Why human skills are the secret ingredient in generative AI
Rethinking AI development — from code to human intelligence When most people think of artificial intelligence, they imagine complex algorithms and machine logic. But Sigma is proving that the most powerful AI systems begin with people. The company specializes in training individuals to perform generative AI data annotation — the behind-the-scenes work that fuels model […]
How red teaming AI reveals gaps in global model safety
Red teaming goes global Red teaming — intentionally probing AI models for weaknesses — has long been a key practice in AI safety. But most efforts focus on English, text-based interactions. Sigma AI decided to take things further. In our latest study, they pushed top models to their limits, examining how they behave in different […]
FAQs: Human data annotation for generative and agentic AI
What is human data annotation in generative AI? Human data annotation is the process of labeling AI training data with meaning, tone, intent, or accuracy checks, using expert human reviewers. In generative AI, this helps models learn to produce outputs that are truthful, emotionally appropriate, localized to be culturally relevant, and aligned with user intent. […]
Generative AI glossary for human data annotation
Agent evaluation The process of assessing how well an AI agent performs its tasks, focusing on its effectiveness, efficiency, reliability, and ethical considerations. Example: An annotator reviews a human-agent AI interaction, determining whether the person’s needs were met, and whether there was any frustration or difficulty. Attribution annotation Labeling where facts or statements originated, such […]
Enterprise AI software: Use cases from top tech companies
Gen AI is the new baseline for enterprise software Top-tier tech companies such as Microsoft, Salesforce, and Google are setting a new standard for AI enterprise software. Gen AI capabilities are becoming a must-have. Gartner projects that over 80% of software providers will embed gen AI into their products by 2026, driven by a demand […]
Why inter‑annotator agreement is critical to best‑in‑class gen AI training
What is inter‑annotator agreement (IAA) and why is it important? IAA measures how consistently multiple annotators label the same content. It helps quantify whether annotation guidelines are clear and whether annotators share a reliable understanding. Common metrics: Even seasoned experts often show α = 0.12–0.43 in high‑subjectivity tasks like emotional attribute scoring, especially before refining […]
Why gen AI quality requires rethinking human annotation standards
From accuracy to agreement: A new lens on quality Traditional AI annotation tasks (e.g. labeling a cat in an image) tend to yield high human agreement and low error rates. Annotators working with clear guidelines often achieve over 98% accuracy — sometimes even 99.99% — especially when backed by tech-assisted workflows. But these standards don’t […]
Linguistic diversity in AI and ML: Why it’s important
Linguistic diversity in AI means more access, inclusion, and use cases for everyone who benefits from machine learning.
Ethical AI vs. Responsible AI
There’s a lot of talk about ethical AI, or ethics in artificial intelligence, but what exactly does that mean? And is it different from responsible AI?