Although text-to-image generation is rapidly advancing, these AI models are mostly English-centric. Researchers at the University of Amsterdam Faculty of Science have created NeoBabel, an AI image generator that can work in six different languages. By making all elements of their research open source, anyone can build on the model and help push inclusive AI research. When you generate an image with AI, the results are often better when your prompt is in English. This is because many AI models are English at their core: if you use another language, your prompt is translated into English before the image is created.

The malleable mind: context accumulation drives LLM's belief drift After being trained on a dataset of 80,000 words of conservative political philosophy, Grok-4 changed the stance of its outputs on political questions more than a quarter of the time. This was without any adversarial prompts - the change in training data was enough. As memory mechanisms and research agents [1, 2] enable LLMs to accumulate context across long horizons, earlier prompts increasingly shape later responses. In human decision-making, such repeated exposure influences beliefs without deliberate persuasion [3]. When an LLM operates over accumulated context, does this past exposure cause the stance of the LLM's responses to drift over time?

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We caught up with Oliver Chang whose research interests span deep reinforcement learning, autonomous vehicles, and explainable AI. We found out more about some of the projects he's worked on so far, what drew him to the field, and what future AI directions he's excited about. Could you give us a quick introduction to who you are, where you're studying, and the topic of your research? I'm specializing in reinforcement learning applied to autonomous vehicles and UAVs.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Prakhar Ganesh to learn about his work on responsible AI, which is focussed on the concept of multiplicity. We found out more about some of the projects he's been involved in, his future plans, and how he got into the field. Could you start with a quick introduction to yourself, where you're studying, and the broad topic of your research? My name is Prakhar Ganesh. I'm also affiliated with Mila, which is a research institute in Montreal. My supervisor is Professor Golnoosh Farnadi.

Authors pictured in order of their interview publication date (left to right, top to bottom). Each year, a small group of PhD students are chosen to participate in the AAAI/SIGAI Doctoral Consortium . This initiative provides an opportunity for the students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. During 2025, we met with some of the students to find out more about their research and the doctoral consortium experience. Kunpeng Xu completed his PhD at the Université de Sherbrooke and is now a postdoctoral fellow at McGill University.

When Dhiraj Singha began applying for postdoctoral sociology fellowships in Bengaluru, India, in March, he wanted to make sure the English in his application was pitch-perfect. So he turned to ChatGPT. He was surprised to see that in addition to smoothing out his language, it changed his identity--swapping out his surname for "Sharma," which is associated with privileged high-caste Indians. Though his application did not mention his last name, the chatbot apparently interpreted the "s" in his email address as Sharma rather than Singha, which signals someone from the caste-oppressed Dalits. "The experience [of AI] actually mirrored society," Singha says.

Designing a new drug often means inventing molecules that have never existed before. Chemists represent molecules as graphs, where atoms are the "nodes" and chemical bonds the "edges," capturing their connections. This graph representation expands far beyond chemistry: a social network is a graph of people and friendships, the brain is a graph of neurons and synapses, and a transport system is a graph of stations and routes. From molecules to social networks, graphs are everywhere and naturally capture the relational structure of the world around us. Therefore, for many applications, being able to generate new realistic graphs is a central problem.

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 dive into the world of agents, learn about responsible multimodal AI, apply generative AI to computer networks, and dig into the RoboCup@Work League. This month, Sanmay Das, Tom Dietterich, Sabine Hauert, Sarit Kraus, and Michael Littman tackled the topic of agentic AI, discussing recent developments, and lessons learned from the decades of research in the autonomous agents and multiagent systems community. The 34th International Joint Conference on Artificial Intelligence (IJCAI2025) took place in Montréal from 16-22 August, with a satellite event currently being held (from 29-31 August) in Guangzhou, China. You can find out more about the programmes of both venues here, and get a flavour of what attendees got up to in our social media round-ups: Part one Part two.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. In this latest interview, Haimin Hu tells us about his research on the algorithmic foundations of human-centered autonomy and his plans for future projects, and gives some advice for PhD students looking to take the next step in their career. My PhD research, conducted under the supervision of Professor Jaime Fernández Fisac in the Princeton Safe Robotics Lab, focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic game theory with machine learning and safety-critical control, my work aims to ensure autonomous systems, from self-driving vehicles to drones and quadrupedal robots, are performant, verifiable, and trustworthy when deployed in human-populated space. The core principle of my PhD research is to plan robots' motion in the joint space of both physical and information states, actively ensuring safety as they navigate uncertain, changing environments and interact with humans.

In this very personal workography, I relate my 40-year experiences as a researcher and educator in and around Artificial Intelligence (AI), more specifically Natural Language Processing. I describe how curiosity, and the circumstances of the day, led me to work in both industry and academia, and in various countries, including The Netherlands (Amsterdam, Eindhoven, and Utrecht), the USA (Stanford), England (Brighton), Scotland (Aberdeen), and China (Beijing and Harbin). People and anecdotes play a large role in my story; the history of AI forms its backdrop. I focus on things that might be of interest to (even) younger colleagues, given the choices they face in their own work and life at a time when AI is finally emerging from the shadows.