This post contains a list of the AI-related seminars that are scheduled to take place in the next couple of months. All events detailed here are free and open for anyone to attend virtually. Eleni Petraki and Damith Herath (University of Canberra) Raspberry PI Sign up here to join. Gaétan Hains, Paola Inverardi, and Alexander Wolf The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here . Tyler Summers (University of Texas at Dallas) EPFL The Zoom link is here .

Imagine you are driving toward an intersection. A truck parked on the corner blocks your view of a car speeding toward the intersection from the right. You can't see it, but the car next to you can. Your car now knows something its own sensors could never observe. But there's an important twist: not every neighboring vehicle is worth listening to, and not every message deserves the same level of trust.

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. 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. Deep learning is a powerful tool for understanding animal behaviour and tracking populations.

Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed two artificial intelligence (AI)-powered platforms that could enable researchers to more accurately predict proteins that undergo phase separation - a process through which proteins in cells separate like oil droplets in water. The tools are the result of a systematic analysis of predicted phase-separating proteins from various living things, including animals, plants, fungi and single-celled organisms such as bacteria. From their analyses, the scientists also uncovered fundamental insights about the phase separation process. Led by Professor Miao Yansong from the School of Biological Sciences and the Institute for Digital Molecular Analytics and Science (IDMxS) at NTU, in collaboration with Professor Weibo Gao from NTU's School of Electrical and Electronic Engineering, the research could be applied to advance the understanding of diseases and ageing, as well as to improve crops. Both AI platforms and their research findings have been reported in the peer-reviewed journal .

This will take place at 11:00 on Tuesday 29 September. Find out more here .

In this summary article, organisers of a tutorial and a workshop at IJCAI-ECAI 2026 pick their key takeaways from their respective sessions. This tutorial was a practical, hands-on tutorial on the theory and methods for handling missing data in tabular and imaging settings, from statistical baselines to autoencoders and generative adversarial networks. The missingness mechanism is more important than the choice of imputation method. MCAR, MAR, and MNAR settings call for different treatment, and MNAR, the most challenging mechanism, is the one that most methods do not handle well. Deep generative imputation is not always better.

In their paper Optimizing Sensor Placement with Greedy Algorithms: A Case Study in Wildlife Camera Trapping for Spatial Capture-Recapture Population Estimation, and present an approach for optimizing sensor placement for wildlife population counts. We caught up with first author Hannah to find out more about this work, which was presented at IJCAI-ECAI 2026 . What is the topic of the research in your paper, and why is it an interesting area for study? In our research, we set out to develop 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. This information is integral for ecologists to measure ecosystem health and to develop effective conservation management strategies, but they usually work under strict budget constraints.

Marcin Wilkowski / More info please / Licenced by CC-BY 4.0 The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics . Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report. In the next discussion in the collection, the panellists discuss AI evaluation. The four dimensions of evaluation the report says we're missing: capability, usability, human-system performance, and legal/ethical compliance Lucy Smith is Senior Managing Editor for AIhub.

Florida gets a lot of attention for its sinkholes, especially when they swallow cars and entire houses. But its sinkhole risk has nothing on Alaska's. Much of Alaska's soil is permafrost - ground that remains below 32 degrees Fahrenheit (0 degrees Celsius) for at least two consecutive years. It is often rich with ice, but when that ice melts, the ground can collapse. The consequences are the same as in Florida: substantial property damage as the land that buildings, roads and pipes were built on sinks .

How much can fair budget-division rules resist manipulation? In our paper, we ask what the best achievable compromise is. We prove that the Nash product rule reaches the optimal frontier. We study settings in which a fixed, divisible resource must be distributed among candidates or projects. The resource might be public money, research funding, charitable donations or even screen time.