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No central for OORT AI TOP data in Google Kagge

The AI ​​-developed intelligence Training Training Data set has witnessed great success on the Google Kagge platform.

Various OORT data collection Kagge existing It was released in early April; Since then, he ascended to the first page in multiple categories. Kaggle is an online platform owned by Google for data science competitions, machine learning, learning and cooperation.

“The kaggle classification on the first page is a strong social signal, indicating that the data group participates in the appropriate societies of data scientists, automatic learning engineers and practitioners,” Ramkomar Supramamiam, the primary contributor to the Crypto Ai OpenLedger project, told Cointelegraph.

“I noticed the standards of promising participation that achieves the validity of early demand and importance” for the training data collected through a central model. He added:

“The organic interest of society, including active use and contributions-how to lines of non-central data pipelines that depend on society such as ORT can achieve rapid distribution and participation without relying on central intermediaries.”

He also told me that in the coming months, Ort plans to issue multiple other data groups. Among these is a set of audio orders data inside the car, one for smart home voice commands and the other for DeepFake videos that aim to improve the verification of the acting media.

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The first page in multiple categories

The data set has been independently verified by Cointelegraph to reach the first page in the AI’s public categories in Kagge, retail, shopping, manufacturing, and engineering earlier this month. At the time of publication, these attitudes were lost after updating the unrelated data set on May 6 and the other on May 14.

OORT data collection on the first Kaggle page in the engineering category. source: Kaggle

While realizing the achievement, he told Subramaniam Cointelegraph that “it is not a final indication of adopting the real world or the quality of the institution.” He said that what distinguishes the Ort data collection “not only the arrangement, but the incentive layer and the incentive layer behind the data set.” Clear:

“Unlike the central sellers who may depend on the non -dark pipelines, it provides a transparent and improved system with the distinctive symbol of tracking, coordination of society, and the possibility of continuous improvement on the assumption that the correct governance exists.”

Although he does not think these results are difficult to repeat, “it shows that encryption projects can use decentralized incentives to regulate economic value.”

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High quality training data from artificial intelligence: a rare commodity

Data Published Through the AI ​​AI, AI AI estimates that the text training data created by man will be exhausted in 2028. The pressure is high enough because the investors are now Mediation Definitions of giving rights to copyrights to artificial intelligence companies.

Reports related to artificial intelligence training data were Trading For years. Although artificial data (created) is increasingly used with at least a degree of success, human data is still viewed largely as better alternative data of high quality that better leads to Amnesty International models.

When it comes to the pictures of artificial intelligence training specifically, things are increasingly complicated with artists who intentionally destroy training efforts. It aims to protect their photos from using them to train artificial intelligence without permission, Nightshade It allows users to “poison” their photos and reduce the performance of the model severely.

Performing the model for each number of poisoned images. source: Towards science

“We are entering an era as high -quality image data will be increasingly rare,” said. I also realized that this scarcity has become more clear due to the increased popularity of poisoning with pictures:

“With technologies such as hiding images and hostile watermarks for AI training, open source data groups face a dual challenge: quantity and confidence.”

In this case, Subramaniam said that the stimulating data groups in society and the preservation of society are “more valuable than ever.” According to him, such projects “cannot become just alternatives, but pillars of artificial intelligence alignment and a source of data economy.”

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