1. Introduction
1.1 Preface
In the previous report, we delved into the development history of ICP and its strategic vision. At a recent technology forum held in Argentina, Dominic Williams, the founder of ICP and president of Dfinity, delivered a keynote speech, sharing his vision for the future of decentralized computing and sparking extensive discussions within the industry. In his speech, he emphasized how ICP breaks boundaries by combining the power of blockchain with artificial intelligence, allowing people to see the future of how these technologies can change the way we build and execute applications. Meanwhile, according to the latest announcement from ICP, the DFINITY Foundation will establish a new office in San Francisco to develop AI, decentralized computing, and other technologies centered around Silicon Valley and carry out strategic layout.
Based on this background, in today’s article, we will delve into the technical logic and ecological layout of how ICP empowers AI, analyzing its key breakthroughs in technical architecture, practical applications, and future development directions.
Today, many people are already using ChatGPT to explore creativity, obtain information, and analyze and create content. ChatGPT is a large language model with massive parameters trained through a large amount of data. These models have pushed AI applications to a new peak. In fact, before applications like ChatGPT emerged, there were already Web2 products related to AI algorithms. One of the earliest large-scale embodiments was services like TikTok and Instagram Reels. They are not just social media services in the traditional sense; they are actually driven by powerful AI engines. AI analyzes video content and user interaction behavior (such as user viewing duration) to provide accurate content recommendations, thereby improving the user experience. This “customized” service makes them very attractive. Moreover, this trend is expanding in a broader direction.
However, as AI rapidly expands into various industries, problems with AI applications in the traditional Web2 architecture are also gradually emerging. For example, most AI models are centralized, which means they rely on single-point control or a limited number of nodes to execute, facing many limitations such as data privacy, centralized computation resources, and lack of transparency. In addition, traditional IT infrastructure (such as AWS) involves cumbersome configuration and maintenance processes: from cloud account registration, server configuration, to database installation, security updates, etc., it is time-consuming, labor-intensive, error-prone, and insecure by default. Moreover, the upgrade cycle of traditional IT is long and complex, making it difficult to support real-time requirements of new models.
The emergence of Web3 has brought new opportunities for the development of AI. With features such as decentralization, transparency, and autonomy, Web3 can achieve smart contract automation, promote the democratization of AI, enhance interoperability, promote fair governance, and improve network security. Some platforms have started to try to solve these problems. For example, Vercel (a provider of cloud platform as a service) provides AI application services through customized infrastructure. These platforms can alleviate some problems but are still far from ideal. More importantly, software generated by AI will be bound to these dedicated platforms, and related data may also be controlled by these platforms, resulting in users being locked into their ecosystem (customer lock-in). In other words, applications and services in this mode cannot achieve decentralization.
The world is beginning to realize that to truly unleash the potential of combining AI with Web3, a powerful infrastructure is needed. In this context, ICP is looking for new solutions. In the traditional AI environment, training models is like a conductor controlling an orchestra to create beautiful music. The conductor here is the central server, which needs to process a large amount of data and has powerful computing capabilities. This is similar to the way companies like OpenAI train large models on large central servers.
However, in ICP, the training method of AI models is different. It is not a single central conductor controlling everything but rather every participant is both a conductor and a musician, collaborating with each other to complete the task. This means that every node or device in the network can contribute to the training, decision-making, and execution of artificial intelligence models.
Decentralized AI models on ICP have the following advantages compared to centralized AI models:
Trust and transparency: Decentralized Artificial Intelligence (DeAI) models on ICP are executed entirely on the chain, with immutability and openness. Users do not need to blindly trust centralized servers but can verify the training and inference process of the models. This solves a key problem in centralized AI, where users often cannot understand how data is used and how models behave.
Data security and control: ICP utilizes Chain Fusion technology to securely access data from different blockchains (detailed later). This brings advantages in data security and control. Users can retain ownership of their data while allowing DeAI models to access and learn from the data, which is different from the centralized AI system where data is usually managed in a centralized manner.
Censorship resistance: AI models running on ICP can resist censorship. Centralized AI models may be subject to control and manipulation by operators. DeAI on ICP provides a more open and censorship-resistant platform, which helps promote fair development and deployment of artificial intelligence.
Scalable composability: Compared to traditional AI systems, DeAI on ICP has stronger scalable composability. Through connected node networks, DeAI can flexibly expand its capacity and process tasks in parallel, thereby improving overall capacity while maintaining high levels of security and performance.
Inclusiveness: ICP offers permissionless and composable access, promoting inclusiveness and fairness. Individuals and small companies can also participate in the development and decision-making of artificial intelligence, encouraging innovation and collaboration.
ICP is transforming the internet into a vast decentralized world computer, where computation is conducted securely through a network of node machines. By combining decentralized node hardware, ICP provides developers with the ability to host and build software applications without the limitations imposed by traditional cloud services. With decentralized infrastructure, AI hosted on ICP is fundamentally immune to network attacks, meaning that sensitive data is always protected from intrusion and tampering.
In fact, ICP has already demonstrated its capabilities by executing neural networks for tasks such as image classification and face recognition. Furthermore, the ability to run larger AI models such as Llama 3 on ICP opens the door to more advanced AI-driven applications. AI executed on the public network is immune to network attacks, runs uninterrupted, and remains accessible at all times. This concept represents a fundamental shift in the way AI is deployed in critical areas such as healthcare, finance, and government in human society.
1.2 Underlying Technologies Supporting AI on ICP