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In an IJCAI-ECAI 2026 paper, Hannah Murray and other researchers proposed a greedy algorithm-based method for optimizing sensor placement in wildlife population monitoring, with the goal of improving the accuracy of spatial capture-recapture (SCR) population estimates. The method approximates habitat response estimates for hundreds of species by sampling averages and divides them into training, validation, and test sets; under parameter shifts, its sensor placements are more accurate and more precise than uniform grid placements. Last fall, the team conducted its first pilot on grizzly bears in the South Chilcotin Mountains of British Columbia, Canada, testing the effect of adding sensor placements on top of existing cameras. No subsequent progress has been disclosed so far.
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Sep 29
AIhub
AIhub Monthly Roundup: September 2026 — Tracking Animal Populations, Recommender Systems, and an Interview with Ken Goldberg
AIhub has published its September 2026 monthly roundup, covering an interview with roboticist Ken Goldberg, tracking wildlife populations with deep learning, early-stage ranker optimization for large-scale recommender systems, and research on how external knowledge flows through AI systems. This month, the new GPT-series model GPT-6 Astra was made available to paying users, and Sebastian Raschka wrote about his first impressions; several Fields Medalists signed a statement warning that using generative AI for mathematical research involves a misalignment between purpose and results.