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A simple glossary to help you wade through, for more detailed and precise info, please read the respective pages. :-) |
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Pundi AI - Pundi AI is the umbrella brand for all the products built by Pundi AI, which includes the Pundi AI Data Platform, Pundi AI Data Marketplace, Pundi AIFX Omnichain Layer, and Purse+ SocialAI Browser Plugin.
Pundi AI Data Platform - The Pundi AI Data Platform is a decentralized platform allowing individuals and companies to upload datasets and have them annotated by anyone from around the world, making it an open data ecosystem for AI. Users can contribute data or annotate and analyze existing datasets in exchange for payment. This system aims to resolve the shortage of clean, high-quality data, which is critical for training AI models.
Pundi AI Data Marketplace - Slated for launch in 2025, the Pundi AI Data Marketplace will allow users to buy and sell high-quality, annotated datasets. The platform will ensure compliance with copyright and GDPR regulations, providing a secure environment for data exchange.
Pundi AIFX Omni layer - The Pundi AIFX omni layer is a Layer 1 blockchain that will support the entire Pundi AI ecosystem, providing a decentralized, blockchain-based infrastructure for data processing and sharing, without users having to interact directly with the complexities of blockchain technology. It is used for Dapps to launch on others chains such as Base and Pundi AIFX abstracts away the complexities including handling dat storage.
Purse+ SocialAI Browser Plugin - A tool already being used by 25,000 users to analyze sentiment on social media (specifically Twitter/X) and earn stablecoins for their contributions, the Purse+ Browser Plugin allows users to tag and analyze sentiment data from tweets. The plugin enables companies to gather insights and make data-driven decisions.
Data Tagging/Annotation - Data tagging/annotation is the action of adding meaningful and informative tags to a dataset, making it easier for machine learning algorithms to understand and process the data. Previously, data annotation was not as crucial as it is now for the reason that data scientists were using structured data which did not require many annotations. During the last 5-10 years, data annotation became more critical for machine learning systems so they can work effectively.