Observed records of climate extremes provide an incomplete picture of risk, missing "unseen" extremes that exceed historical bounds. In parallel, neglecting spatial dependence undervalues the risk of synchronized hazards that amplify impacts. To address these challenges, we develop DeepX-GAN (Dependence-Enhanced Embedding for Physical eXtremes - Generative Adversarial Network), a knowledge-informed deep generative model designed to better capture the spatial structure of rare extremes. The zero-shot generalizability of DeepX-GAN enables simulation of unseen extremes that fall outside historical experience yet remain statistically plausible. We define two types of unseen extremes: "checkmate" extremes that directly hit targets, and "stalemate" extremes that narrowly miss. These unrealized scenarios expose latent risks in fragile systems and may reinforce a false sense of resilience if overlooked. Near misses, in particular, can prompt either proactive adaptation or dangerous complacency, depending on how they are interpreted. Applying DeepX-GAN to the Middle East and North Africa (MENA), we find that these unseen extremes disproportionately affect regions with high vulnerability and low socioeconomic readiness, but differ in urgency and interpretation. Future warming could expand and redistribute these unseen extremes, with emerging exposure hotspots in Indo-Pakistan and Central Africa. This distributional shift highlights critical blind spots in conventional hazard planning and underscores the need to develop spatially adaptive policies that anticipate emergent risk hotspots rather than simply extrapolating from historical patterns.

Kyiv, Ukraine – Referring to the Soviet puzzle video game, Alla says her husband kills Russian soldiers as though he is playing "human Tetris". "A drone hangs in the sky, and he watches [them] crawl across the forest," she told Al Jazeera. And then they crawl again." The war in Ukraine has turned Avdiivka, where Alla's husband is stationed, into a maze of ruins, trenches and tunnels surrounded by burned-down fields and patches of forest studded with landmines, explosion craters and remnants of Russian soldiers and armoured vehicles. Avdiivka sits 20km (12 miles) north of separatist Donetsk, wedged deep into occupied areas.

Throughout his career, Milosz has been consulting and devising growth strategies for small and start-up businesses, particularly within financial services. His focus areas include search, conversion, user experience and technical developments. Prior to the acquisition of Chilli Fruit Web Consulting, Milosz has been involved in Plus Guidance (an early-stage UK tech start-up, now acquired) and Sigma Digital Marketing Agency based in Oxfordshire.

User feedback can be an effective indicator to the success of the human-robot conversation. However, to avoid to interrupt the online real-time conversation process, explicit feedback is usually gained at the end of a conversation. Alternatively, users' responses usually contain their implicit feedback, such as stance, sentiment, emotion, etc., towards the conversation content or the interlocutors. Therefore, exploring the implicit feedback is a natural way to optimize the conversation generation process. In this paper, we propose a novel reward function which explores the implicit feedback to optimize the future reward of a reinforcement learning based neural conversation model. A simulation strategy is applied to explore the state-action space in training and test. Experimental results show that the proposed approach outperforms the Seq2Seq model and the state-of-the-art reinforcement learning model for conversation generation on automatic and human evaluations on the OpenSubtitles and Twitter datasets.

I am completely unqualified in matters of raising children, yet this topic has provoked me to post my opinion on the problem. I came across a Guardian article by George Monbiot about raising children in a new academic environment of robots. The article emphasized the need for not making children robot-like, but teaching them to collaborate and use their creativity and curiosity. The post gave me a pause: on the surface, it criticized current state of education, but for me it presented a controversy between technologies and education. I dug a little deeper and saw that technologies, robots and AI are not welcomed in education like I thought.

We present an alternative form AI that avoids limited type of interaction, namely the AI agents acting independently of each other rather than working together as a team. To do so, we add the multi-agent functionality to the AI for a simple pursuit game. Initially the AI directs each agent independently to pursue the target player. These agents then suffer from collision and overlapping such that the player can evade the clustered agents without difficulty. Next we introduce our multi-agent AI that coordinates the efforts of the enemy agents so that they stay in formation and work together to corner the player. In so doing we wish to show that this greatly improves the quality of gameplay and the realism simulated by the AI. Further, this upholds the original intention of the AI as designed by the developers and avoids unrealistic “cheats” to circumvent the intended gameplay. While this research is centered in gaming, we also believe that it has further reaching applications in security, simulations, and robotics.