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 […]
Building LLMs with sensitive data: A practical guide to privacy and security
Know your data: what “sensitive” means in practice Why this matters for LLMs: leakage is real Modern models can memorize and later regurgitate rare or sensitive strings from training corpora. Research has demonstrated the extraction of training data from production LLMs via carefully crafted prompts, and a growing body of work on membership-inference risks. The […]
When “uh… so, yeah” means something: teaching AI the messy parts of human talk
A quick primer: what’s what (and why it matters) Signals, not noise: disfluency carries meaning A sentence like, “I — I can probably help … later?” encodes hesitation, caution, and weak commitment. If ASR or cleanup filters strip stutters, filler, or rising intonation, downstream models may over-state confidence. Annotation pattern Example “That’s a whole — […]
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 […]
Human annotators in AI: Adding context & meaning to raw data
Let’s start with the basics: Who are data annotators? Data annotators are responsible for manually labeling and categorizing data, to ensure it’s understandable and useful for machine learning algorithms. This process, known as data annotation, involves tagging, reviewing, and validating various types of unstructured data, including text, images, video, and audio. The result is a […]
Your gen AI data roadmap: 5 strategies for success
Gen AI data roadmap to kickstart your journey 1 – Preparing for gen AI begins with a data strategy Data is the fuel of AI. For companies to fully leverage the potential of this technology, a strong data foundation is imperative. This involves addressing data management issues related to quality, security, transparency, integration, storage, and […]