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Featured Articles
How We Develop Standardized Protocols for Data Labeling Projects
Character.AI Joins Forces with Pareto.AI to Improve Conversational AI Models
Should You Pay Per Task or By Hour? Optimizing Worker Productivity for High-Quality Data
Reinforcement Learning from Human Feedback: Everything You Need to Know
Equitable Pay for All: Understanding Geographic Wage Disparity in Remote Work
The Future of Crowd Work: Q&A with Dr. Mark Whiting
Say hi to Gilbert Kamau—a data scientist and dedicated AI trainer whose journey from actuarial science to cutting-edge AI work at Pareto is packed with insights.
Discover how the future of work is evolving towards an atomized, purpose-driven model that emphasizes individual talents and specialized tasks.
Discover 26 essential prompting principles to enhance your interactions with large language models (LLMs). Learn how to craft precise prompts for clearer, more accurate AI-generated responses.
We delve into the implications of data scarcity on model training, emphasizing the need for high-quality, expert-sourced human data as a cornerstone of AI development. We also explore how supplementing expert-led data collection with synthetic data can be a viable strategy for addressing these challenges.
Apple's DCLM-7B sets a new AI standard with thoughtful data curation. Explore its impact, transparency, and the role of expert data in our latest blog.
With the introduction of OpenAI o1's reasoning capability, prompting methods need to be adjusted. OpenAI o1 handles complex reasoning internally, which means old prompting strategies may no longer be effective. Understanding these shifts is key to leveraging the model’s strengths optimally.
Welcome to our "Behind the Data" series, where we delve into engaging discussions with our top data annotators. This series shines a spotlight on the individuals who play a pivotal role in the development and training of AI.
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