AI, LLM Training, and Operations Efficiency

Learn how to leverage AI, technology and human capital for the future.

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Behind the Data: Zachary Neeley

Last month, we had a candid conversation with Zachary Neeley, a political science tutor who transformed from a skeptical outsider to a passionate AI trainer.

Designing Robust Human Studies for AI Safety Evaluations

A comprehensive guide to identifying vulnerabilities in AI models through systematic jailbreaking research, exploring methodologies, challenges, and potential defenses.

Data Annotation's Role in Shaping Ethical AI Governance Post-AGI

As we move closer to AGI, the standards we establish today in data annotation—fair wages, worker rights, diversity, and transparent practices—will influence the ethical boundaries of tomorrow’s AI systems.

Data Annotation's Growing Appeal to PhDs and Scientists

It's time for academia to be recognized and compensated fairly for the invaluable knowledge and expertise these professionals bring to the table.

Behind the Data: Gilbert Kamau

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.

Preparing for the Future of Work: Adapting to Atomized Tasks

Discover how the future of work is evolving towards an atomized, purpose-driven model that emphasizes individual talents and specialized tasks.

26 Prompting Principles for Optimal LLM Output

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.

Is Data Scarcity the Biggest Obstacle to AI’s Future?

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.

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