HabileData<p>6 Essential Data Annotation Techniques that Drive Computer Vision</p><p>Our latest video on the 6 common types of annotation in Computer Vision reveals how the perfect blend of human intelligence and cutting-edge data annotation techniques can significantly enhance the performance and scalability of your AI and ML models.</p><p><a href="https://www.youtube.com/watch?v=EHXVzz7VHvo" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">youtube.com/watch?v=EHXVzz7VHvo</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/dataannotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataannotation</span></a> <a href="https://mastodon.social/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/trainingdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>trainingdata</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a></p>
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#dataannotation
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HabileData<p>Data Annotation vs Data Labelling- Find the right for you</p><p>Key takeaways:</p><p>• Understand the core difference between annotation and labeling<br>• Explore use cases across NLP, computer vision & more<br>• Learn how each process impacts model training and accuracy</p><p>Read now to make smarter data decisions: </p><p><a href="https://www.hitechbpo.com/blog/data-annotation-vs-data-labeling.php?utm_medium=referral&utm_campaign=group-sharing" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">hitechbpo.com/blog/data-annota</span><span class="invisible">tion-vs-data-labeling.php?utm_medium=referral&utm_campaign=group-sharing</span></a></p><p><a href="https://mastodon.social/tags/DataAnnotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnnotation</span></a> <a href="https://mastodon.social/tags/DataLabeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataLabeling</span></a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/Annotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Annotation</span></a> <a href="https://mastodon.social/tags/Data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Data</span></a> <a href="https://mastodon.social/tags/ComputerVision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputerVision</span></a> <a href="https://mastodon.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NLP</span></a> <a href="https://mastodon.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a></p>
Miguel Afonso Caetano<p>"Scale AI is basically a data annotation hub that does essential grunt work for the AI industry. To train an AI model, you need quality data. And for that data to mean anything, an AI model needs to know what it's looking at. Annotators manually go in and add that context.</p><p>As is the means du jour in corporate America, Scale AI built its business model on an army of egregiously underpaid gig workers, many of them overseas. The conditions have been described as "digital sweatshops," and many workers have accused Scale AI of wage theft.</p><p>It turns out this was not an environment for fostering high-quality work.</p><p>According to internal documents obtained by Inc, Scale AI's "Bulba Experts" program to train Google's AI systems was supposed to be staffed with authorities across relevant fields. But instead, during a chaotic 11 months between March 2023 and April 2024, its dubious "contributors" inundated the program with "spam," which was described as "writing gibberish, writing incorrect information, GPT-generated thought processes."</p><p>In many cases, the spammers, who were independent contractors who worked through Scale AI-owned platforms like Remotasks and Outlier, still got paid for submitting complete nonsense, according to former Scale contractors, since it became almost impossible to catch them all. And even if they did get caught, some would come back by simply using a VPN.</p><p>"People made so much money," a former contributor told Inc. "They just hired everybody who could breathe.""</p><p><a href="https://futurism.com/scale-ai-zuckerberg-incompetence" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">futurism.com/scale-ai-zuckerbe</span><span class="invisible">rg-incompetence</span></a></p><p><a href="https://tldr.nettime.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://tldr.nettime.org/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GenerativeAI</span></a> <a href="https://tldr.nettime.org/tags/Meta" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Meta</span></a> <a href="https://tldr.nettime.org/tags/ScaleAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ScaleAI</span></a> <a href="https://tldr.nettime.org/tags/DataAnnotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnnotation</span></a> <a href="https://tldr.nettime.org/tags/DataLabeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataLabeling</span></a> <a href="https://tldr.nettime.org/tags/GigWork" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GigWork</span></a></p>
Miguel Afonso Caetano<p>"The production of artificial intelligence (AI) requires human labour, with tasks ranging from well-paid engineering work to often-outsourced data work. This commentary explores the economic and policy implications of improving working conditions for AI data workers, specifically focusing on the impact of clearer task instructions and increased pay for data annotators. It contrasts rule-based and standard-based approaches to task instructions, revealing evidence-based practices for increasing accuracy in annotation and lowering task difficulty for annotators. AI developers have an economic incentive to invest in these areas as better annotation can lead to higher quality AI systems. The findings have broader implications for AI policy beyond the fairness of labour standards in the AI economy. Testing the design of annotation instructions is crucial for the development of annotation standards as a prerequisite for scientific review and effective human oversight of AI systems in protection of ethical values and fundamental rights."</p><p><a href="https://journals.sagepub.com/doi/10.1177/20539517251351320" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/10.11</span><span class="invisible">77/20539517251351320</span></a></p><p><a href="https://tldr.nettime.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://tldr.nettime.org/tags/GenerativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GenerativeAI</span></a> <a href="https://tldr.nettime.org/tags/DataWork" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataWork</span></a> <a href="https://tldr.nettime.org/tags/DataLabour" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataLabour</span></a> <a href="https://tldr.nettime.org/tags/AIPolicy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIPolicy</span></a> <a href="https://tldr.nettime.org/tags/PoliticalEconomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PoliticalEconomy</span></a> <a href="https://tldr.nettime.org/tags/DataLabeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataLabeling</span></a> <a href="https://tldr.nettime.org/tags/AIEthics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIEthics</span></a> <a href="https://tldr.nettime.org/tags/DataAnnotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnnotation</span></a></p>
CSBJ<p>🧬 Can AI fix the chaos in biological sample data?</p><p>🔗 Annotation of biological samples data to standard ontologies with support from large language models. Computational and Structural Biotechnology Journal, DOI: <a href="https://doi.org/10.1016/j.csbj.2025.05.020" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.csbj.2025.05</span><span class="invisible">.020</span></a></p><p>📚 CSBJ: <a href="https://www.csbj.org/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">csbj.org/</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/AIinScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIinScience</span></a> <a href="https://mastodon.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LLMs</span></a> <a href="https://mastodon.social/tags/Bioinformatics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bioinformatics</span></a> <a href="https://mastodon.social/tags/DataAnnotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnnotation</span></a> <a href="https://mastodon.social/tags/GPT4" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GPT4</span></a> <a href="https://mastodon.social/tags/BiomedicalAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BiomedicalAI</span></a> <a href="https://mastodon.social/tags/OpenScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenScience</span></a> <a href="https://mastodon.social/tags/FAIRData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FAIRData</span></a> <a href="https://mastodon.social/tags/Ontology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Ontology</span></a> <a href="https://mastodon.social/tags/AIinBiology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIinBiology</span></a> <a href="https://mastodon.social/tags/DataInteroperability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataInteroperability</span></a></p>
ResearchBuzz: Firehose<p>TechXplore: Third-party data annotators often fail to accurately read the emotions of others, study finds. “Machine learning algorithms and large language models (LLMs), such as the model underpinning the functioning of the platform ChatGPT, have proved to be effective in tackling a wide range of tasks. These models are trained on various types of data (e.g., texts, images, videos, and/or […]</p><p><a href="https://rbfirehose.com/2025/05/22/techxplore-third-party-data-annotators-often-fail-to-accurately-read-the-emotions-of-others-study-finds/" class="" rel="nofollow noopener" target="_blank">https://rbfirehose.com/2025/05/22/techxplore-third-party-data-annotators-often-fail-to-accurately-read-the-emotions-of-others-study-finds/</a></p>
Adrianna Tan<p>Multiple layers of cursed</p><p><a href="https://data-workers.org/roukaya/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">data-workers.org/roukaya/</span><span class="invisible"></span></a></p><p><a href="https://hachyderm.io/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://hachyderm.io/tags/Syria" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Syria</span></a> <a href="https://hachyderm.io/tags/DataAnnotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnnotation</span></a> <a href="https://hachyderm.io/tags/Exploitation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Exploitation</span></a></p>
Harald Sack<p>Nice overview about LLMs for data annotation including paper references of papers with open source code & data. <br>Zhen Tan et al, Large Language Models for Data Annotation: A Survey, <a href="https://arxiv.org/abs/2402.13446" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2402.13446</span><span class="invisible"></span></a></p><p><a href="https://sigmoid.social/tags/llms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>llms</span></a> <a href="https://sigmoid.social/tags/generativeai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>generativeai</span></a> <a href="https://sigmoid.social/tags/informationextraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>informationextraction</span></a> <a href="https://sigmoid.social/tags/dataannotation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataannotation</span></a></p>
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