Researchers Develop a New AI-Based Method for Metamaterials Design

Translated data: Researchers have developed an innovative AI-based design method that utilizes machine learning for reverse engineering, rapidly generating metamaterial designs based on desired mechanical properties. These designs are then precisely manufactured through 3D printing, ensuring the final product accurately reflects the desired performance. This AI-driven approach accelerates the development of new metamaterials, enabling an integrated design and manufacturing process that overcomes the limitations of current design methods.

Tsinghua University and Uber Join Forces with Manifold AI to Successfully Raise Over 100 Million Yuan

The AI research platform Manifold AI (Manifold Space), jointly established by Tsinghua University, Uber, and Manifold AI, has recently completed a pre-A round funding exceeding 100 million yuan. The platform focuses on supporting scientific research, especially in the debugging of machine learning models. Its core functions include scientific research support, data management, analysis collaboration tools, etc., aiming to promote innovation and development in the fields of scientific research and machine learning.

Scality Launches AI Certification for Over 20 Key Tools to Help Enterprises Accelerate AI Application Development

Global leading storage software company Scality upgrades its AI ecosystem certification program, now covering over 20 key AI and machine learning tools and frameworks. The program is based on its network resilient storage architecture, aiming to ensure tool interoperability, enhance data security and application efficiency, and meet the growing demand for AI development.

Meta AI Releases New Video Learning Model V-JEPA: A Breakthrough in Video Understanding

Recently, the Meta AI team launched the video joint embedding prediction architecture (V-JEPA) model, an innovative initiative aimed at advancing machine intelligence. Humans can naturally process information from visual signals and recognize surrounding objects and motion patterns. An important goal of machine learning is to reveal the fundamental principles that drive unsupervised learning in humans. Researchers proposed a key hypothesis—the predictive feature principle—arguing that the representations of continuous sensory inputs should be able to predict each other. Early research methods utilized slow feature analysis.

Google X Lab Incubates New Startup Heritable Agriculture: Using AI to Enhance Crop Yields

Google X's 'moonshot factory' has recently announced the independent development of a new startup, Heritable Agriculture. This new company aims to improve the way crops grow using data and machine learning technologies. Heritable Agriculture stated in a press release that plants are efficient and astonishing systems: 'Plants are solar-powered, carbon-negative self-assembling machines that rely on sunlight and water to thrive.' However, agriculture puts immense pressure on the Earth and its resources.

99% Accuracy! New AI Tool from Keele University Easily Identifies Fake News

Recently, researchers at Keele University developed a new AI tool capable of identifying fake news with an accuracy of up to 99%, providing crucial support in combating online misinformation. The research team includes Dr. Uchenna Anie from the School of Computer Science and Mathematics, Dr. Sangeeta Sangeeta, and Dr. Patricia Aso-Ayobode. The team employed various machine learning techniques to design a model that scans news content and assesses its credibility. The researchers used an 'ensemble voting' technique, which...