Large language models excel on broad multilingual benchmarks but remain to be evaluated extensively in figurative and culturally grounded reasoning, especially in low-resource contexts. We present BengaliFig, a compact yet richly annotated challenge set that targets this gap in Bengali, a widely spoken low-resourced language. The dataset contains 435 unique riddles drawn from Bengali oral and literary traditions. Each item is annotated along five orthogonal dimensions capturing reasoning type, trap type, cultural depth, answer category, and difficulty, and is automatically converted to multiple-choice format through a constraint-aware, AI-assisted pipeline. We evaluate eight frontier LLMs from major providers under zero-shot and few-shot chain-of-thought prompting, revealing consistent weaknesses in metaphorical and culturally specific reasoning. BengaliFig thus contributes both a diagnostic probe for evaluating LLM robustness in low-resource cultural contexts and a step toward inclusive and heritage-aware NLP evaluation.

Discussions about artificial intelligence (AI) have quickly turned from the excited to the apocalyptic. Are warnings that AI could pose an existential threat valid, or do they distract from the real danger AI is already causing? A year on from the murders of British journalist Dom Phillips and Brazilian Indigenous activist Bruno Pereira, producer Flo Phillips reports on the justice being served and how their work goes on, done by others. Eight decades after the first train of prisoners arrived at the Auschwitz-Birkenau extermination camp, Holocaust survivors – and their testimonies – are dwindling. Producer Johanna Hoes explores the politics of memory and the importance of recounting history, so it doesn't repeat itself.

On the first day, I attended the workshop on AI Explainability that brought together researchers from both the HCI and Computer Science communities. The workshop was opened by UNamur professors Bruno Dumas, specializing in HCI, and Benoît Frénay who works on Machine Learning. Dr Frénay presented the XAI research field and the interdisciplinary research being conducted at UNamur on this topic. He pointed out the lack of a user-centered approach in the XAI machine learning community where less than 1% of accepted papers in major conferences, such as NeurIPS, test their XAI methods with user studies. The rest of the morning was devoted to the presentation of eight abstracts, including mine, related to XAI research with either a computer science or HCI angle.

According to Global Market Insights, Artificial Intelligence (AI) in the Banking, Financial and Insurance (BFSI) Market is estimated to be worth over USD 2.5 billion in 2017 and is anticipated to grow at a Global CAGR of more than 30% from now through 2024. Not surprisingly, the Asia Pacific region driven by China is leading the way with an estimated CAGR of over 40%. AI has applications that vary widely in finance - from cost savings to improving customer experience and fraud detection - and right now there are already 2.5 million U.S. financial services workers whose jobs are directly impacted by AI. Finance was one of the first sectors to embrace AI. Sydney Swaine-Simon and Abhishek Gupta write: "The financial sector is one of the first domains to drive interest in using artificial intelligence, even before high computing machines were available. In the 1960s, a lot of research focused on Bayesian statistics, a method used heavily in machine learning. Some of its use cases included stock market prediction and auditing. It wasn't until the 1980s until the majority of commercialization opportunities were explored with expert systems. During that time, over two thirds of Fortune 1000 companies had at least one AI project being developed."