Robotics programs require egocentric, multi-sensor training data at a scale that is growing exponentially, creating a procurement challenge distinct from any prior AI development cycle  Annotation ...

The pancreas and tumor are represented by the colors orange and green, respectively. Given the weak label, we expand it to a lesion marker (teal green) and background markers (red). We then utilize ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Oxipital AI’s V-CortX uses 3D scans of real products to generate millions of AI training examples automatically, removing the need for thousands of manually annotated images that traditional vision ...

When we talk about artificial intelligence, most people immediately think of futuristic robots and self-driving cars. But here’s the truth I’ve learned over years of working with data and leading ...

Annotation automation fails in safety-critical edge cases where human judgment is the only reliable signal While autonomous vehicle programs have matured through standardized sensor configurations and ...

Scale AI—which helps companies like ChatGPT improve the data that feeds their systems—is pictured on a laptop in New York on Aug. 16, 2023. On TikTok, Reddit, and elsewhere, posts are popping up from ...

Before we dive deeper, let’s answer the question: what is data annotation? Data annotation helps us to label data for its further usage by ML models. With labeled data, machines can better understand ...

Computer vision teams face an uncomfortable reality. Even as annotation costs continue to rise, research consistently shows that teams annotate far more data than they actually need. Sometimes teams ...