There’s no denying that the rapidly maturing artificial intelligence (AI) industry has been fraught with many critical challenges since its inception. For instance, in 2022, the cost of poor software quality in the United States escalated to a staggering $2.41 trillion — with AI failures found to have contributed significantly to this problem.
And, despite the AI market’s projected contribution of up to $15.7 trillion to the global economy by 2030, the current technological infrastructure remains deeply problematic. For instance, most contemporary AI systems have remained highly centralized, with the landscape being overwhelmingly controlled by a handful of tech giants, including Microsoft, Google, OpenAI, and Amazon.
This concentration of power has had profound implications for innovation, particularly from an ethical standpoint, creating significant barriers to transparent, accountable, and democratized technological development.
In this context, Polyhedra Network’s EXPchain platform promises to transform how technologists perceive and interact with AI. By offering a comprehensive tech stack, the project helps build trust, ensure privacy, and create accountability in AI systems.
Furthermore, as AI systems stand to become increasingly influential in facilitating critical decisions across different industries — from financial lending to medical diagnostics — EXP Chain has devised a verifiable/trustworthy solution that can decentralize and make complex AI processes highly transparent.
A lack of trust and its repercussions
Traditional approaches to AI regulation have struggled to balance the competing demands of security, privacy, and operational transparency. For instance, the European Union’s AI Act — approved earlier this year — introduced stringent compliance protocols, which if not adhered to, could result in a penalty of up to €35 million.
Similarly, California’s SB 1047 Bill has proposed legislation that imposes penalties on AI model developers if they are found to not be sourcing their training data in a transparent/ethical manner. The penalties start with 10% for first-time offenses and escalate to 30% for subsequent violations.
EXPchain addresses these fundamental concerns through its innovative zero-knowledge machine learning (zkML) framework. It enables mathematical verification of AI systems without compromising sensitive data or proprietary models — creating an unprecedented level of accountability that can restore confidence in AI-driven technologies which has been waning recently.
To this point, recent statistics show that nearly 71% of the global workforce is currently concerned about the rapidly increasing adoption of AI while 80% of respondents (from another survey) believe that existing AI offerings aren’t secure and thus cannot be trusted with confidential data.
What EXPchain brings to the table
One of EXPchain’s core USPs lies in its novel ‘Expander Proof System,’ which offers unprecedented computational efficiency for zero-knowledge proofs (zkPs). With performance metrics that include processing a single image in 2.2 seconds on a single-thread CPU and handling 150 seconds per token for large language models, the Expander module offers a substantial leap in verification technology.
Similarly, the platform’s zkPyTorch toolkit is designed to simplify the integration of zk machine learning into many of today’s popular developmental workflows. By automating the conversion of PyTorch operations into zero-knowledge circuits, developers can implement sophisticated AI verification schemes without requiring any specialized cryptographic expertise.
The Polyhedra team believes that such an approach can help developers reduce their work times from months to mere days — all while providing them with complete compatibility with today’s most popular ML models.
Another one of EXPchain’s critical innovations lies in its Proof of Intelligence (PoI) framework, which creates an immutable chain of trust for AI models. To elaborate, it cryptographically links each model’s provenance and performance to a verifiable on-chain record, transparency in AI ecosystems.
As a result, organizations can validate the authenticity and ethical compliance of their proprietary AI systems with a level of certainty and precision which was previously impossible.
Applications and future outlook
From the outside looking in, EXPchain’s potential spans diverse sectors. For example, within the realm of financial services, the platform can help facilitate cryptographically secure transaction verification and private credit score assessments. Similarly, healthcare providers can leverage zkML to perform diagnostic model verifications while maintaining patient data confidentiality.
The technology can also seamlessly expand into creative domains like digital entertainment, where verifiable computational processes can authenticate digital assets and personalize user experiences without compromising privacy. Smart city infrastructures could also benefit from transparent resource allocation algorithms, while scientific researchers could see improved data sharing and fraud prevention mechanisms thanks to EXPchain.
Looking ahead, the Polyhedra team seems to working toward fulfilling an ambitious roadmap, one that includes the development of distributed proof systems for real-time verification, zk-friendly quantization for efficient AI model deployment, and multi-GPU support to scale performance for large AI models.
Thus, as AI continues to permeate every aspect of human experience, offerings like EXPchain will be crucial in maintaining public trust, protecting individual privacy, and ensuring that the development of this yet nascent technology happens in a responsible and transparent fashion.
