Precision in data annotation: What’s needed for gen AI models

Graphic depicts a golden compass on an open book to illustrate precision in data annotation for building reliable generative AI models

Precision in gen AI data annotation Gen AI models learn to create novel content. However, for these models to be reliable and useful, their content should be grounded in accurate information and logical structures.  In gen AI data annotation, precision extends beyond accurate facts; it also encompasses creativity and nuance. Precise outputs should be factually […]

Human touch in gen AI: Training models to capture nuance 

Graphic depicts a woman annotator using headphones and a computer to illustrate the human touch in generative AI training

Humanity in gen AI data annotation Data annotation is not just about accuracy and precision. It requires human expertise and careful oversight to ensure AI models interact with the world in a meaningful, relevant, and responsible way.   Drawing from our most recent whitepaper, “Beyond accuracy: The new standards for quality in human data annotation for […]

Accelerating the new AI: Key insights from our latest whitepaper

A racecar on a blurred track represents the speed of change in generative AI, and how Sigma is accelerating this new AI through human data annotation

The key to gen AI success? High-quality data powered by human expertise  Building accurate and reliable generative AI models demands vast amounts of high-quality training data. Achieving this is easier said than done: it requires the right blend of efficient workflows, deep-domain knowledge, and human oversight.   With over 30 years at the forefront of AI […]

What humans hear that machines miss: 10 hidden language cues

Graphic depicts a woman in a modern office wearing headphones and working at a computer to illustrate human language cues and the nuanced communication machines often miss

Irony and sarcasm What it is: Saying the opposite of what is meant, often with a tonal cue. Example: “Oh, fantastic job…” said with clear frustration. Why machines miss it: Literal interpretation of words leads to mislabeling intent. Pragmatic implicature What it is: Inferring meaning beyond explicit words, based on context. Example: “It’s cold in […]

Inside Sigma’s 2025 CSR report: Driving positive impact 

Graphic depicts a vibrant tree with colorful leaves and deep, glowing roots to illustrate Sigma’s 2025 CSR report and its commitment to ethical growth and sustainable foundations.

Making a difference: How Sigma is driving positive change Guided by Sigma’s core values, our Environmental, Social, and Governance (ESG) program shows how we create a positive impact for our employees, communities, and the planet.    Here are a few key insights from our CSR report:  Environmental responsibility We are taking steps to reduce our carbon […]

Behind the scenes of creating the book ‘Nature’s Palette’ with AI

The book 'Nature's Palette' demonstrates the power of human and AI collaboration to create art, and the value of Sigma AI’s role in human data annotation.

The concept: Blending art, science, and AI We began by brainstorming coffee table book ideas that would appeal to a broad, international adult audience. AI generated a host of possible topics and then provided additional data on the popularity of certain topics and categories.  Humans narrowed these options down to the most appealing. A book […]

Best practices to scale human data annotation for large datasets

Scaling human data annotation for large datasets requires immense coordination, just as honeybees work together

The data dilemma: How much training data is enough for LLMs? Among the many challenges of training LLMs is the demand for gigantic amounts of training data. The exact volume varies based on the model’s intended use case and the complexities of the language domain. To determine the optimal dataset size, experts recommend experimenting with […]

How do you know it’s time to outsource data annotation?

Planning to build a major new AI initiative, by outsourcing data annotation

You need to move quickly but without compromising quality. In-house annotation? For many organizations, it isn’t sustainable anymore. But how do you know it’s time to outsource data annotation? If you’re struggling to keep pace with your data annotation demands, facing a bottleneck, or simply want to optimize your AI development pipeline, read on to […]

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