art_b4

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@awaoriginal @opensea very cool collection 💯🔥

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@ThisIsJoules Awesome

Decentralized data labeling is a difficult problem to solve. AI models require scale, accuracy, and diversity, but centralized approaches are costly, constrained by limited resources, and unable to scale across every domain. At the same time, decentralized systems introduce incentive misalignment, Sybil attacks, and quality control challenges, making it difficult to ensure contributors prioritize accuracy over maximizing rewards. Season 1 of our Data Services Platform was our first proof of concept to test whether a decentralized approach could work at scale while maintaining data integrity. Turns out, it works! Our peer review system effectively filtered out 17% of submitted datapoints for simple research tasks and 33% of submitted datapoints for in-depth research tasks, demonstrating its ability to reject low-quality data while preserving dataset reliability. This validation step is crucial—data is only useful if it is accurate. Among these, 92% of the peer-reviewed submissions met internal QA standards, proving that when contributors are properly incentivized and the right guardrails are in place, decentralized data collection can be both scalable and reliable. Another key takeaway is the varying value of different types of datasets. Research tasks had higher acceptance rates because they only required retrieving publicly available information or submitting personal experiences. While this data is useful, it's not particularly scarce—models can often ingest similar data from web scraping. However, unlike raw scraped data, these datapoints went through peer validation, which improves accuracy and ensures relevance, giving them a step up in comparison. More complex datasets, on the other hand, are far more difficult to source because they require contributors to generate new, high-value datapoints that don’t already exist in structured form. This became clear in our more advanced, technical tasks: while only 10% of jailbreak prompt task submissions were accepted, this still yielded 24,000+ critical datapoints essential for testing AI model safety and robustness. These types of datasets cannot simply be scraped—they must be intentionally created and validated. As we expand from 10k contributors in Season 1 to 100k in Season 2, we've taken our learnings to refine task segmentation, automate more verification steps, and implement stronger fraud detection. This is about more than just improving data labeling. It’s about redefining how datasets are built—ensuring that data quality, diversity, and integrity can scale beyond traditional methods. This work also opens the door for a more inclusive AI economy, where dataset creation is no longer monopolized by centralized players, but distributed across a global network of contributors. A system where anyone with an internet connection can participate in and benefit from AI development. The future of AI depends on high-quality, scalable data—this is how we get there. Excited for you all to read the full report. Let me know what you think! saharalabs.ai/blog/decentral…

@InternTerry Try

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The Beatles won Best Rock Performance Grammy Award for “Now and Then”. The engineers used AI to clean up John Lennon’s vocal to make this happen! As a fan this is just too cool 🫡 What applications of AI you look forward to see? Leave it in the comments! #GRAMMYs

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Introducing Humanity Foundation 🖐️ We are proud to announce the launch of Humanity Foundation - a crucial step towards crafting a secure, privacy-focused, human-centric digital future. Everything you need to know ⬇️