Training AI data models requires access to large numbers of diverse and high-quality human trainers.
Intermediaries in the AI Training market retain much of the cost that should go to the trainers. Training tasks are repeated many times in order to achieve the minimal quality levels required for accurate AI models
With more industries adopting AI, trainers with specialized expertise are required to provide training data for industry-specific AI models
The 2 Billion “un-banked” currently cannot participate in AI training work, exacerbating the scarcity of trainers, reducing diversity of AI model input, and barring entire populations from participating in the AI revolution
Matching AI projects directly with communities of trainers, projects are able to tap into global groups of domain experts while negating the need to pay high premiums to intermediaries
ALE community coordination software toolsets provide scalable community incentives that create long-term sustainable solutions for AI dataset creation. ALE-powered crowd wisdom allows ALE communities to scale and benefit all community members equitably
Reputation-based compensation and gamification mechanics reduce the need for massive repetition of training tasks found in traditional settings, increase engagement and improve the quality of training data output
Open to the entire world population, ALE enables all to meaningfully contribute to and participate in the AI revolution, while massively expanding and diversifying the pool of trainers projects can tap into for their AI training needs
With over 100 oft-cited research papers and several books on reputation-based systems, AI and decentralized governance, Wulf is the pre-eminent expert in community-based AI training
$ALE powers the Al Learning Ecosystem, which is disintermediating and decentralizing a $30B market.
$ALE’s unique and multi-faceted staking mechanics provide for network security, growth and AI training accuracy
Half of $ALE's annual inflation will be paid to ALE Communities based on their relative network contribution, and to each ALE Worker based on their reputation, ensuring network growth, and accurate training work