UK DSIT launched a national consultation on keeping children safe online, including options such as age-based restrictions (including a possible under-16 social media ban), enforcement, and supporting education and guidance. Responses will inform the government’s next steps.
Ireland introduced the Regulation of AI Bill 2026 to implement the EU AI Act domestically, including establishing an independent AI Office of Ireland to oversee enforcement and support innovation. The bill aims to be in place ahead of the EU implementation deadline of 2 August 2026.
IMDA and the Institute of Singapore Chartered Accountants (Isca) launched the AIxAccountancy programme under Singapore’s National AI Impact Programme to upskill accountants in AI. The programme is delivered in phases with certificates, badges, and access to an AI learning hub.
India’s IT secretary said the government is considering a dedicated AI law as AI-related risks like deepfakes and cyber threats evolve. No timeframe or draft text was provided.
ISCA and IMDA officially launched AIxAccountancy, an AI fluency training programme for non-tech accountancy and corporate finance professionals under Singapore’s National AI Impact Programme. The initiative includes online learning, AI tool practice, and completion credentials.
Motion planning algorithms should be evaluated in human-in-the-loop environments to ensure they produce safe and efficient behaviors during interactions. However, existing simulation platforms often rely on recorded datasets, lack dedicated interfaces for real-time human interaction, or remain weakly integrated with an autonomous driving ecosystem. Moreover, many human-in-the-loop simulators are c
Offline reinforcement learning (RL) holds significant potential for crowd robot navigation in human-robot coexistence applications. However, the inherent complexity of pedestrian motion renders the design of effective reward functions for promoting socially compliant robot behaviors a persistent challenge. This paper proposes a Social Preference Learning for Crowd Robot Navigation (SPLC) algorithm
Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance. We present MMBench-Live, a continuously evolving multimodal benchmark built by a multi-agent-driven automated pipeline. Our framework treats benchmark evolution as task-guided dataset co
Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices. Despite decades of research, OL lacks a shared infrastructure for systematic evaluation and ontology access. This absence has hindered progress and fragmented research, leaving the central challenges of OL largely unaddress
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal computation incur substantial memory and latency overhead, severely limiting real-world deployment on resource-constrained devices. Binarization offers an attractive solution by drastically reducing storage and computational costs. However, existing
Capital continues to cluster around large-scale AI and compute-adjacent bets, with one private round and two public-market events anchoring the desk. The pattern is concentration at the top end rather than breadth across the stack.
Capital continues to cluster around large-scale AI and compute-adjacent bets, with one private round and two public-market events anchoring the desk. The pattern is concentration at the top end rather than breadth across the stack.
Capital continues to cluster around large-scale AI and compute-adjacent bets, with one private round and two public-market events anchoring the desk. The pattern is concentration at the top end rather than breadth across the stack.
Named moves remain sparse, but the departures and hires touch roles that shape data, AI, and field operations. That mix matters because these are leverage points for how large teams translate strategy into execution.
Named moves remain sparse, but the departures and hires touch roles that shape data, AI, and field operations. That mix matters because these are leverage points for how large teams translate strategy into execution.
Named moves remain sparse, but the departures and hires touch roles that shape data, AI, and field operations. That mix matters because these are leverage points for how large teams translate strategy into execution.
The benchmark desk shows a narrow but useful picture: one model’s math and GSM8K entries hold steady across repeated records, while the surface itself is being refreshed by live, continuously evolving evaluation design. The story is as much about how scores are tracked as the scores themselves.
The benchmark desk shows a narrow but useful picture: one model’s math and GSM8K entries hold steady across repeated records, while the surface itself is being refreshed by live, continuously evolving evaluation design. The story is as much about how scores are tracked as the scores themselves.
The benchmark desk shows a narrow but useful picture: one model’s math and GSM8K entries hold steady across repeated records, while the surface itself is being refreshed by live, continuously evolving evaluation design. The story is as much about how scores are tracked as the scores themselves.