Oort的多種工具Kaggle數據集爬到了多個類別的第一頁

由分散的AI解決方案提供商開發的人工智能培訓圖像數據集在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解決方案提供商開發的人工智能培訓圖像數據集在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數據集列表於4月初發布，此後已經看到它爬上了多個類別的第一頁。 Kaggle是一個用於數據科學和機器學習競賽，學習與協作的Google擁有的在線平台。

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的核心貢獻者Ramkumar Subramaniam認識到，前頁面的Kaggle排名是一個強烈的社交信號，這表明數據集正在吸引數據科學家，機器學習工程師和從業者的正確社區。

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的創始人兼首席執行官Max Li說，該公司觀察到有希望的參與度指標，這些指標驗證了通過分散模型收集的培訓數據的早期需求和相關性。

"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."

李說：“我們感謝卡格格爾社區的積極反應。” “這項成就反映了我們團隊在開發高質量，多樣化和可訪問的AI培訓數據方面的努力和奉獻精神。”

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計劃在未來幾個月內發布多個數據集。其中包括一個車內語音命令數據集，一個用於智能主頁語音命令，另一個用於DeepFake視頻，旨在改善AI驅動的媒體驗證。

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.

所涉及的數據集經過獨立驗證，可以在本月早些時候到達Kaggle的General AI，零售與購物，製造業和工程類別的第一頁。在發佈時，它在5月6日和5月14日的另一項可能無關的數據集更新之後丟失了這些職位。

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

Subramaniam認識到這一成就，這不是現實領養或企業級質量的明確指標。

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

設置OORT數據集的原因不僅是排名，而且是數據集背後的出處和激勵層。

"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說：“在一個圖像稀缺和中毒技術正在增加，可驗證和社區化/激勵數據集的世界中，數據集比以往任何時候都更有價值。” “這樣的項目不僅可以成為替代方案，而且可以成為數據經濟中AI一致性和出處的支柱。”

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發布的數據估計，人類生成的文本AI培訓數據將在2028年耗盡。壓力足夠高，以至於投資者現在正在調解授予AI公司版權材料的權利的交易。

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.

關於越來越稀缺的AI培訓數據及其如何限制空間增長的報告已經循環了多年。雖然綜合（AI生成的）數據越來越多地使用至少一定程度的成功使用，但人類數據仍在很大程度上被視為更好的替代性，更高質量的數據，從而導致更好的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.

專門針對人工智能培訓的圖像，藝術家故意破壞培訓工作，以保護其圖像免於未經許可，而無需允許，事情就變得越來越複雜。

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

一個這樣的項目“ Nightshade”允許用戶“毒化”其圖像並嚴重降低模型性能。

"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說：“我們進入一個時代，高質量的圖像數據將變得越來越稀缺，在這種情況下，諸如Oort的數據集（如Oort's）比以往任何時候都更有價值。”

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.

在這種情況下，OORT數據集是來自各個領域的各種圖像的集合，包括食品，時尚，建築，技術和藝術，這些圖像是根據CC BY-4.0許可發布的，並通過標記的眾庫籌集活動收集。

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.

該項目旨在提供一個平衡且全面的數據集，該數據集可用於訓練各種任務的圖像識別模型，例如對象檢測，圖像分割和圖像生成。

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.

該計劃是通過2021年初的代幣產品資助的，並看到了區塊鏈社區成員的參與，他們提供了圖像貢獻以換取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.

該項目的奉獻者收集並格式化了這些圖像，並於4月初在Kaggle發行。它在發布後一個月內到達了多個類別的第一頁。

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

領先的AI和區塊鏈出版物和網站也認可了OORT數據集，進一步強調了其重要性和創新。

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該內容不是財務建議，不一定代表CCNR的觀點，也不應將其視為認可。