[machine translation]
Hello everyone. I'm Kimura from the Quantum Applications CPJ team at the Quantum Research Institute. I recently presented our latest research findings at FTU 2025, held at Fujitsu Technology Park on December 2, 2025. To share this presentation with a wider audience, I am now providing a detailed introduction on the Fujitsu Tech Blog.
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Hello, this is Sakai from Artificial Intelligence Laboratory of Fujitsu Research. In this blog, I’ll introduce 1-bit quantization in an easy-to-understand way. The background of this technology lies in the growing size of generative AI models and the accompanying challenges in computational resources. Our AI research team has developed a groundbreaking solution—1-bit quantization—and even released it as open-source software (OSS). This article explains the background and the technology in simple terms.
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Hello, we are Uno, Miyake, and Inderjeet from the Data & Security Research Laboratory.
In recent years, the development of AI technology has been remarkable. A future where AI agents from multiple companies and organizations collaborate and co-create to solve more complex and diverse challenges is becoming increasingly realistic. As knowledge essential for improving AI performance approaches its limits based solely on web-based information, AI agents trained on knowledge accumulated by enterprises are expected to solve industry-wide challenges, such as those in supply chains, and create new innovations through cross-organizationa collaboration.
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Hello. We're Suzuki and Kurume from the Converging Technologies Laboratory. Our research group is developing "Policy Twin" technology that dramatically advances policy-making by local governments and nations.
Have you ever heard the keyword "digital twin"? A digital twin is a general term for technologies that construct and simulate a twin of the real world in a virtual space, and it is utilized in various fields such as manufacturing and smart cities. We went further and developed "Social Digital Twin" [^1],[^2] technologies to reproduce people and society in the digital world and solve social issues. And to further deepen these efforts, we are taking a further step by attempting to digitalize "Policy" itself, which forms the foundation of social design. This is the "Policy Twin" technology we are developing.
This time, we would like to introduce the full scope of this innovative Policy Twin technology and its application in actual municipalities. Aren't you excited that our real society will also evolve into a better one by freely testing policies in a digital space, creating such possibilities for new social design? Please look forward to it!
(NOTE: This article was generated using machine translation.)
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Hello, we are Sakamoto and Nitta from the Data & Security Research Lab.
The current advancement of generative AI technology presents significant potential while also raising urgent challenges such as content authenticity, AI system security, ethical use, and governance. These challenges span a wide range—from the spread of deepfakes and misinformation, to new attacks on AI systems like prompt injection, and compliance with legal regulations—making them difficult for any single company or technology to solve alone. Fujitsu believes that for the healthy development of AI technology, international collaboration and a multifaceted approach leveraging diverse expertise are essential.
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Hello. We are Sonoda and Yoshii from Data & Security Research Lab. We are excited to introduce one of our latest research projects!
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Hello! We are Matsumura, Nishiguchi, and Yamazaki from Fujitsu Research. In our project, we are engaged in research and development of Materials Informatics (MI) with the aim of solving customer challenges related to materials technology.
In this Materials Informatics special feature, we introduce a case study where we utilized the knowledge distillation function integrated into GeNNIP4MD [1], a tool we developed for creating neural network potentials for Molecular Dynamics (MD) simulations. We conducted simulations of all-solid-state battery solid electrolyte interphase (SEI) formation using MD simulations of a solid-solid interface model exceeding 120k atoms, consisting of a solid electrolyte membrane and a negative electrode metal.
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In recent years, the number of artificial satellites has dramatically increased, making space-based services more accessible. Satellites are already integrated into our daily lives, from smartphone location services to weather forecasts. However, many challenges remain in leveraging satellite data. For instance, obtaining data for specific locations at specific times can be difficult, the time from data acquisition to usability can be too long, or the costs can be excessive. Fujitsu Limited, as an ICT (Information and Communication Technology) company, is advancing research and development to overcome these challenges and make space data more user-friendly and valuable. This article will explain Fujitsu's "Space Data On-Demand" concept and the three innovative technologies enabling it, in an easy-to-understand manner for those less familiar with the technology. We will particularly focus on "Satellite Edge Computing," which performs AI processing on satellites, introducing its mechanisms and potential.
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Remark: This document was translated using generative AI technology.
Hello. I'm Ogata from the Converging Technologies Laboratory. We are developing "Supply Chain Digital Rehearsal" technology to support companies' mid-to-long-term strategic decision-making. I'd like to introduce the features of this technology and our joint verification with a major food company targeting their product supply chain.
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Remark: This document was translated using generative AI technology.
Hello, we are Asai, Akima, and Takemori from the Artificial Intelligence Laboratory. In this article, we introduce our work on collaboration technologies for multi-agent systems — a key challenge when applying such systems to real-world settings where agents (often representing different organizations) have diverse and sometimes conflicting objectives.
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Hello, we are Myouken and Hatazoe from the Converging Technologies Laboratory. We have developed technologies that support "high-quality, rapid visualization of the ocean environment" and "multifaceted policy planning and implementation" for initiatives such as the environmental conservation of blue carbon ecosystems [*1], which are expected to contribute to decarbonization and the preservation of the ocean environment. By leveraging these technologies, we have successfully obtained J-Blue Credit® [*2] certification [*3]. In this article, we will introduce the technologies that constitute "high-quality, rapid visualization of the ocean environment."
*1:ecosystems that absorb and store CO2 in the ocean, such as seaweed and seagrass beds, salt marshes/tidal flats, and mangrove forests
*2:a type of carbon credit issued by the Japan Blue Economy Association for projects utilizing blue carbon
*3:Uwaijima Initiative! Collaborative Blue Carbon Project for Eelgrass Restoration by Fisheries Cooperatives, Communities, and Local Government(Japanese)
Hello, I’m Kazuki Osamura from the Spatial Robotics Research Center at Fujitsu Laboratories.
We are developing a Spatial World Model, a core technology showcased at Fujitsu Technology Update（FTU2025）, enabling human–robot collaboration in real-world environments.
We also released an official press release on the Spatial World Model today.
I would be grateful if you could read it together with this article.
Hello! We are researchers from the Converging Technologies lab at Fujitsu Research. We are presenting a novel semi-automated solution to the problem of data conversion and integration into desired structures and formats. In data-driven systems, integrating disparate data sources becomes challenging when incoming data doesn’t conform to the system’s data specifications. Despite advances in automated schema matching systems, data integration tasks involving complex semantic interrelationships still require users to manually identify and define elaborate transformations between datasets. This process consumes a huge amount of manual time and effort and remains a bottleneck in modern data integration workflows. Our DataSemantics technology employs an AI-driven human-in-the-loop system to automate the end-to-end data conversion. It uses LLMs to analyze semantic relationships and generate step-by-step transformation pipelines autonomously, while only requesting the user’s attention to resolve specific semantic ambiguities through its user interface.
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Hello. We are Taro Togawa and Takao Nakagawa from Artificial Intelligence Laboratory in Fujitsu Research.
To promote the use of generative AI at enterprises, Fujitsu has developed a generative AI framework for enterprises that can flexibly respond to diverse and changing corporate needs and easily comply with the vast amount of data held by a company and laws and regulations. The framework was successively launched in July 2024 as part of Fujitsu Kozuchi (R&D)'s AI service lineup. In this article, we will focus on the transformation in system operations and maintenance brought about by generative AI, and introduce Test Specification Generation Technology, which automates the creation of test cases from existing design documents.
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