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AAAI presidential panel – AI agents

Joudy Bourghli & The Bigger Picture / The Omnipresent Tapestry / 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 […]

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Interview with AAAI Fellow Tanya Berger-Wolf: AI for ecology, biodiversity, and conservation

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. Over the course of the next few months, we’ll be talking to some of the 2026 AAAI Fellows. In this interview, we met with Tanya Berger-Wolf, who was elected

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Interview with AAAI Fellow Sanmay Das: multiagent systems

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. We’re talking to some of the 2026 AAAI Fellows to find out more about their work. In this interview, we chat to Sanmay Das, who was elected as a

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Panda and tiger reading

AIhub monthly digest: May 2026 – AI for science, the lottery ticket hypothesis, and world models

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 learn about AI for science, delve into world models, research transparent and trustworthy AI, and hear about the lottery ticket hypothesis. Making AI systems more

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Making AI systems more transparent and trustworthy: an interview with Ximing Wen

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Ximing Wen who is researching transparent and trustworthy AI systems. We found out more about her work, her experience as a research intern, and what inspired her to study AI. Tell us a bit about your PhD – where are you studying,

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Panda and tiger reading

AIhub monthly digest: April 2026 – machine learning for particle physics, AI Index Report, and table tennis

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 PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index

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#AAAI2026 invited talk: Yolanda Gil on improving workflows with AI

Jamillah Knowles & Digit / Pink Office / Licenced by CC-BY 4.0 Yolanda Gil is a professor at the University of Southern California, where she also serves as Senior Director for major strategic AI and data science initiatives. From 2018 – 2020, she was president of AAAI. In her invited talk at AAAI 2026, she

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Interview with Deepika Vemuri: interpretability and concept-based learning

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Deepika Vemuri who is working on interpretability and concept-based learning. We found out more about the two aspects of concept-based models that she’s been researching. Could you tell us a bit about your PhD – where are you studying, and what is

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Causal models for decision systems: an interview with Matteo Ceriscioli

How do you go about integrating causal knowledge into decision systems or agents? We sat down with Matteo Ceriscioli to find out about his research in this space. This interview is the latest in our series featuring the AAAI/SIGAI Doctoral Consortium participants. Could you start by telling us a bit about your PhD – where

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Interview with Xinwei Song: strategic interactions in networked multi-agent systems

In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We hear from Xinwei Song about the two main research threads she’s worked on so far, plans to expand her investigations, and what inspired her to study AI. Could you start with a quick introduction

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