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 Report, and watch a robot beating elite players at table tennis.
Table tennis robot outplays elite human players
In an article published in Nature this month, Sony AI introduced Ace, a table tennis robot that has beaten professional players in competitive matches. The system combines event-based vision sensors and a control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware.
2026 AI Index Report released
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.
Emergence of fragility in LLM-based social networks: an interview with Francesco Bertolotti
In their paper, Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook, Luca Sodano, Sofia Sciangula, Amulya Galmarini and Francesco Bertolotti study how social structures emerge when the “individuals” in a network are artificial agents powered by large language models. Francesco told us how LLMs behave in the social network Moltbook, and what this reveals about network dynamics.
Machine learning for particle physics
How is machine learning used in the search for new particles at CERN? Daniel Whiteson revealed all in his AAAI 2026 invited talk. We summarised this fascinating talk which covered the use of algorithms past and present, and looked forward to potential exciting discoveries.
Meeting the AAAI doctoral consortium participants
Our series featuring the 2026 AAAI/SIGAI doctoral consortium participants continued apace this month, with no fewer than five interviews.
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 scaling up multi-agent systems.
Xinwei Song is researching strategic interactions in networked multi-agents systems. We found out more about her work using algorithmic game theory and multi-agent reinforcement learning.
Abdelrahman Sayed Sayed’s work is at the intersection of continuous AI models, formal methods, and autonomous systems. We had a conversation about formal verification for safety evaluation of autonomous vehicles.
How can we integrate causal knowledge into agents or decision systems to make them more reliable? We chatted with Matteo Ceriscioli to find out more.
Deepika Vemuri is working on interpretability and concept-based learning. We learnt about the two aspects of concept-based models that she’s been researching.
Interview with Sukanya Mandal: Synthesizing multi-modal knowledge graphs for smart city intelligence
In their paper LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins, which was published in the AAAI Fall Symposium series, Sukanya Mandal and Noel O’Connor introduced an approach that leverages large language models to automate the construction of synthetic multi-modal knowledge graphs specifically designed for a smart city cognitive digital twin. Sukanya told us more about cognitive digital twins, the framework they employed, and some key results.
How AI Is physically breaking senior engineers
In his piece, The Human Cost of 10x: How AI Is Physically Breaking Senior Engineers, Denis Stetskov outlines how the deluge of AI-generated pull requests are overwhelming the senior engineers that have to review them. As he writes: “The industry calls this “10x productivity”. I call it what it is: a system that generates output at machine speed and forces humans to process it at biological speed.”
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