Berkeley RDI and Polyhedra have made a strategic collaboration to unveil a groundbreaking Production-Ready zkML. This production aims to mark a transformative leap in the fusion of AI and cryptographic verification. The organizations are now delivering a real-world application, four years after pioneering the concept. The application aims to empower AI developers to utilize zkML without requiring specific expertise in zero-knowledge proofs.
The journey began in 2020 when Polyhedra’s research team, including Jiaheng Zhang, Dawn Song, and Yupeng Zhang published a paper in collaboration with Berkeley RDI. The paper was titled “Zero Knowledge Proofs for Decision Tree Predictions and Accuracy”, introducing a zkML, zero-knowledge machine learning. The concept of the paper aimed at building trust in AI by ensuring verifiable results while maintaining the privacy of underlying data and models.
Polyhedra prioritizes trustless systems to minimize human error in technology incidents. The zkML model, at its core, allows the developers to prove the accuracy of AI model predictions on a specific data sample without exposing any sensitive information.
zkML Polyhedra: Advancing Trust and Transparency in AI
Through zero-knowledge proofs, a service provider can demonstrate that a specific output was genuinely produced by running a given model on an input. The zkML technology provides an effective solution to one of AI’s most pressing challenges, which are trust and transparency. The zkML model, by enabling verifiable computations, ensures that the results generated by AI models are accurate and trustworthy. This approach addresses concerns about the opaque nature of AI, where unverified outputs can lead to flawed decisions.
The CTO of the organization, Tiancheng Xie shared that: “We have spent the entire life of the company building systems that can operate without human intervention, that are verified by math, and are cryptographically secure.”
Applications of zkML extend beyond inference verification, encompassing areas such as data origin authentication, accurate data labeling, and verification of AI training processes. This guarantees that every step of the AI lifecycle adheres to strict standards of integrity.
The zkML model eliminates high computational barriers, built on the innovative Expander-proof system, making production-ready solutions a reality. This advancement not only strengthens trust in AI but also ensures compliance with privacy regulations.
The Future of Verifiable AI
Dawn Song, Director of Berkeley RDI highlights: “Berkeley RDI and Polyhedra are setting a new standard for trust and transparency in artificial intelligence with innovative zero-knowledge machine learning (zkML) technology, a groundbreaking approach combining machine learning with cryptographic verification.”
This collaboration sets a new benchmark for transparency and accountability in AI. Song emphasized that the goal is to ensure the efficiency brought by AI doesn’t compromise trust and safety.
Looking ahead, zkML is poised to transform the AI landscape by enabling secure deployment, decentralized ecosystems, and innovative applications. With zkML, Berkeley RDI, and Polyhedra aim to build a future where trust is at the heart of AI innovation.
