AIhub monthly digest: September 2026 – tracking animal populations, recommender systems, and an interview with Ken Goldberg

Summary

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet AI pioneer Ken Goldberg, use AI techniques to track animal populations, investigate how to improve recommender systems, and find out how external knowledge is used in AI systems. Combining cultures, from code to canvas: an interview with Ken Goldberg In the latest instalment in our AI pioneers series , we spoke to award-winning roboticist, filmmaker, and artist, Ken Goldberg . We discussed the culture clash within robotics, what art and science have to learn from each other, and how AI will shape the future of art. Understanding animal behaviour and tracking populations Deep learning is a powerful tool for understanding animal behaviour and tracking populations. We caught up with Isla Duporge to find out about her work developing remote-sensing and AI methods to investigate animal movement in the wild. Optimising sensor placement for estimating wildlife populations Hannah Murray is also researching wildlife populations. In this interview , she told us more about work developing optimization methods that help ecologists determine where to place sensors, such as camera traps, to get precise estimates of species population counts from the data they collect. Improving the process for large-scale recommender systems In large-scale recommender systems, platforms often need to handle massive volumes of items at web latency. To meet this requirement, systems often employ a two-stage decision process. We heard from Haruka Kiyohara about her research trying to improve the first part of this decision process – an early-stage ranker, which filters up to billions of items cheaply into a smaller candidate set. How is external knowledge used in AI systems? We kicked off our series meeting the IJCAI-ECAI doctoral consortium participants meeting Yash Saxena . Many AI systems now look through external documents before answering a question. In his research, Yash follows what happens to that evidence as it moves through the system. How much can fair budget-division rules resist manipulation? Haris Aziz, Patrick Lederer and Jeremy Vollen won a distinguished paper award at IJCAI-ECAI for their work “Approximate Strategyproofness in Approval-based Budget Division”. In this blog post , they summarise their pape…