Uploaded June 2026 | Updated September 2026, 2 weeks ago
Title: Reducing AI Agnostophobia
Speaker: Shu Kong, University of Macau
Abstract:
Artificial Intelligence (AI) transforms everything from autonomous vehicles to scientific research. Yet, its rapid adoption has triggered "AI agnostophobia", a fear of the unknown exacerbated by unpredicted AI failures, such as autonomous vehicles colliding with overturned trucks and AI assistants exhibiting severe demographic biases. To reduce AI agnostophobia, we talk demystifies the bedrock of modern AI: Foundation Models (FMs) trained on billions of internet data. By analyzing such data, we expose the underlying data imbalances that inherently drive model bias; we present simple post-hoc techniques that can mitigate this bias.
Nevertheless, can we safely rely on AI to analyze our data in highly specialized fields like biology and ecology? To address this question, we introduce AutoExpert, a practical research framework designed to automate domain-data annotation with expert-crafted guidelines. By presenting AI-based approaches and key insights, we delineate both the limitations and the promises of expert-AI collaboration, illustrating how this synergy can accelerate interdisciplinary research.
Profile:
Shu Kong is in the faculty of Computer Science at the University of Macau, with prior academic appointments at Carnegie Mellon University and Texas A&M University. He holds a Ph.D. from UC Irvine. His research spans computer vision, applied machine learning, and interdisciplinary science. He actively promotes the field of Open-World Vision, on which his work earned Best Paper / Marr Prize nomination at ICCV 2021. His previous interdisciplinary breakthroughs include an automated high-throughput pollen analysis system, which was highlighted by the U.S. National Academy of Sciences (NAS) and the National Science Foundation (NSF) as "opening a new era of fossil pollen research." Personal Homepage
Find out more about the TSVP on the program website: oist.jp/visiting-program
#ArtificialIntelligence #AI #FoundationModels #ModelBias #AutoExpert #DataAnnotation #Biology #Ecology #ExpertAICollaboration #InterdisciplinaryResearch #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research
Title: Reducing AI Agnostophobia
Speaker: Shu Kong, University of Macau
Abstract:
Artificial Intelligence (AI) transforms everything from autonomous vehicles to scientific research. Yet, its rapid adoption has triggered "AI agnostophobia", a fear of the unknown exacerbated by unpredicted AI failures, such as autonomous vehicles colliding with overturned trucks and AI assistants exhibiting severe demographic biases. To reduce AI agnostophobia, we talk demystifies the bedrock of modern AI: Foundation Models (FMs) trained on billions of internet data. By analyzing such data, we expose the underlying data imbalances that inherently drive model bias; we present simple post-hoc techniques that can mitigate this bias.
Nevertheless, can we safely rely on AI to analyze our data in highly specialized fields like biology and ecology? To address this question, we introduce AutoExpert, a practical research framework designed to automate domain-data annotation with expert-crafted guidelines. By presenting AI-based approaches and key insights, we delineate both the limitations and the promises of expert-AI collaboration, illustrating how this synergy can accelerate interdisciplinary research.
Profile:
Shu Kong is in the faculty of Computer Science at the University of Macau, with prior academic appointments at Carnegie Mellon University and Texas A&M University. He holds a Ph.D. from UC Irvine. His research spans computer vision, applied machine learning, and interdisciplinary science. He actively promotes the field of Open-World Vision, on which his work earned Best Paper / Marr Prize nomination at ICCV 2021. His previous interdisciplinary breakthroughs include an automated high-throughput pollen analysis system, which was highlighted by the U.S. National Academy of Sciences (NAS) and the National Science Foundation (NSF) as "opening a new era of fossil pollen research." Personal Homepage
Find out more about the TSVP on the program website: oist.jp/visiting-program
#ArtificialIntelligence #AI #FoundationModels #ModelBias #AutoExpert #DataAnnotation #Biology #Ecology #ExpertAICollaboration #InterdisciplinaryResearch #OIST #TSVP #Theoretical #Science #VisitingProgram #Okinawa #research
![[Seminar] Making eDNA data FAIR (Findable, Accessible, Interoperable, Reusable)](https://i.ytimg.com/vi/Cxb1RMoVpwE/mqdefault.jpg "[Seminar] Making eDNA data FAIR (Findable, Accessible, Interoperable, Reusable)
Seminar title: Making eDNA data FAIR (Findable, Accessible, Interoperable, Reusable)
Speaker: Dr. Miwa Takahashi, CERC Postdoctoral Fellow, Environomics Future Science Platform, NCMI | CSIRO
Friday, August 30, 2024 - 13:00 to 14:00
Environmental DNA (eDNA) has emerged as a powerful monitoring tool for species detection and distribution mapping, with a remarkable surge in activity over the past decade. Metabarcoding studies have generated millions of DNA sequences with taxonomic assignments, while species-specific eDNA assays have produced comprehensive distribution maps for hundreds of taxa. Despite the widespread practice of data sharing upon publication, inconsistent data formats pose challenges for data reuse. To address this issue, the “Making eDNA FAIR (Findable, Accessible, Interoperable, Reusable)” project has been launched. Our international, multidisciplinary working group, consisting of eDNA researchers, journal editors and biodiversity and omics data scientists, is developing best practice guidelines for eDNA data formatting and sharing. Our aim is to extend the eDNA data lifecycle, facilitating reuse and reanalyses of this invaluable biodiversity data resource. Please join our seminar to learn about the FAIR data principles and the project, and start our discussions on the bottlenecks, needs, and strategies to achieve FAIR eDNA!
https://groups.oist.jp/macc/event/seminar-making-edna-data-fair-findable-accessible-interoperable-reusable")
