an open and transparent trading environment for both data supply and demand parties to inspire data innovation and sharing.
Despite this, there are also some problems with real-world data acquisition, such as data quality, difficulty in processing, diversity and insufficient representation.Synthetic data may be the future star of the Web3 data track.Based on generative AI technology and simulation, synthetic data can simulate the properties of real data, as an effective supplement to real data, and improve data usage efficiency.In the fields of autonomous driving, financial market trading, game development, etc., synthetic data has shown its mature application potential.
Privacy Protection: The role of FHE in Web3
In the data-driven era, privacy protection has become the focus of global attention. The introduction of regulations such as the EU’s General Data Protection Regulation (GDPR) reflects the strict protection of personal privacy.However, this also presents challenges: some sensitive data cannot be fully utilized due to privacy risks, which undoubtedly limits the potential and reasoning capabilities of AI models.
FHE is fully homomorphic encryption, allowing direct calculation operations on encrypted data without decrypting the data, and the calculation results are consistent with the results of the same calculations on plain text data.
FHE provides solid protection for AI privacy computing, allowing GPU computing power to perform model training and inference tasks in an environment that does not touch the original data.This brings huge advantages to AI companies.They can securely open API services while protecting trade secrets.
FHEML supports encrypting data and models throughout the machine learning cycle, ensuring the security of sensitive information and preventing the risk of data leakage.In this way, FHEML strengthens data privacy and provides a secure computing framework for AI applications.
FHEML is a complement to ZKML, which demonstrates the correct execution of machine learning, while FHEML emphasizes the calculation of encrypted data to maintain data privacy.
Computing power revolution: AI computing in decentralized networks
The current computing complexity of AI systems doubles every three months, resulting in a surge in computing power demand, far exceeding the supply of existing computing resources.For example, OpenAI’s GPT-3 model training requires huge computing power, which is equivalent to 355 years of training time on a single device.Such a shortage of computing power not only limits the advancement of AI technology, but also makes advanced AI models out of reach for most researchers and developers.
At the same time, the global GPU utilization rate is less than 40%, coupled with the slowdown in microprocessor performance improvement and chip shortages caused by supply chain and geopolitical factors, all of which make the computing power supply problem even more serious.AI practitioners are in a dilemma: either purchase hardware by themselves or rent cloud resources, they urgently need an on-demand, cost-effective computing service method.
IO.net is a Solana-based decentralized AI computing power network that provides AI companies with a financial and easy-to-access computing power market by aggregating idle GPU resources around the world.The computing power demanding party can publish computing tasks on the network. The smart contract allocates the tasks to the miner nodes that contribute computing power. The miner performs the tasks and submits the results, and receives points rewards after verification.IO.net’s solution improves resource utilization efficiency and helps solve computing power bottlenecks in fields such as AI.
In addition to the general decentralized computing power network, there are platforms such as Gensyn and Flock.io that focus on AI training, and dedicated computing power networks such as Ritual and Fetch.ai that focus on AI reasoning.
The decentralized computing power network provides a fair and transparent computing power market, breaks the monopoly, lowers the application threshold, and improves the efficiency of computing power utilization.In the web3 ecosystem, decentralized computing power network will play a key role, attracting more innovative dapps to join, and jointly promote the development and application of AI technology.
DePIN: Web3 empowers Edge AI
Imagine that your phone, smartwatch, and even smart devices at home have the ability to run AI – this is the charm of Edge AI.It allows computing to occur at the source of data generation, achieve low latency and real-time processing, while protecting user privacy. Edge AI technology has been applied to key areas such as autonomous driving.
In the Web3 field, we have a more familiar name – DePIN.Web3 emphasizes decentralization and sovereignty of user data. DePIN can enhance user privacy protection and reduce the risk of data leakage by processing data locally. Web3’s native Token economic mechanism can incentivize DePIN nodes to provide computing resources and build a sustainable ecosystem.system.
At present, DePIN is developing rapidly in the Solana ecosystem and has become one of the preferred public chain platforms for project deployment.Solana’s high TPS, low transaction fees and technological innovations provide strong support for the DePIN project.Currently, the market value of the DePIN project on Solana is over US$10 billion, and well-known projects such as Render Network and Helium Network have made significant progress.
IMO: New paradigm for AI model release
The concept of IMO was first proposed by Ora protocol, which tokenized AI models.
In the traditional model, due to the lack of a revenue sharing mechanism, once the AI model is developed and put into the market, it is often difficult for developers to obtain continuous benefits from the subsequent use of the model, especially when the model is integrated into other products and services.Creators have a hard time tracking usage, let alone making a profit from it.And the performance and effectiveness of AI models often lack transparency, which makes it difficult for potential investors and users to evaluate their true value, limiting the market recognition and business potential of the model.
IMO provides a new way of funding and value sharing for open source AI models. Investors can purchase IMO tokens and share the subsequent benefits generated by the model.Ora Protocol uses two ERC standards, ERC-7641 and ERC-7007, combining AI oracle (Onchain AI Oracle) and OPML technology to ensure the authenticity of the AI model and the ability of token holders to share the benefits.
The IMO model enhances transparency and trust, encourages open source collaboration, adapts to crypto market trends, and injects impetus into the sustainable development of AI technology.IMO is still in its early stage of trial, but with the increase in market acceptance and the expansion of participation, its innovation and potential value are worth looking forward to.
AI Agent:A new era of interactive experience
AI Agent is able to perceive the environment, think independently, and take corresponding actions to achieve the established goals.Supported by the large language model, AI Agent can not only understand natural language, but also plan decisions and perform complex tasks.They can act as virtual assistants, learn their preferences through interactions with users and provide personalized solutions.Without clear instructions, AI Agent can also solve problems independently, improve efficiency and create new value.
Myshell is an open AI native application platform that provides a comprehensive and easy-to-use creative toolset, supports users to configure robot functions, appearance, sound, and connect to external knowledge bases. It is committed to creating a fair and open AI content ecosystem, leveraging generative AITechnology empowers individuals to become super creators.Myshell trains a special large language model to make role-playing more humane; voice cloning technology can accelerate personalized interaction of AI products, and MyShell reduces the cost of voice synthesis by 99%, and voice cloning can only be achieved in 1 minute.Using Myshell’s customized AI Agent, it can currently be used in many fields such as video chat, language learning, image generation, etc.
In the integration of Web3 and AI, it is currently more about exploring the infrastructure layer, how to obtain high-quality data and protect data privacy, how to host models on the chain, how to improve the efficient use of decentralized computing power, and how to verifyKey issues such as large language models.As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give birth to a series of innovative business models and services.
