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#datalabeling

2 posts2 participants0 posts today
Public
HabileData

Data Annotation vs Data Labelling- Find the right for you

Key takeaways:

• Understand the core difference between annotation and labeling
• Explore use cases across NLP, computer vision & more
• Learn how each process impacts model training and accuracy

Read now to make smarter data decisions:

hitechbpo.com/blog/data-annota

#DataAnnotation#DataLabeling#AI
Public
HabileData

Use of Text Annotation in Different Industries

Text annotation plays a crucial role in training #AI models, ensuring accurate language processing and data interpretation. From entity recognition to sentiment analysis, understanding the right #annotation techniques can significantly enhance AI performance.

hitechbpo.com/blog/text-annota

Hitech BPO · Text Annotation Guide: Types, Techniques, Tools & TipsExplore the world of text annotation with our expert guide. Learn the types, techniques, tools, tips, and strategies for effective text data annotation.
#TextAnnotation#AITraining#MachineLearning
Public
HabileData

The Future of Image Annotation: Emerging Trends

The future of Image Annotation is here

Key Insights:
• Rise of automation in labeling for faster results
• Integration of deep learning for smarter annotations
• Real-time annotation for dynamic environments

Unlock how these innovations are shaping the next-gen AI models.

datafloq.com/read/future-image

#ImageAnnotation#AI#ComputerVision
Public
Miguel Afonso Caetano

"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.

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.

It turns out this was not an environment for fostering high-quality work.

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."

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.

"People made so much money," a former contributor told Inc. "They just hired everybody who could breathe.""

futurism.com/scale-ai-zuckerbe

Futurism · The AI Company Zuckerberg Just Poured $14 Billion Into Is Reportedly a Clown Show of Ludicrous IncompetenceBy Frank Landymore
#AI#GenerativeAI#Meta
Public
Miguel Afonso Caetano

"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."

journals.sagepub.com/doi/10.11

#AI#GenerativeAI#DataWork
Public
Matasoft

Data categorization doesn't need hours of tedious work anymore. (Un)Perplexed Spready analyzes context and automatically classifies items with formulas like =PERPLEXITY1(A2, "Is this electronics, clothing, or home goods?").
matasoft.hr/qtrendcontrol/inde

#AIProductivity#PracticalAI#DataManagement
Public
Matasoft

If you're still manually parsing text in Excel with complex formulas, you're living in the past. (Un)Perplexed Spready uses AI to extract specific information from text cells with natural language commands. No regex needed, just results!
matasoft.hr/qtrendcontrol/inde

#AIProductivity#PracticalAI#DataManagement
Public
Matasoft

Tired of manually categorizing products one by one? (Un)Perplexed Spready's AI integration analyzes thousands of product descriptions and automatically sorts them into categories like "meat, dairy, bakery, or other" with just one formula. This is AI doing REAL work!
matasoft.hr/qtrendcontrol/inde

#DataProductivity#DataManagement#AI
Public
Matasoft

While everyone's playing with AI chatbots asking for jokes, (Un)Perplexed Spready is revolutionizing spreadsheet data. It extracts specific product measures from dirty product descriptions and converts them to metric units with a single formula. That's practical AI!
matasoft.hr/qtrendcontrol/inde

#DataManagement#AI#Spreadsheets
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