Oort的多种工具Kaggle数据集爬到了多个类别的第一页

由分散的AI解决方案提供商开发的人工智能培训图像数据集在Google的平台Kaggle上取得了巨大的成功。

An artificial intelligence training image data set developed by decentralized AI solution provider OORT has seen considerable success on Google’s platform Kaggle.

由分散的AI解决方案提供商开发的人工智能培训图像数据集在Google的平台Kaggle上取得了巨大的成功。

OORT’s Diverse Tools Kaggle data set listing was released in early April and has since seen it climb to the first page in multiple categories. Kaggle is a Google-owned online platform for data science and machine learning competitions, learning and collaboration.

Oort的不同工具Kaggle数据集列表于4月初发布，此后已经看到它爬上了多个类别的第一页。 Kaggle是一个用于数据科学和机器学习竞赛，学习与协作的Google拥有的在线平台。

Ramkumar Subramaniam, core contributor at crypto AI project OpenLedger, recognized that a front-page Kaggle ranking is a strong social signal, indicating that the data set is engaging the right communities of data scientists, machine learning engineers and practitioners.

Crypto AI Project Openledger的核心贡献者Ramkumar Subramaniam认识到，前页面的Kaggle排名是一个强烈的社交信号，这表明数据集正在吸引数据科学家，机器学习工程师和从业者的正确社区。

Max Li, founder and CEO of OORT, said that the firm observed promising engagement metrics that validate the early demand and relevance of its training data gathered through a decentralized model.

OORT的创始人兼首席执行官Max Li说，该公司观察到有希望的参与度指标，这些指标验证了通过分散模型收集的培训数据的早期需求和相关性。

"We're grateful for the positive response from the Kaggle community," said Li. "This achievement reflects the hard work and dedication of our team in developing high-quality, diverse, and accessible AI training data."

李说：“我们感谢卡格格尔社区的积极反应。” “这项成就反映了我们团队在开发高质量，多样化和可访问的AI培训数据方面的努力和奉献精神。”

OORT plans to release multiple data sets in the coming months. Among those is an in-car voice commands data set, one for smart home voice commands and another for deepfake videos meant to improve AI-powered media verification.

OORT计划在未来几个月内发布多个数据集。其中包括一个车内语音命令数据集，一个用于智能主页语音命令，另一个用于DeepFake视频，旨在改善AI驱动的媒体验证。

The data set in question was independently verified to have reached the first page in Kaggle’s General AI, Retail & Shopping, Manufacturing, and Engineering categories earlier this month. At the time of publication, it lost those positions following a possibly unrelated data set update on May 6 and another on May 14.

所涉及的数据集经过独立验证，可以在本月早些时候到达Kaggle的General AI，零售与购物，制造业和工程类别的第一页。在发布时，它在5月6日和5月14日的另一项可能无关的数据集更新之后丢失了这些职位。

Recognizing the achievement, Subramaniam said that it’s not a definitive indicator of real-world adoption or enterprise-grade quality.

Subramaniam认识到这一成就，这不是现实领养或企业级质量的明确指标。

What sets OORT’s data set apart is not just the ranking, but the provenance and incentive layer behind the data set.

设置OORT数据集的原因不仅是排名，而且是数据集背后的出处和激励层。

"In a world where image scarcity and poisoning techniques are increasing, verifiable and community-sourced/incentivized data sets become more valuable than ever," said Subramaniam. "Such projects can become not just alternatives, but pillars of AI alignment and provenance in the data economy."

Subramaniam说：“在一个图像稀缺和中毒技术正在增加，可验证和社区化/激励数据集的世界中，数据集比以往任何时候都更有价值。” “这样的项目不仅可以成为替代方案，而且可以成为数据经济中AI一致性和出处的支柱。”

Data published by AI research firm Epoch AI estimates that human-generated text AI training data will be exhausted in 2028. The pressure is high enough that investors are now mediating deals granting rights to copyrighted materials to AI companies.

AI研究公司Epoch AI发布的数据估计，人类生成的文本AI培训数据将在2028年耗尽。压力足够高，以至于投资者现在正在调解授予AI公司版权材料的权利的交易。

Reports concerning increasingly scarce AI training data and how it may limit growth in the space have been circulating for years. While synthetic (AI-generated) data is increasingly used with at least some degree of success, human data is still largely viewed as the better alternative, higher-quality data that leads to better AI models.

关于越来越稀缺的AI培训数据及其如何限制空间增长的报告已经循环了多年。虽然综合（AI生成的）数据越来越多地使用至少一定程度的成功使用，但人类数据仍在很大程度上被视为更好的替代性，更高质量的数据，从而导致更好的AI模型。

When it comes to images for AI training specifically, things are becoming increasingly complicated with artists purposely sabotaging training efforts to protect their images from being used for AI training without permission.

专门针对人工智能培训的图像，艺术家故意破坏培训工作，以保护其图像免于未经许可，而无需允许，事情就变得越来越复杂。

One such project, Nightshade, allows users to "poison" their images and severely degrade model performance.

一个这样的项目“ Nightshade”允许用户“毒化”其图像并严重降低模型性能。

"We're entering an era where high-quality image data will become increasingly scarce, and in this situation, verifiable and community-sourced/incentivized data sets like OORT's are more valuable than ever," said Subramaniam.

Subramaniam说：“我们进入一个时代，高质量的图像数据将变得越来越稀缺，在这种情况下，诸如Oort的数据集（如Oort's）比以往任何时候都更有价值。”

In this case, the OORT data set is a collection of diverse images from various domains, including food, fashion, architecture, technology, and art, which are released under a CC BY-4.0 license and collected via a tokenized crowdsourcing campaign.

在这种情况下，OORT数据集是来自各个领域的各种图像的集合，包括食品，时尚，建筑，技术和艺术，这些图像是根据CC BY-4.0许可发布的，并通过标记的众库筹集活动收集。

The project aims to provide a balanced and comprehensive data set that can be used to train image recognition models for various tasks, such as object detection, image segmentation, and image generation.

该项目旨在提供一个平衡且全面的数据集，该数据集可用于训练各种任务的图像识别模型，例如对象检测，图像分割和图像生成。

The initiative was funded through a token offering in early 2021, and saw participation from members of the blockchain community, who provided image contributions in exchange for OORT tokens.

该计划是通过2021年初的代币产品资助的，并看到了区块链社区成员的参与，他们提供了图像贡献以换取Oort令牌。

The project's Devotees collected and formatted the images, and they were finally released on Kaggle in early April. It reached the first page in multiple categories within a month of release.

该项目的奉献者收集并格式化了这些图像，并于4月初在Kaggle发行。它在发布后一个月内到达了多个类别的第一页。

The OORT data set has also been recognized by leading AI and blockchain publications and websites, further highlighting its significance and innovation.

领先的AI和区块链出版物和网站也认可了OORT数据集，进一步强调了其重要性和创新。

This content is not financial advice and does not necessarily represent the views of CCNR and should not be viewed as an endorsement.

该内容不是财务建议，不一定代表CCNR的观点，也不应将其视为认可。