AI Safety II: Goodharting and Reward Hacking
In this post, the second in the series (after...
By creating digital doubles of complex computer vision product systems, you can perform design trade-off studies in a virtual environment. Optimize camera configurations and understand performance before building hardware.
Capturing real data of edge cases and rare events is prohibitively expensive and often impossible. Synthetic data can be used to augment real data to ensure coverage of critical use cases that impact system performance and safety.
Real-world data contains many biases. Biases related to demographics have significant ethical and legal implications. Synthetic data enables companies to build diverse and balanced human datasets to mitigate bias in a privacy-compliant manner.
Spatial computing, autonomy, AR/VR, and robotic applications require detailed knowledge of the 3D world. With synthetic data, developers now have access to pixel-perfect annotations of depth, surface normals, 3D landmarks, and more to build better models.
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