Blog•Jan 19, 2026
An Introduction to Science Communication at #AAAI2026
The session introduces AI researchers to effective science communication, covering why it matters, storytelling, blog writing, social media use, image selection, avoiding hype, and engaging with media. Presenter Dr. Lucy Smith emphasizes clear, concise explanations for non‑specialists to broaden impact and networks. Attendees receive practical advice, Q&A, and hands‑on activities to develop communication skills across traditional and unconventional formats.
By AIhub
Blog•Jan 16, 2026
Interview with Anindya Das Antar: Evaluating Effectiveness of Moderation Guardrails in Aligning LLM Outputs
In this episode, Anindya Das Antar explains their new Bayesian probabilistic method for evaluating and selecting moderation guardrails that align large language model outputs with expert-defined expectations. The approach estimates activation probabilities for each guardrail, revealing their individual and interactive...
By AIhub
Blog•Jan 15, 2026
Guarding Europe’s Hidden Lifelines: How AI Could Protect Subsea Infrastructure
The episode explores how AI can safeguard Europe’s extensive subsea cables and pipelines, focusing on the EU‑funded VIGIMARE project led by researcher Johanna Karvonen. It details how machine‑learning models will fuse satellite imagery, AIS data, radar and acoustic signals from...
By AIhub
Blog•Jan 14, 2026
What’s Coming up at #AAAI2026?
The episode previews AAAI‑2026, the first AAAI conference held outside North America, hosted in Singapore from Jan 20‑27. It highlights the diverse program—including invited talks from leaders like Peter Stone and Isabelle Guyon, a science‑communication tutorial, extensive tutorials and labs on topics...
By AIhub
Blog•Jan 12, 2026
Robots to Navigate Hiking Trails
The episode explores the challenges of autonomous robot navigation on unstructured hiking trails, emphasizing the need to perceive, plan, and adapt to dynamic obstacles like fallen trees, mud, and erosion. Researchers combine LiDAR-based geometric terrain analysis with camera-driven semantic segmentation...
By AIhub
Blog•Jan 9, 2026
AAAI Presidential Panel – AI Reasoning
The panel, moderated by former AAAI President Francesca Rossi, examined AI reasoning as outlined in the AAAI’s 2025 Future of AI Research report. Experts Holger Hoos and Subbarao Kambhampati discussed how to define and implement reasoning in AI, emphasizing planning...
By AIhub
Blog•Jan 5, 2026
Forthcoming Machine Learning and AI Seminars: January 2026 Edition
The episode outlines a calendar of free virtual AI and machine learning seminars running from early January to late February 2026, featuring talks on topics such as LLM introspection, causal representation learning for climate teleconnections, AI ethics, combinatorial optimization, generative...
By AIhub
Blog•Dec 29, 2025
AIhub Monthly Digest: December 2025 – Studying Bias in AI-Based Recruitment Tools, an Image Dataset for Ethical AI Benchmarking, and...
The December 2025 AIhub monthly digest covers four main topics: Frida Hartman's research on gender bias in AI-driven recruitment tools, Alice Xiang's launch of the Fair Human-Centric Image Benchmark (FHIBE) for ethical computer‑vision evaluation, Professor Marynel Vázquez's insights on human‑robot...
By AIhub
Blog•Dec 24, 2025
Half of UK Novelists Believe AI Is Likely to Replace Their Work Entirely
A Cambridge‑led survey of 258 UK novelists and industry insiders reveals that 51% fear AI could fully replace their work, with 59% reporting unauthorized use of their writing to train models and 39% already seeing income losses. While a third...
By AIhub
Blog•Dec 23, 2025
RL without TD Learning
The episode introduces a novel off‑policy reinforcement learning algorithm that replaces temporal‑difference learning with a divide‑and‑conquer paradigm, dramatically reducing error accumulation by using logarithmic Bellman recursions. Seohong Park explains how the method leverages the triangle‑inequality property in goal‑conditioned RL, employing...
By AIhub
Blog•Dec 19, 2025
Identifying Patterns in Insect Scents Using Machine Learning
The episode explores a University of Amsterdam project that uses machine learning to decode how insect olfactory receptors bind to scent molecules, aiming to create a large, shared database of 25,000+ scent-receptor interactions. Researchers from biology, mathematics, data science, and...
By AIhub
Blog•Dec 18, 2025
2025 AAAI / ACM SIGAI Doctoral Consortium Interviews Compilation
The episode compiles interviews with 23 doctoral consortium participants, showcasing a wide spectrum of AI research—from kernel learning for time‑series forecasting and explainable AI for robotics and cyber‑physical systems, to privacy‑preserving generative models, bias mitigation in large language models, and...
By AIhub
Blog•Dec 10, 2025
Australia’s Vast Savannas Are Changing, and AI Is Showing Us How
The episode explores how Australia’s extensive northern savannas are being monitored and forecasted using an AI tool called Themeda, which leverages 33 years of satellite data and deep learning to predict land‑cover changes at a fine 25 × 25 m resolution. The researchers...
By AIhub
Blog•Dec 9, 2025
Generations in Dialogue: Human-Robot Interactions and Social Robotics with Professor Marynel Vasquez
In this episode, Professor Marynel Vázquez discusses her evolving research on human‑robot interaction, emphasizing how robots can navigate social groups by modeling interactions as graphs and adapting to errors in real time. She highlights the potential of socially aware robots...
By AIhub
Blog•Dec 8, 2025
AI Language Models Show Bias Against Regional German Dialects
The episode discusses a new study revealing that large language models, including GPT‑5 and open‑source alternatives, systematically assign negative stereotypes to speakers of German regional dialects compared to Standard German, influencing decisions in hiring, education, and other contexts. Researchers from...
By AIhub
Blog•Dec 5, 2025
We Asked Teachers About Their Experiences with AI in the Classroom — Here’s What They Said
The episode explores Canadian teachers' firsthand experiences with generative AI in K‑12 classrooms, revealing deep concerns about assessment integrity, equity gaps, and added workload. Interviewees stress that current AI policies overlook the emotional labor and professional judgment essential to teaching,...
By AIhub
Podcast•Dec 3, 2025•0 min
The Machine Ethics Podcast: Fostering Morality with Dr Oliver Bridge
In this episode, Ben Byford interviews interdisciplinary researcher Dr. Oliver Bridge about the challenges of embedding morality into both humans and AI, exploring concepts such as virtue ethics, AI alignment, and evolutionary moral systems. Bridge emphasizes the value of systems...
By AIhub
Blog•Dec 1, 2025
Forthcoming Machine Learning and AI Seminars: December 2025 Edition
Lucy Smith’s December 2025 AI seminar roundup lists a series of free, virtual talks covering diverse topics such as optimization for societal impact, AI safety, AI literacy measurement, protein engineering with diffusion models, and the role of third‑party intelligence in markets....
By AIhub
Blog•Nov 27, 2025
Generations in Dialogue: Embodied AI, Robotics, Perception, and Action with Professor Roberto Martín-Martín
In this episode, host Ella Lan talks with Professor Roberto Martín‑Martín about his journey from tinkering with toys to pioneering embodied AI that integrates perception, learning, and action in robotics. Martín‑Martín explains how his research—spanning pick‑and‑place, navigation, and complex tasks like...
By AIhub
Blog•Nov 27, 2025
EU Proposal to Delay Parts of Its AI Act Signal a Policy Shift that Prioritises Big Tech over Fairness
The episode examines the EU Commission’s proposal to postpone key provisions of the AI Act until 2027, a move critics say favors large tech firms over fairness and signals a broader shift in digital regulation. It contrasts this with the...
By AIhub
Blog•Nov 24, 2025
What Is AI Poisoning? A Computer Scientist Explains
The episode explains AI poisoning, where attackers deliberately corrupt an AI’s training data (data poisoning) or the model itself (model poisoning) to cause targeted misbehaviour or overall performance degradation. It distinguishes direct attacks like backdoors, which trigger specific harmful outputs,...
By AIhub
Blog•Nov 21, 2025
New AI Technique Sounding Out Audio Deepfakes
Researchers from CSIRO, Federation University Australia, and RMIT introduced Rehearsal with Auxiliary‑Informed Sampling (RAIS), a continual‑learning method that selects and stores a diverse set of past audio samples using auxiliary labels to detect evolving audio deepfakes without forgetting earlier threats....
By AIhub
Blog•Nov 19, 2025
ACM SIGAI Autonomous Agents Award 2026 Open for Nominations
The episode announces the open call for nominations for the 2026 ACM SIGAI Autonomous Agents Research Award, highlighting its purpose to honor researchers whose current work significantly influences the autonomous agents field. Listeners are instructed on how to nominate candidates...
By AIhub