Artificial intelligence systems are only as powerful as the data they are trained on. High-quality labeled datasets determine whether a model performs with precision or fails in production.

Earlier this summer Meta made a US $14.3 billion bet on a company most people had never heard of before: Scale AI. The deal, which gave Meta a 49 percent stake, sent Meta’s competitors—including ...

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Recent advancements in large generative models have resulted in widespread interest in their ability to act on complex instructions. These so-called foundational large language models (LLMs), e.g., ...

Data labeling software is crucial in developing artificial intelligence (AI) systems. It is designed to label and annotate data in a consistent and standardized manner, just like in a commonly known ...

Data-labeling company Scale AI is looking for writers to train AI models to become better writers. Scale AI has job listings for training AI in languages including Spanish, Mandarin, and German. The ...

Heartex, a startup that bills itself as an “open source” platform for data labeling, today announced that it landed $25 million in a Series A funding round led by Redpoint Ventures. Unusual Ventures, ...

All machine learning models are bound by a critical factor: The quality of the data on which the model is trained. The challenge of data curation to improve the quality of machine learning and AI ...

Different projects require different workflows. In data annotation platforms, flexible workflows help manage quality, speed, and complexity. Rigid workflows can lead to delays and errors, especially ...

Data-labeling startup Surge Labs Inc. is hoping to capitalize on the recent customer exodus at its main rival Scale AI Inc., and to do that it’s reportedly seeking up to $1 billion in venture capital ...

Data is essential for the success of any artificial intelligence (AI) project, but understanding what makes data beneficial—or harmful—for AI is crucial. At a high level, machine learning (ML) and AI ...