), a trailblazer in artificial intelligence, announces the rollout of its distributed AI training protocol, poised to revolutionize global access and collaboration in AI development. Embracing DePIN's decentralized framework, NeuroMesh bridges the gaps between the demand for training large AI models and distributed GPUs. This initiative aims to foster inclusivity in AI development, facilitating participation across diverse sectors and geographies.
NeuroMesh transcends traditional AI by fostering collaboration. Their vision is to equip every developer and organization, regardless of location or resources, with the ability to train and utilize cutting-edge AI models. This aligns perfectly with the vision of AI pioneers like Yann LeCun, who advocate for a future powered by crowdsourced and distributed AI training.
The PCN Training Algorithm: The magic behind NeuroMesh lies in the PCN training algorithm. Unlike traditional backpropagation methods, PCN enables fully local, parallel, and autonomous training. The team aims to create a vast network, where each node — representing a participating GPU — learns independently. PCN minimizes inter-layer communication, reducing data traffic and facilitating asynchronous training.
This cutting-edge model, inspired by recent advancements in neuroscience research pioneered by Oxford University, mimics the human brain's localized learning approach. By storing error values and optimizing for a local target in each layer, it replicates the behavior of brain neurons.
Individuals, projects with GPU resources, and entities with training needs are all welcome to join this transformative initiative. For comprehensive details on NeuroMesh and to participate in this leading-edge endeavor, users can visitcomprises researchers and engineers from esteemed institutions such as Oxford, NTU, PKU, THU, HKU, Google and Meta.
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