The application of artificial intelligence is expanding at an unprecedented rate, but the decentralized AI (DeAI) sector is hitting a critical stumbling block: the lack of diverse, secure, and verifiable data. Existing onchain datasets are simply too limited to train robust AI models. This data scarcity threatens to leave the future of AI in the hands of centralized powerhouses with unrestricted access to the internet’s vast data repositories.
The potential of DeAI lies in its ability to democratize, increase transparency, and strengthen AI. However, this potential can only be realized if the existing data gap is bridged. The solution to this problem may be found in innovative cryptography techniques.
Traditional AI models operate on a simple principle: the more data they can consume, the more intelligent they become. However, this strength also exposes a significant weakness. Centralized AI models are often trained on data collected without explicit consent, raising significant concerns about privacy and control. DeAI, founded on blockchain’s principles of decentralization and transparency, offers an ethical alternative.
However, the majority of onchain data comes from financial transactions or DeFi. For smaller language models, more specific data is required for fine-tuning. This lack of diverse and rich datasets leaves DeAI models wanting for the resources necessary to compete with traditional models.
Large data repositories, like The Pile and Common Crawl, exist outside of web3 and contain information from billions of sources. These data resources, both in terms of depth and volume, have allowed centralized AI providers to refine their models rapidly and extensively. Replicating this level of data onchain is not feasible within a competitive timeframe.
Cryptography offers a viable solution, specifically through the application of zero-knowledge proofs. These cryptographic techniques have already shown promise in improving blockchain scalability and privacy. Two methods – zero-knowledge fully homomorphic encryption (zkFHE) and zero-knowledge TLS (zkTLS) – could be the key to unlocking web2’s data for DeAI.
zkFHE permits computations on encrypted data without the need for decryption. For instance, an AI model could be trained on sensitive medical records without exposing the raw patient data. This expands the training possibilities for DeAI models exponentially.
zkTLS extends this principle to internet communication. It allows users to prove possession of specific data from a website, such as a credit score or social media activity, without revealing the underlying information. This is vital for integrating the wealth of data in web2’s silos into DeAI systems.
By combining zkFHE and zkTLS, DeAI could access the vast data resources of web2 while maintaining privacy and decentralization. This could equalize the competition, enabling DeAI to compete with, and potentially surpass, centralized AI.
However, implementing zkFHE and zkTLS is a complex process, requiring substantial advancements in hardware and software. Standardization and interoperability are also essential for broad adoption. Nevertheless, the potential benefits are substantial.
In the race for AI dominance, data is the most valuable resource. By incorporating cryptographic solutions like zkFHE and zkTLS, DeAI could access the necessary resources to compete effectively. This is not just about building smarter AI; it’s about building a more democratic and equitable AI future.
