Modulus Raises $6.3M to Bring Crypto Security to Artificial Intelligence

A project that aims to combine two of the world’s riskiest technologies has successfully raised $6.3 million in a seed round, the company announced on Wednesday. The company announced on Wednesday.

Known as Modulus Labs, the project, like many startups, originated in the prestigious halls of Stanford University. Co-founder Daniel Shore, like many 20-somethings during the pandemic, couldn’t resist the allure of the crypto space, which led to the start of Modulus. But unlike many players drawn to trading platforms, Shore and his co-founders were diving into crypto whitepapers with a particular focus on zero-knowledge cryptography.

“We found ourselves in a position to marry the two,” Shore told The Block from a cramped WeWork office surrounded by pink sticky notes.

The concept behind the module was to use zero-knowledge proofs, which provide a cryptographic technique that enables the validation of the integrity of a thing without revealing any additional underlying raw information. Co-founder Shorr draws a parallel, likening the approach to a “blue check” for AI systems reminiscent of X (formerly known as Twitter).

Indeed, as the line between real and artificial AI becomes increasingly blurred, Modulus is establishing itself in the market, with recent headlines using deepfakes to influence public opinion about the ongoing war in the Middle East between Israel and Hamas. The module, in particular, uses ZK-proof, specifically zkML, to assure users that AI queries remain unchanged or tampered with, thus paving the way for a wider variety of web3 applications to incorporate AI.

Bridging the gap between artificial intelligence and blockchain

As explained by Variant public partner Jesse Walden, the project bridges the gap between the opacity of machine language models running on servers and the transparency of the blockchain, thus allowing more advanced decentralized protocols to eliminate the need for human governance of complex operations. and minimize the dynamic. “

So what does it mean in practice? The project has spilled a lot of ink on the potential implications.

“It brings dApps closer to parity in features with their centralized counterparts, bringing recommendations and matching algorithms to Web3,” the project wrote in a recent Medium post.

“This could be used for NFT markets to better respond to the wallet owner based on the NFTs that currently own the wallet,” the blog added. It can be used as an automated and trusted Oracle to validate off-chain data. And most interestingly for us, it can enable new use cases that would have been impossible without on-chain AI support.

There are also opportunities for tokenomics improvements “such as improving the quality of a model on-chain and paying tokens for improvements.”

In addition, there are more unconventional prospects, such as the possibility of setting up a decentralized autonomous organization driven by artificial intelligence, a concept that may seem far-fetched given the challenges often associated with governance. not be

Walden notes the potential impact on the decentralized lending market, which could use artificial intelligence to manage loan collateral ratios rather than actually rely on human decision-making, which has historically been inferior to crypto lending.

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