Today on, we're diving into recent reports that the Trump administration is considering an executive order that would establish some sort of federal oversight over new AI models. This week on, the team discusses the surprising reports of the Trump administration seemingly reversing its stance when it comes to AI safety and regulation. We also look into what exactly is going on with the Hantavirus outbreak, and whether you should be worried. Also, we get into the story of how a former federal employee who was ousted by Elon Musk's so-called Department of Government Efficiency is now running for office. Plus, a Spirit Airlines laid off employee shares with us how they experienced the company's shutdown news last weekend and what they'll miss most about the job. A Federal Worker Was Fired for Filming DOGE. Write to us at [email protected] . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . And we're going to talk about whether this move actually signals a meaningful shift in future regulation of this technology.

In this episode of “Uncanny Valley,” we get into how the Elon Musk-Sam Altman trial goes way beyond their rivalry and could have major implications both for OpenAI and also the AI industry at large.

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 the topic of your research? I'm a PhD student from IIT Hyderabad working with Dr Vineeth N Balasubramanian, supported by the PMRF Fellowship. Most current state-of-the-art models are black boxes, which is especially problematic when these models are used in high-stakes applications like criminal justice and healthcare, where people's lives depend on the decisions of these models.

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.

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 delved into the history of RoboCup, learned about time series, studied multiplicity, and found out more about Theory of Mind. RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup's founders, to find out more about how it all started, how the community has grown over the years, and the vision for the future. What we've learned from 25 years of automated science, and what the future holds We're excited to launch a new series, where we'll be speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises, to give you an inside perspective on the headlines.

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.

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.

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 are you studying, and what's the broad topic of your research? The idea is to integrate causal knowledge into agents or decision systems to make them more reliable.

What I've learned from 25 years of automated science, and what the future holds: an interview with Ross King We're excited to launch our new series, where we're speaking with leading researchers to explore the breakthroughs driving AI and the reality of the future promises - to give you an inside perspective on the headlines. Our first interviewee is Ross King, who created the first robot scientist back in 2009. He spoke to us about the nature of scientific discovery, the role AI has to play, and his recent work in DNA computing. Automated science is a really exciting area, and it feels like everyone's talking about it at the moment - e.g. But you've been working in this field for many years now. In 2009 you developed Adam, the first robot scientist to generate novel scientific knowledge. Could you tell me some more about that? So the history goes back to before Adam.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Minghong Geng recently completed his PhD and is now working as a postdoctoral researcher at Singapore Management University. We sat down to discuss his research on multi-agent systems. Firstly, congratulations on completing your PhD! What is the general topic of your research? I work on multi-agent systems.