Abstract: An electronic device for controlling a photographic system may obtain a video stream and a user query for a target event, obtain a set of photos from the video stream, obtain at least one photoshoot suggestion based on the user query via a language model, obtain a snapped photo for the target event based on the at least one photoshoot suggestion, in response to a given video frame included in the video stream satisfying a target content criterion, and output one or more photos selected from the set of photos and the snapped photo as event photos.

Abstract: Rapid and data-efficient training of an artificial intelligence (AI) algorithm are disclosed. Ground truth data are not available and a policy must be learned based on limited interactions with a system. A policy bank is used to explore different policies on a target system with shallow probing. A target policy is chosen by comparing a good policy from the shallow probing with a base target policy which has evolved over other learning experiences. The target policy then interacts with the target system and a replay buffer is built up. The base target policy is then updated using gradients found with respect to the transition experience stored in the replay buffer. The base target policy is quickly learned and is robust for application to new, unseen, systems.