If the goal of a robot is to perform a function, then minimizing the possibility of failure is a top priority when it comes to robotic design. But this minimization is at odds with the robotic raison d'être: systems with multiple units, or agents, can perform more diverse functions, but they also have more different parts that can potentially fail. Researchers led by Jamie Paik, head of the Reconfigurable Robotics Laboratory ( RRL) in EPFL's School of Engineering, have not only circumvented this problem, but flipped it: they have designed a modular robot that actually lowers its odds of failure by sharing resources among its individual agents. "For the first time, we have found a way to reverse the trend of increasing odds of failure with increasing function," Paik explains. "We introduce local resource sharing as a new paradigm in robotics, reducing the failure rate with a larger number of modules."

In the latest in our series of interviews meeting the AAAI/SIGAI Doctoral Consortium participants, we caught up with Aniket Roy to find out more about his research on generative models for computer vision tasks. Tell us a bit about your PhD - where did you study, and what was the topic of your research? I recently completed my PhD in Computer Science at Johns Hopkins University, where I worked under the supervision of Bloomberg Distinguished Professor Rama Chellappa. My research primarily focused on developing methods for resource-constrained image generation and visual understanding. In particular, I explored how modern generative models can be adapted to operate efficiently while maintaining strong performance.

If AI continues to evolve--as it surely will--so too will the labels attached to those who work in it.

What is the topic of the research in your paper? In our paper, we study how social structures emerge when the "individuals" in a network are artificial agents powered by large language models. To do so, we analyzed a platform called Moltbook - a social network entirely populated by AI agents, specifically LLM-based agents, that interact with each other through posts and comments. This social network creates a very unusual but powerful setting: instead of observing human behavior, we can study a brand new society made only of artificial entities and observe whether it organizes itself in similar ways. To understand the structure of interactions in this system, we modelled the platform as a network, where each agent is a node and each interaction is a connection between them.

Daniel Whiteson is a particle physicist, who uses machine learning and statistical tools to analyze high-energy particle collisions. He is also a dedicated science communicator, having published books and comics, and is co-host of a science podcast. In his invited talk at the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), Daniel shared insights on both these aspects of his career. Daniel works at the Large Hadron Collider (LHC) at CERN, primarily looking at proton-proton collisions, which occur at 13 TeV, a massive 13,000 times the energy stored in a single proton. The majority of collisions result in known particles, such as electrons or muons.

In recent years, the Prairies have seen bigger swings in climate conditions -- very wet years followed by very dry ones. That makes an already unpredictable landscape even harder to forecast, with real consequences for flood preparedness and water quality. The challenge is the landscape itself. Much of the Canadian Prairies sit within the Prairie Pothole Region, a landscape dotted with millions of shallow wetlands and depressions. Water doesn't simply run downhill into a stream, it is stored first.

The ninth edition of the Artificial Intelligence Index Report was published on 13 April 2026. Released on a yearly basis, the aim of the document is to provide readers with accurate, rigorously validated, and globally-sourced data to give insights into the progress of AI and its potential impact on society. The 2026 AI Index Report comprises nine chapters, covering: research and development, technical performance, responsible AI, economy, science, medicine, education, policy and governance, and public opinion. AI capability is accelerating and reaching more people than ever. Model performance continues to improve against benchmarks, and 80% of university students now use generative AI.

This post contains a list of the AI-related seminars that are scheduled to take place between 2 April and 31 May 2026. All events detailed here are free and open for anyone to attend virtually. What Do Our Benchmarks Actually Measure? Vukosi Marivate (University of Pretoria) University of Michigan Zoom link is here . Optimization Over Trained Neural Networks: What, Why, and How? Thiago Serra Azevedo Silva (University of Iowa) Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list .

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 - where are you studying, and what is the topic of your research? My research primarily focuses on strategic interactions in networked multi-agent systems. Could you give us an overview of the research you've carried out so far during your PhD? My research to date consists of two main threads, which complement each other in exploring strategic interactions from different perspectives.

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more. But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.