Author: Dominic Williams, CoinDesk; Translation: Song Xue, LianGuai
The rapid development of Artificial Intelligence (AI) has attracted worldwide attention, and many people are wondering what the next step for this technology breakthrough will be. While AI has demonstrated its potential to transform various industries, it faces a major obstacle in widespread adoption: lack of trust and transparency.
Decentralized computing through blockchain can alleviate the current trust issues, but there is one problem.
Dominic Williams is the founder and Chief Scientist of the DFINITY Foundation, a non-profit research organization and major contributor to Internet Computer.
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Currently, insights into AI models are limited, and there is no real way to verify the source of the data on which the models are trained, what data the models have collected, and how this data informs the models and their accuracy.
Until there is fundamental change in the transparency of AI programs and the infrastructure they build, users at all levels will not feel secure in using these models due to lack of trust and overall skepticism.
The intersection of AI and blockchain technology offers a synergistic effect that will enhance both technologies and drive widespread adoption through their integration.
Currently, due to the need for large amounts of computing resources and datasets, most blockchains lack the necessary infrastructure to support AI models, limited by computing power. The limitation of computing power is partly due to the fact that most blockchains are not fully decentralized.
Instead, many of the most popular blockchains in the world today rely on centralized cloud infrastructure (such as Google Cloud and Amazon Web Services), which hinders the ability of blockchains to support the processing and storage of data at the speed required by AI.
Despite the negative headlines about the current integration attempts of AI and blockchain, the current integration results in AI running alongside blockchain rather than AI running on the blockchain as originally intended.
The core infrastructure and underlying technology of these “blockchain AI” projects primarily run on centralized servers and utilize plugins to connect centralized AI models to the centralized cloud network running the blockchain. This fails to address the fundamental issues of trust and transparency in utilizing blockchain technology to advance AI.
Fully/Completely decentralized blockchains, like the Internet Computer (ICP), the network I helped build, provide computing power that matches or exceeds that of Web2 cloud servers, enabling AI models to run entirely within smart contracts. This will make the training parameters and inputs of large language models both open source and tamper-proof. To achieve AI integration on the blockchain, we need a blockchain that can process data at a speed comparable to Web2 clouds, and this can only come from complete decentralization.
Hosting artificial intelligence models on the blockchain allows AI systems to leverage the inherent decentralization to improve transparency in various aspects of the model. Therefore, AI on the blockchain is the logical next step for long-term success, as the blockchain enhances the credibility, accountability, and security of AI, thereby strengthening trust among users.
However, there is a misconception about how these two technologies work together, and until these misconceptions are cleared, the growth of the AI ecosystem on the blockchain will not reach its full potential.
Unlocking the full potential of AI on the blockchain requires a truly decentralized network. It must be able to store and process data so that complete models can run unhindered in smart contracts. Decentralized systems like ICP enable AI to function like an autonomous cloud, thus changing the landscape of AI development.
Establishing truth and trust
For example, consider an AI model designed for healthcare professionals. The model is widely used but ultimately generates untrustworthy responses. This is because there is no simple way to verify the training data on which the model is based and how that data is used.
Such centralized models only produce output without insight into the input. However, in a decentralized environment, AI language models can be built solely based on reputable medical textbooks and well-established medical research paper databases.
When doctors interact with AI, the hidden process is fully transparent, and cryptographic proofs ensure what content the AI has been trained on. As a result, the generated responses can be verified, and doctors can trust the results.
This example is just one of many examples that demonstrate the importance of decentralization in establishing trust in AI models. By operating in a completely open and public environment, AI on the blockchain ensures transparency in data processing, allowing users to understand how their data is being utilized.
In addition, on-chain AI applications can access and contribute to the same dataset, creating a collaborative ecosystem within the blockchain. The tamper-proof and secure features of the blockchain ensure that this data is not easily maliciously abused.
The collaboration between AI and blockchain provides an excellent opportunity to advance both technologies and promote more trustworthy and reliable information exchange.
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