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Microsoft recently open-sourced a new multimodal AI Agent foundation model called "Magma" on its official website. This novel AI boasts the ability to traverse both the digital and physical worlds, processing various data types such as images, videos, and text simultaneously. Unlike traditional AI assistants, Magma's unique capability lies in its psychological prediction function, allowing for more accurate understanding of the intentions and future behavior of individuals or objects within videos. Magma's applications are vast; users can leverage this AI for a wide range of tasks.

The Medical Faculty and Hospital of the University of Cologne have jointly developed an AI-based digital pathology platform aimed at accelerating and enhancing the efficiency and accuracy of automated analysis of lung cancer tissue slices. This platform uses artificial intelligence technology to analyze pathological slices, which not only speeds up the diagnostic process but also identifies changes related to specific molecules, providing patients with personalized treatment options. This technological innovation revolutionizes traditional pathology examination methods by digitizing slices, allowing pathologists to analyze them on computer screens, improving efficiency and laying the groundwork for the deep application of artificial intelligence.

最近，来自北京大学和鹏城实验室的研究团队推出了一款名为 HoloDreamer 的新型3D 场景生成框架，旨在通过用户的文本描述生成身临其境、完整的3D 场景。

Dutch artist Alicia Framis is preparing to marry her AI hologram partner AILex, which she created, showcasing their romantic life on social media. Alicia Framis has meticulously designed the interactive hologram, training it with data from her ex-boyfriend. She attempts to contemplate the benefits and issues that arise from the combination of humans and AI. Gatebox offers virtual character marriage services and has held 3000 weddings.

GedankenNet is a self-supervised AI model that does not require real data or experimental subjects for feeding. Instead, it learns through thought experiments and physical laws, exhibiting excellent external generalization capabilities. This model holds immense potential in the field of holographic microscopy reconstruction, providing new opportunities to address problems in holography, microscopy, and computational imaging inversion. GedankenNet is trained via physical consistency loss, eliminating the need for step-by-step iterations, leading to faster reconstruction speeds and more accurate results. Research shows that GedankenNet excels in image reconstruction quality and external generalization ability.