Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make ...

Artificial intelligence is blamed for taking away thousands of jobs. But, it also creates a few — at least for now. That’s because some artificial intelligence systems are still pretty dumb. They need ...

Following the pandemic, digitalization accelerated and enterprises started investing aggressively in artificial intelligence (AI) and automation to improve their business processes and drive ...

Labeling and annotation platforms might not get the attention flashy new generative AI models do. But they’re essential. The data on which many models train must be labeled, or the models wouldn’t be ...

Training AI or large language models (LLMs) with your own data—whether for personal use or a business chatbot—often feels like navigating a maze: complex, time-consuming, and resource-intensive. If ...

In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible.

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 ...

Artificial Intelligence (AI) continues to capture attention for its ability to transform financial services. From accelerating onboarding to improving anti-financial crime outcomes, AI is reshaping ...

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