Could Decentralized Protocols Revolutionize Machine Learning Infrastructure?

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With the surging interest in artificial intelligence (AI) and machine learning (ML), there's been a strain on hardware resources and soaring costs associated with cloud services. However, the emergence of decentralized infrastructure poses a potential challenge to the dominance of centralized players.

During the ETHGlobal event in London, Harry Grieve, the co-founder of Gensyn, a machine learning compute network, shared insights with Cointelegraph regarding the transformative potential of peer-to-peer computing networks in challenging Web2 services such as Amazon Web Services.

"Gensyn holds significant potential as the internet changes to a more dynamic representation of information that will empower 'self-sovereignty and computational liberty online,'" stated Grieve during the interview.

Gensyn, an upcoming decentralized network, aims to facilitate connections to diverse devices across the internet for training machine learning models. Supported by several Web3 venture capital firms and backed by a $50 million investment from Andreessen Horowitz in 2023, Gensyn holds significant promise according to Grieve.

"We realized that if you want to build this in a decentralized way, you need a way to reach a decentralized consensus about who did what. That’s basically all a blockchain is," explained Grieve, articulating the challenges they encountered while developing the network.

Grieve articulates these challenges as follows: "How can you peer with another device and train a machine learning model on that device where A), the device is untrusted? B), your training model can’t fit on that single device. And C, you want the achievable scale of the entire system and unit economic outcomes as good as AWS."

"Gensyn's lite paper outlines the protocol as 'a layer-1 trustless protocol for deep learning computation,' wherein participants are immediately rewarded for providing computing resources and executing ML tasks," Grieve elaborated.

However, Grieve highlights a significant challenge in verifying completed ML work, which necessitates a convergence of complexity theory, game theory, cryptography, and optimization. He draws inspiration from Satoshi Nakamoto's Bitcoin protocol, likening Gensyn's aspirations to the early days of Bitcoin mining when individuals could generate BTC using relatively modest hardware.

"Satoshi gave people the right to their money again; they could generate it from a laptop. They could convert electricity into money. They could convert fiat into something harder," remarked Grieve, emphasizing the potential democratizing impact of decentralized networks like Gensyn.

While Gensyn's ultimate vision is to democratize access to computing resources for ML training, the initial focus will be on users with ample graphics processing units (GPUs) due to their efficiency. Grieve envisions a future where even individuals with standard laptops can contribute to the network seamlessly.

"We are thoroughly planning on people building on top of Gensyn to make more user-friendly, supply-side, and contribution applications. Ultimately, an individual with a laptop will be able to download our client and run it in a way that connects you to the network," Grieve shared.

Moreover, advancements in hardware, particularly Apple's Silicon chips, offer a potential boon to protocols like Gensyn. Research indicates that Apple's M2 and M3 chips rival mid-tier Nvidia RTX GPUs, potentially expanding the pool of devices contributing to Gensyn's global supercluster.

"I think people are more likely to have something that looks like a MacBook than a standalone GPU, so it’s a much more like democratizing force if you can contribute that," Grieve commented on the potential impact of Apple Silicon chips on decentralized networks like Gensyn.

Grieve also underscores the versatility of Apple Silicon chips and their potential to be emulated by other manufacturers, thereby further diversifying the network's hardware landscape.

As decentralized networks like Gensyn and continue to explore the integration of innovative hardware, the future of machine learning infrastructure appears poised for a significant transformation.

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