Integrating hyperscale AI into national defense modeling and simulation (M&S) is crucial for enhancing strategic and operational capabilities. We explore how hyperscale AI can revolutionize defense M\&S by providing unprecedented accuracy, speed, and the ability to simulate complex scenarios. Countries such as the United States and China are at the forefront of adopting these technologies and are experiencing varying degrees of success. Maximizing the potential of hyperscale AI necessitates addressing critical challenges, such as closed networks, long-tail data, complex decision-making, and a shortage of experts. Future directions emphasize the adoption of domestic foundation models, the investment in various GPUs / NPUs, the utilization of big tech services, and the use of open source software. These initiatives will enhance national security, maintain competitive advantages, and promote broader technological and economic progress. With this blueprint, the Republic of Korea can strengthen its defense capabilities and stay ahead of the emerging threats of modern warfare.

Click-through rate (CTR) prediction plays an important role in personalized recommendations. Recently, sample-level retrieval-based models (e.g., RIM) have achieved remarkable performance by retrieving and aggregating relevant samples. However, their inefficiency at the inference stage makes them impractical for industrial applications. To overcome this issue, this paper proposes a universal plug-and-play Retrieval-Oriented Knowledge (ROK) framework. Specifically, a knowledge base, consisting of a retrieval-oriented embedding layer and a knowledge encoder, is designed to preserve and imitate the retrieved & aggregated representations in a decomposition-reconstruction paradigm. Knowledge distillation and contrastive learning methods are utilized to optimize the knowledge base, and the learned retrieval-enhanced representations can be integrated with arbitrary CTR models in both instance-wise and feature-wise manners. Extensive experiments on three large-scale datasets show that ROK achieves competitive performance with the retrieval-based CTR models while reserving superior inference efficiency and model compatibility.

AI is used at Quang Trung Software City in apartment building management. HANOI -- In the development of the fourth industrial revolution, artificial intelligence (AI) has made significant progress and is expected to make breakthroughs in the next ten years. Following the global trend, the Vietnamese government aims to turn AI into a driving force of development and make it a top priority in growth policies. One of the most important tasks is developing human resources, including improving higher education training quality, building 4.0 technology training institutes, encouraging technological application, renovation at businesses; and investment in science-technology research. According to Minister of Science and Technology Chu Ngọc Anh Việt Nam has advantages in start-up innovation and technological development, especially in AI.

HÀ NỘI In the development of the fourth industrial revolution, artificial intelligence (AI) has made significant progress and is expected to make breakthroughs in the next ten years. Following the global trend, the Vietnamese Government aims to turn AI into a driving force of development and make it a top priority in growth policies. One of the most important tasks is developing human resources, including improving higher education training quality, building 4.0 technology training institutes, encouraging technological application, renovation at businesses; and investment in science-technology research. According to Minister of Science and Technology Chu Ngọc Anh Việt Nam has advantages in start-up innovation and technological development, especially in AI. With an open business climate and labour force interested in technology, the application of AI in various sectors has made progress, connecting parts of the nation's AI ecosystem.