#AITrainingdata

Awesome Agents

@awesomeagents.bsky.social

1 week ago

Meta Signs $150M Deal to License News Corp Content for AI Meta will pay News Corp up to $50 million per year for three years to license Wall Street Journal and other content for Meta AI training and chatbot responses.

Meta Signs $150M Deal to License News Corp Content for AI

awesomeagents.ai/news/meta-150m-news-corp...

#Meta #NewsCorp #AiTrainingData

Ars Technica News

@arstechni.ca

1 week ago

Musk fails to block California data disclosure law he fears will ruin xAI https://arstechni.ca #AItrainingdata #FirstAmendment #California #chatbots #ElonMusk #Policy #grok #xAI #AI

Awesome Agents

@awesomeagents.bsky.social

2 weeks ago

They Trained on Everything: How AI Labs Consumed the World's Books and Why They're Coming for the Rest From pirated libraries to destroyed books to ancient manuscripts, AI companies have consumed millions of copyrighted works and are now approaching the limits of available human text. Here is what they used, what they stole, and what they are looking for next.

They Trained on Everything: How AI Labs Consumed the World's Books and Why They're Coming for the Rest

awesomeagents.ai/news/ai-training-data-bo...

#AiTrainingData #Copyright #Books

Ed "Zorz" Hafizov

@zorzstudios.com

3 weeks ago

Amazon may launch #marketplace for publishers to sell content to AI firms: zorz.it/FPpXK

#PesalaBandara #AITrainingData #Amazon #ArtificialIntellgence #publisher #AIFirms #content #technology

CySecurity News

@cysecuritynews.bsky.social

1 month ago

Snap Faces Lawsuit From Creators Over Alleged AI Data Misuse   A legal conflict between online creators and companies dedicated to artificial intelligence has entered an increasingly personal and sharper stage. In recent weeks, well-known YouTubers have filed suits in federal court against Snap alleging that the company built its artificial intelligence capabilities on the basis of their copyrighted material.  In the complaint, there is a familiar but unresolved question for the digital economy: Can the vast archives of video created by creators that power the internet be repurposed to train commercial artificial intelligence systems without the knowledge or consent of the creators?  Among the participants in the proposed class action, which was filed in the Central District Court of California on Friday, are internet personalities whose combined YouTube audience exceeds 6.2 million subscribers. According to Snap, the videos they uploaded to YouTube were scraped to be used as datasets for training AI models on Snapchat, which were scraped in violation of platform rules as well as federal copyright laws. A similar claim has previously been brought against Nvidia, Meta, and ByteDance by the plaintiffs, claiming that a growing segment of the artificial intelligence industry is relying on creator content without authorization. Specifically, the YouTubers contend that Snap was using large-scale video-language datasets, including HD-VILA-100M, developed for academic and research purposes rather than commercial applications.  The newly filed complaint specifically challenges Snap's reported use of these datasets. Upon filing the lawsuit, YouTube has asserted that any commercial use would have been subject to YouTube's technological safeguards, terms of service, and licensing restrictions. Plaintiffs argue that these limitations were bypassed in order for Snap's AI systems to incorporate the material.  In addition to statutory damages, the lawsuit seeks a permanent injunction prohibiting further alleged infringements. Among the participants are the creators of the YouTube channel h3h3, which has a subscriber base of 5.52 million, as well as the golf-focused channels MrShortGame Golf and Golfholics.  The case is one of the latest in a series of copyright disputes between users and artificial intelligence developers. Recently, publishers, authors, newspapers, artists, and user-generated content platforms have brought similar claims. As reported by the nonprofit Copyright Alliance, over 70 copyright infringement lawsuits have been filed against artificial intelligence companies to date with varying outcomes.  Several cases involving Meta and a group of authors were resolved in favor of the technology company by a federal judge. In another case involving Anthropic and authors, the company reached a settlement. Several other cases are still pending, which leaves courts with the task of defining how technological innovation intersects with intellectual property rights in our rapidly evolving age. There are a number of individuals in the U.S. who have uploaded original video content to YouTube and whose works have allegedly been incorporated into the large-scale video datasets referenced in the complaint. The proposed class entails more than just the named plaintiffs, but all U.S-based individuals who have uploaded original video content to YouTube.  According to Snap's filing, these datasets formed the foundation for the company's artificial intelligence training pipeline, enabling the company to process and ingest creator content in significant quantities. ByteDance, Meta, and Nvidia have been the targets of comparable class complaints, resulting from a coordinated legal strategy intended to challenge industry-wide data acquisition practices by the same plaintiffs.  Also requesting declaratory judgment that Snap willfully circumvented YouTube’s copyright protection mechanisms, the plaintiffs seek monetary relief along with declaratory judgment. As part of the complaint, statutory damages, costs and interest are requested, as well as an injunction to stop the continued use of the disputed video materials. There is a central claim in the complaint that Snap developed and refined its generative AI video systems by accessing and copying YouTube content en masse, despite the platform's architecture which permits controlled streaming, but does not provide access to source files for download.  Snap’s model development is attributed to specific datasets, including HD-VILA-100M and Panda-70M, cited in the complaint. According to the filing, HD-VILA-100M contains metadata that references YouTube videos rather than hosting the audiovisual files themselves. As a result, the plaintiffs maintain that Snap had to retrieve and duplicate the references directly from YouTube’s servers in order to operationalize such datasets for model training. As a result of this process, they contend that technology protection measures and access controls designed to prevent large-scale extraction and downloading were necessarily bypassed. This lawsuit alleges the use of automated tools and structured workflows to facilitate this retrieval. Moreover, the complaint claims that the datasets segmented individual YouTube uploads into multiple discrete clips, which required repeated access to the same source video as well.  According to the plaintiffs, this method resulted in millions of separate acts of copying which were essentially identical in nature. In Snapchat’s AI-powered features, those copies were allegedly used to train and enhance text-to-video and image-to-video models. In spite of license restrictions associated with certain datasets, the filing asserts that these activities were conducted for commercial deployment rather than academic or research purposes. As a final point, the plaintiffs assert Snap's conduct violated YouTube's terms of service and constituted unlawful circumvention of technological safeguards, regardless of whether particular videos had been formally registered with the U.S. Copyright Office.  Thus, the complaint positions the dispute in context not merely as a disagreement over platform rules but as a broader issue related to the legal and technical limits governing large-scale data ingestion for commercial AI development.  Depending on the outcome of the litigation, it may have implications that extend far beyond the parties involved. At stake are not only the questions of liability in a single dispute but also the broader compliance landscape that undergirds commercial AI development. In this case, the court will examine how training data is sourced, whether technical safeguards constitute enforceable measures of protection, and how thoroughly dataset provenance and licensing constraints need to be audited before model deployment is undertaken.  Technology companies are reminded by this case that data governance frameworks that can be defended, training pipelines that are transparent, and third-party datasets should be rigorously reviewed. Creators and platforms alike should take note of this development as it signals that regulation of artificial intelligence will be shaped less by abstract policy debates and more by detailed judicial scrutiny of the technological processes used in transforming publicly accessible content into machine-learning systems.

Snap Faces Lawsuit From Creators Over Alleged AI Data Misuse #AITrainingData #ArtificialIntelligenceLitigation

Ed "Zorz" Hafizov

@zorzstudios.com

1 month ago

First legal ruling on #AI, #copyright, and training data goes the way of creators: zorz.it/UNENC

#MattGrowcoot #AITrainingData #ArtificialIntelligence #LegalRuling #FairUse #GenerativeAI #legal #ThomsonReuters #law

Winbuzzer

@winbuzzer.com

1 month ago

Cloudflare: Google Massively Abuses Search Monopoly for AI Data Advantage Cloudflare has accused Google of abusing its search crawler monopoly to harvest 4.8 times more AI training data than Microsoft, forcing publishers into an impossible choice between protecting content…

winbuzzer.com/2026/02/09/c...

Cloudflare: Google Abuses Search Monopoly for 4.8x AI Data Advantage

#AI #Google #Cloudflare #BigTech #Search #AITrainingData #AICrawlers #AITraining #Content #Publishers #SearchResults #SearchEngines

HitechDigital Solutions

@hitechdigital.bsky.social

1 month ago

Image Annotation Methods That Power Object Detection Models

Accurate image annotation boosts object detection with clean labels and checks

Know More: hitechdigitalsolutions.tistory.com/entry/How-to...

#ImageAnnotation #ObjectDetectionModels #AITrainingData #DataLabeling #MachineLearningWorkflow

@semi1-98.bsky.social

1 month ago

Data Annotation And Labelling Market Share, Forecast [2035] www.marketresearchfuture.com/reports/data...
#DataAnnotation #MachineLearningData #AITrainingData #DataLabeling #AIDataset

HitechDigital Solutions

@hitechdigital.bsky.social

1 month ago

What Is Object Detection? A Simple Guide to How AI Sees Objects

Ever wondered how AI recognizes people, cars, or faces? This guide explains object detection, real-world uses, and why image annotation trains reliable AI.

shorturl.at/aPPp1

#ObjectDetection #AITrainingData #ImageAnnotationServices

Habiledata

@habiledata.bsky.social

1 month ago

Top Data Annotation Companies for AI and ML Projects in 2026 | Tech Web Space Discover the best data annotation companies for AI and ML in 2026. Compare top providers, services, trends, and expert selection tips.

Top data annotation companies power AI success with high-quality training data. Expert labeling across image, text, video & LiDAR ensures scalable, secure workflows, reduced bias, and faster AI model deployment in 2026.

Explore more: bit.ly/4t5XgCW

#DataAnnotation #AITrainingData #MLDataLabeling

HitechDigital Solutions

@hitechdigital.bsky.social

1 month ago

Data Preparation for Machine Learning: Challenges and Solutions Struggling with messy data? Discover the top data preparation challenges in machine learning, and proven strategies to overcome them.

How ai training data services Solve the Biggest Data Preparation Challenges in Machine Learning

AI training data services deliver clean, unbiased labeled data to boost accuracy, cut rework, and speed models to production.

shorturl.at/QvjPP

#MachineLearning #DataPreparation #AITrainingData #MLOps

HackerNoon

@hackernoon.com

1 month ago

Your AI Model Isn’t Broken. Your Data Is

Your AI model isn’t failing; your data is. Learn how clean, verified data improves model accuracy and how easy it is to fix with APIs. #aitrainingdata

HitechDigital Solutions

@hitechdigital.bsky.social

1 month ago

How to Get AI and ML Data Annotation Services for Your Project

Machine learning needs quality ai and ml data annotation services. Learn how to get labeled datasets via in-house teams or outsourcing.

shorturl.at/zwn0y

#MachineLearningData #MLDatasets #DataLabeling #AITrainingData #MLAnnotation

HitechBPO

@hitechbpo.bsky.social

1 month ago

Top 10 Advanced Prompt Engineering Techniques for LLM Prompts need a clear structure, need to be testable and should allow for the model to understand complex logic and reasoning clearly. Companies use advanced prompt engineering techniques to make large...

Top 10 Advanced Prompt Engineering Techniques for LLM

In this article, we break down the Top 10 Advanced Prompt Engineering Techniques used by AI practitioners.

Read the full article here: hitech-bpo.livejournal.com/311.html

#AItrainingdata #promptengineering

HitechBPO

@hitechbpo.bsky.social

1 month ago

Top 10 Advanced Prompt Engineering Techniques for LLM Prompts need a clear structure, need to be testable and should allow for the model to understand complex logic and reasoning clearly. Companies use advanced prompt engineering techniques to make large...

Top 10 Advanced Prompt Engineering Techniques for LLM

In this article, we break down the Top 10 Advanced Prompt Engineering Techniques used by AI practitioners.

Read the full article here: hitech-bpo.livejournal.com/311.html

#AItrainingdata #promptengineering

Evolution AI Hub

@evolutionaihub.bsky.social

1 month ago

Wikipedia Starts Charging AI Giants For Training Data Access Wikipedia will now charge AI giants for access to its training data, marking a major shift in how open knowledge powers generative AI.

Wikipedia is done being scraped.

The nonprofit is now charging AI giants like Amazon, Meta, and Microsoft for access to its knowledge—turning “open data” into licensed power.

🔍📚🤖

#Wikipedia #AI #GenerativeAI #AITrainingData #DataRights #TechNews #MediaAndAI

evolutionaihub.com/wikipedia-ch...

Ars Technica News

@arstechni.ca

1 month ago

Wikipedia signs AI training deals with Microsoft, Meta, and Amazon https://arstechni.ca #largelanguagemodels #WikimediaEnterprise #WikimediaFoundation #AIinfrastructure #machinelearning #AItrainingdata #generativeai #jimmywales #non-profit #Perplexity #microsoft #MistralAI #wikipedia #Biz&IT…

Legal Podcast Network Business

@lpn-business.bsky.social

2 months ago

Scraping entire legal databases or proprietary platforms is shockingly easy.

While companies debate ethics, others are pulling in massive data sets with a few lines of code. 

The real barrier isn’t extraction—it’s handling the flood of data afterward.

#DataScraping #AITrainingData #DigitalTheft

Investurns.com

@investurns.bsky.social

2 months ago

Data Marketplaces: The Next Big AI Investment? The Unseen Engine of AI: Why Data Marketplaces Are a Ground-Floor Investment Opportunity Let's cut to the chase. Everyone's talking about AI. ChatGPT, Midjourney, self-driving cars—it’s the biggest tech revolution…

Data Marketplaces: The Next Big AI Investment? #DataEconomy #bigdatainvestment #dataasaservice #AItrainingdata #web3dataplatforms #decentralizeddatamarketplace #SingularityNET #machinelearningdatasets #oceanprotocol #datamonetization

Jörg Lehmann

@jrglmn.bsky.social

3 months ago

Please extend this reading list!

#AITrainingData #Commons #OpenAccess #PublicDomain

@stabiberlin.bsky.social @europeana.bsky.social @bldigischol.bsky.social @nfitzger.glammr.us.ap.brid.gy @miaout.bsky.social @amsichani.bsky.social

HitechBPO

@hitechbpo.bsky.social

3 months ago

How Fashion Image Annotation Changed the Game for a Californian Tech Company

Empower your fashion AI systems with scalable and precise annotation: hitechbpo.com/case-studies...

#fashionimageannotation #imageannotation #retail #machinelearning #aitrainingdata

JimLiddle

@jimliddle.bsky.social

3 months ago

How AI Firms and Content Platforms Can Protect Data Integrity - Senior Executive Experts from the Senior Executive CMO Think Tank share tips to help AI firms approach public web content responsibly and explain how platforms like Reddit can strengthen data licensing, access control...

I and other members of the Senior Executive AI Think Tank share some thoughts on responsible data practices & compliance risks when dealing with the growing demand for training data

#EnterpriseAI #DataIntegrity #EthicalAI #ResponsibleAI #AITrainingData

seniorexecutive.com/ai-training-...

HitechBPO

@hitechbpo.bsky.social

4 months ago

Scale Your AI Projects with Quality Synthetic Data for Faster Model Training

Get high-quality synthetic data here: www.hitechbpo.com/synthetic-da...

#SyntheticData #SyntheticDataGeneration #AITrainingData #AIScalability #ComputerVision

HitechBPO

@hitechbpo.bsky.social

4 months ago

Top 10 Text Annotation Techniques for NLP Projects Find the top text annotation techniques to boost NLP accuracy, from NER and POS tagging to sentiment analysis. Read the guide to power your AI projects.

Top 10 Text Annotation Techniques for NLP Projects

Text annotation techniques help AI grasp meaning beyond just words.

Learn more in this insightful blog: www.hitechbpo.com/blog/text-an...

#textannotation #naturallanguageprocessing #aitrainingdata #textannotationservices

Habiledata

@habiledata.bsky.social

4 months ago

3d Cuboid Annotation

What is 3D Cuboid Annotation?
It turns flat images into 3D-aware datasets, helping AI see object size, depth & orientation.
Essential for autonomous vehicles, robotics & automation.

👉 bit.ly/3WJnrR8

#3dcuboidannotation #objectdetection #computervision #aitrainingdata

wgrav

@wgrav.fosstodon.org.ap.brid.gy

4 months ago

I wonder if the copyleft licenses like the GNU GPLv3 are enough to stop things like LLM training off of code... do we need a modernized GPLv4?

#OpenSource #license #foss #floss #fosslaw #libre #gnu #fsf #gpl #gplv3 #github #PublicDomain #law #AISlop #aitrainingdata #antiai #aitrainingconcerns […]

HackerNoon

@hackernoon.com

5 months ago

Synthetic Data Isn’t Fake. It’s the Future of Private, Scalable AI

Discover how synthetic data is transforming AI by overcoming privacy, scarcity, and scalability challenges. Learn how GANs, VAEs, and diffusion models generate #aitrainingdata

Habiledata

@habiledata.bsky.social

6 months ago

Polygon Annotation: The Key to Precision in Object Detection Discover how polygon annotation improves AI accuracy in object detection, reduces false positives, and powers real-world computer vision applications.

Polygon annotation is the precision edge in object detection. Pixel-perfect accuracy, fewer false positives, smarter AI across industries.
Full guide: differ.blog/p/accurate-o...

#polygonannotation #objectdetection #Aitrainingdata #Datalabeling #machinelearning #computervision #Boundingbox #Ai