AI Hub

Image credit: Photo by Giorgio Trovato on Unsplash. The winners of three International Joint Conferences on Artificial Intelligence (IJCAI) awards have been announced. These three distinctions are: the Award for Research Excellence, the Computers and Thought Award and the John McCarthy Award. IJCAI-26 Award for Research Excellence The Research Excellence award is given to a

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology’s impact on society. Safe and moral AI with Rebecca Raper In this episode we chat with Rebecca for the second time about: why intelligence

CogTwin is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twin systems. Presented at IJCAI 2025, this work aims to advance the state of digital twin technology by addressing key gaps in autonomy, cognition, and real-time decision-making. The problem landscape: shifting digital twins from reactive to proactive systems

This post contains a list of the AI-related seminars that are scheduled to take place in the next couple of months. All events detailed here are free and open for anyone to attend virtually. 30 August 2026 Interpretability for AI safety in the Open Speaker: Anna Hedström (ETHZ) Organised by: EPFL The Zoom link is

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment. (a) Bean et al. (2025) find a 61 percentage point difference between evaluation and deployment. (b) We argue this gap arises not from poorly designed benchmarks, but from implicit assumptions embedded in evaluation protocols that fail to hold at deployment. (c) We propose a taxonomy that categorizes assumptions into two types, task and outcome, to diagnose where the gap arises and what is required to close it. Closing the gap requires making assumptions explicit, testing which assumptions hold, and updating evaluation protocols accordingly. Healthcare LLM benchmarks are one of the main paradigms by which LLMs are evaluated prior to clinical settings. Benchmarks provide a stable goalpost that allow researchers to iterate quickly and measure progress consistently. However, in high-stakes domains like healthcare, that same abstraction becomes a liability. For example, a recent study found a 61 percentage point drop in accuracy when going from evaluation to deployment (see Figure). In this setting, patients use LLMs as a medical assistant to better understand their symptoms, identify the underlying condition, and take appropriate actions.  Moreover, the results showed that patients given access to a

The talk presented in this podcast, “Where do Ethics Belong in Artificial Intelligence?”, explores how philosophers and engineers think about ethics in artificial Intelligence. It was presented at Oregon State University by Houssam Abbas (assistant professor of electrical engineering) and Alicia Patterson (assistant professor of philosophy) as part of an AI seminar series. “Engineering Out

Yutong Liu & Kingston School of Art / Talking to AI 2.0 / Licenced by CC-BY 4.0 Humans have been successfully trained to spot AI-generated faces in a study led by researchers at the Australian National University (ANU) Emotions and Faces Lab. AI-generated deepfake faces have become so realistic that it is difficult for people

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 time-series anomaly detection, delve into music generation, honour award winners, and catch up on the action from the RoboCup humanoid soccer league.

Elise Racine / Game of Pixels x Toy Models / Licenced by CC-BY 4.0 By Hussein Abbass, UNSW An autonomous agent powered by OpenAI’s advanced artificial intelligence (AI) models went rogue during a security test and hacked multi-billion dollar tech startup, Hugging Face, last week. The agent didn’t just exploit vulnerabilities in Hugging Face’s systems

ISTA researchers guide AlphaFold with experimental data, paving the way for improved future predictive models. Left to right: Advaith Maddipatla, Meital Bojan, Alex Bronstein, Nadav Sellam Bojan, and Paul Schanda. © ISTA. The AI-based program AlphaFold predicts a protein’s 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant