Subnet 49
Hivetrain AutoML
Hivetrain.ai
Hivetrain’s AutoML Loss Subnet automates neural network advancements, discovering novel loss functions and algorithms
SN49 : AutoML
| Subnet | Description | Category | Company |
|---|---|---|---|
| SN49 : AutoML | Neural network optimization | Infrastructure | Hivetrain |
Hivetrain’s Incentivized AutoML Loss Subnet is a collaborative platform aimed at advancing deep learning by automating the discovery of novel neural network components, such as innovative loss functions, activation functions, and potentially new algorithms that exceed current state-of-the-art standards. Drawing inspiration from the AutoML Zero paper, the platform employs genetic programming combined with evolutionary and gradient-based optimization to evolve increasingly complex mathematical functions.
Hivetrain’s AutoML Subnet signifies a transformative shift in AI development, aiming to redefine how AI systems evolve. Their vision extends beyond conventional AutoML by automating the search and optimization of neural network components like loss and activation functions, as well as potentially entire algorithms. Inspired by the AutoML Zero approach, Hivetrain applies evolutionary algorithms to enable AI systems to participate in their own evolution.
Instead of merely improving existing components, they are broadening the scope of AI exploration itself, allowing the system to search for innovative solutions that human intuition may overlook. This machine-centered approach to research takes AI beyond human-designed limits, toward systems capable of self-directed optimization.
Laying Groundwork for Artificial Superintelligence
By starting with elements such as loss functions, Hivetrain envisions a self-improving AI framework—one that evolves autonomously over time, which is an essential step toward Artificial Superintelligence (ASI). Their network-based approach leverages both miners and validators, creating a distributed search effort that advances AI in ways that individual teams cannot.
Pioneering Self-Improving AI
This subnet is not just about incremental improvements; it’s about building a sustainable, autonomous AI ecosystem. Hivetrain’s ultimate goal is to facilitate continuous self-improvement within AI systems, paving the way for innovation that requires minimal human intervention.
Contribution to a New Frontier in AI
Participating in Hivetrain’s subnet is about contributing to a paradigm shift in AI development. Each participant, whether miner or validator, is part of a broader effort to build self-evolving AI systems that will eventually surpass human-devised components.
Current Focus on Loss Function Search
Hivetrain’s initial focus is on discovering superior loss functions for neural networks. Loss functions are crucial for guiding deep learning model training by quantifying the difference between predicted outputs and targets. Customized loss functions can enhance model learning from complex datasets, boost convergence rates, and improve generalization on new data.
Loss function search holds particular value in addressing unique challenges in deep learning, such as class imbalance, noise, and varied data distributions. Through genetic algorithms and AutoML, this subnet aims to automate the discovery of innovative loss functions that outperform standard ones, pushing the boundaries of deep learning in diverse industries like healthcare and autonomous systems, where precision and reliability are essential.
By contributing to this subnet, participants help to lay the foundations of a truly self-improving AI ecosystem that redefines the future of artificial intelligence.
Hivetrain’s diverse team unites experts in AI, machine learning, and ethics, leveraging years of experience from academia, industry, and open-source to innovate in artificial intelligence.
@bitcurrent – Bizops & strategy
@mekaneeky – AI nerd
@kobetryz12 – Data nerd
@alexdrocks – Web crafter/consultant