Machine-Learning

Data labeling is essential for training AI in autonomous systems, balancing manual and automated methods while addressing privacy, accuracy, and scalability challenges.

Outlining key challenges in real-world data capture—like occlusion, lighting, dust, and poor calibration.

This post examines key sensor technologies for automation and their trade-offs, while also addressing cybersecurity, data processing, and the widespread underuse of captured sensor data.

This first DIVI post defines Data as raw sensor input essential for automation, stressing its quality and fusion as key to reliability.