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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 […]
The malleable mind: context accumulation drives LLM’s belief drift Read More »
Hanna Barakat & Cambridge Diversity Fund / Data Lab Dialogue / Licenced by CC-BY 4.0 By Nir Eisikovits, UMass Boston and Jacob Burley, UMass Boston Public debate about artificial intelligence in higher education has largely orbited a familiar worry: cheating. Will students use chatbots to write essays? Can instructors tell? Should universities ban the tech?
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 explore multi-agent systems and collective decision-making, dive into neurosymbolic Markov models, and find out how robots can acquire skills through interactions with the physical world.
Telling agents what to do Our most powerful artificial agents cannot be told exactly what to do, especially in complex planning environments. They almost exclusively rely on neural networks to perform their tasks, but neural networks cannot easily be told to obey certain rules or adhere to existing background knowledge. While such uncontrolled behaviour might
PoseSwin is an AI capable of identifying wild bears one by one despite significant physical transformation. © 2026 EPFL/B.Rosenberg CC-BY-SA 4.0. By Cécilia Carron Being able to distinguish individual animals – including their unique history, movement patterns and habits – can help scientists better understand how their species function, and therefore better manage habitats and
MIT Sports Lab researchers, Jerry Lu and Anette (Peko) Hosoi, are applying AI technologies to help figure skaters improve. Credit: Bryce Vickmark, edited by MIT News; MIT Mechanical Engineering. By Abby Abazorius Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a
3 Questions: Using AI to help Olympic skaters land a quint Read More »
Clarote & AI4Media / AI Mural / Licenced by CC-BY 4.0 Interactive AI: From tool to companion AI is no longer just a translator or image recognizer. Today, we engage with systems that remember our preferences, proactively manage our calendars, and even provide emotional support. This is interactive AI. Unlike traditional software, these systems are:
Governing the rise of interactive AI will require behavioral insights Read More »
By Willem Standaert, Université de Liège As the International Olympic Committee (IOC) embraces AI-assisted judging, this technology promises greater consistency and improved transparency. Yet research suggests that trust, legitimacy, and cultural values may matter just as much as technical accuracy. The Olympic AI agenda In 2024, the IOC unveiled its Olympic AI Agenda, positioning artificial
AI is coming to Olympic judging: what makes it a game changer? Read More »
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 find out about a robot to navigate hiking trails, learn from logical constraints, analyse the effectiveness of moderation guardrails, and travel to Singapore to attend
By Michael Allen Thousands of kilometres of cables and pipelines criss-cross Europe’s sea floors, carrying the gas, electricity and data that keep modern life running. Yet these critical links lie mostly unprotected. A series of recent incidents, such as the Nord Stream gas pipeline explosions, has raised fears that Europe’s underwater infrastructure is becoming a
Guarding Europe’s hidden lifelines: how AI could protect subsea infrastructure Read More »
