Solving social issues through the Industry AI Cloud powered by IOWN

The Rapid Rise in AI Utilization

The emergence of generative AI has accelerated the adoption of artificial intelligence at an unprecedented rate. For instance, ChatGPT amassed 100 million users within two months—a stark contrast to Facebook, which took 54 months to achieve the same milestone. This highlights the extraordinary pace at which AI is advancing.
The AI market is projected to grow 20-fold from its 2021 levels, reaching $1.8 trillion (approximately ¥280 trillion) by 2030 ^[1]. Within businesses, AI adoption has progressed significantly, particularly for improving productivity in general-purpose tasks.
According to a 2024 survey by Japan’s Ministry of Internal Affairs and Communications, more than 90% of companies in the U.S. have already introduced AI, and about 60% of companies in Japan have done so. While AI is an essential tool for driving digital transformation (DX), its impact on creating new services and fundamentally transforming business models remains limited. This indicates that the full potential of AI-enabled DX has yet to be realized.

The need for AI in specialized tasks

To enhance the value of existing services and create new ones, it is vital to leverage AI in more specialized business operations. However, these specialized tasks often involve diverse factors such as data types, formats, and legal requirements, which vary by industry.
For example, the manufacturing sector deals with CAD data, while the healthcare industry relies on electronic medical records and genetic information. These types of data are often highly sensitive, and companies are cautious about their potential exposure. Strict guidelines, therefore, govern their management, particularly regarding distribution and protection.
In healthcare, sensitive data owned by individuals, medical institutions, and corporations must be securely collected and analyzed in a protected environment. For instance, within NTT Group, NTT Precision Medicine provides genetic testing services that collect genetic information to analyze disease risks, thereby contributing to advanced healthcare solutions (Fig. 1).

Application of AI to specialized tasks: Case studies in the healthcare and medical fields — Fig. 1. Case studies in the healthcare and medical fields

At PRiME-R, real-world data is collected and stored, while AI is used to structure electronic medical record data. These structured datasets are then analyzed to support research and development in fields such as pharmaceuticals. Additionally, at NTT Data, health checkup information from employees is stored in the Health Data Bank, which also incorporates personal health records, such as step counts and sleep duration, to provide advice that promotes employee health.
However, these initiatives remain confined to their respective domains. To create new value—such as delivering personalized medicine or accelerating drug discovery—it is essential to aggregate independently collected data and analysis results into a shared platform for broader application (Fig. 2).
For example, in the case of developing treatments for rare conditions, it is crucial to efficiently identify patients with relevant medical histories and provide this information to pharmaceutical companies. Traditionally, pharmaceutical firms or their intermediaries have had to individually verify such cases with medical institutions.
By combining and analyzing multiple datasets, it becomes possible to achieve outcomes that were previously unattainable with isolated data. This integration can lead to enhanced productivity across the industry and foster innovation by transforming traditional processes.

Creating new value across industries: Case studies in the medical sector — Fig. 2. Case studies in the medical industry

Co-creation through the Industry AI cloud

General business processes have already begun leveraging AI to enhance efficiency, but introducing AI into specialized operations can significantly increase their added value. Moreover, by accumulating data onto a shared platform across companies and applying AI, industries can address social challenges collectively.
This initiative, referred to as the Industry AI Cloud, involves NTT collaborating with various industry partners to foster co-creation. Below are examples of projects within this framework.

■ Co-creating the Mobility AI Platform for a zero-traffic-accident society

On October 31, 2024, NTT announced a partnership with Toyota Motor Corporation to embark on initiatives toward achieving a society with zero traffic accidents. This effort emphasizes the importance of a triadic collaboration among humans, mobility, and infrastructure and aims to develop a Mobility AI Platform to support this vision.
When vehicles continuously collect information about humans, infrastructure, and other vehicles, blind spots can be significantly reduced. With AI learning from this data, it becomes possible to predict human and vehicle behavior with high accuracy, enabling advanced driver assistance.
For instance, the Mobility AI Platform could help prevent intersection collisions in urban areas, ensure smoother merging on highways, and provide autonomous driving services in rural areas to address mobility challenges.
These advancements aim to enhance safety and security in diverse scenarios and expand the platform’s implementation nationwide. Key components of the Mobility AI Platform:

AI Infrastructure: Mobility AI trained with industry-specific data
By developing AI models based on collected data, this infrastructure supports the implementation of services such as autonomous driving and AI agents, contributing to the creation of new values.
Decentralized computing infrastructure: Distributed data centers
To manage the projected 22x increase in communication volume and 150x rise in computational demands by 2030, NTT plans to deploy data centers across Japan using its Innovative Optical and Wireless Network (IOWN) technology.
NTT currently possesses and is expanding its development infrastructure for Graphics Processing Units (GPUs) that power Large Language Models (LLMs) like tsuzumi. With the growth of the Industry AI Cloud, this GPU development infrastructure will be further extended to support various industries, including mobility.
Intelligent communication infrastructure: Reliable and seamless connectivity
The final component is the intelligent communication infrastructure. By leveraging technologies such as the low-latency, high-capacity, and energy-efficient All Photonics Network (APN) provided by NTT, AI can optimize communication, enabling seamless connectivity between humans, mobility, and infrastructure while facilitating the collection of Supporting the intelligent communication infrastructure requires a variety of technologies. For example, Cradio®, a technology that ensures consistently reliable wireless environments, is one such innovation. By predicting network quality and implementing appropriate environmental controls, it enables an optimal and natural communication environment at all times, even in dense autonomous vehicle networks.

■ Preemptive Supply and Demand Matching Through Optimization of Agricultural Product Transactions

NTT is also working on improving the supply-demand balance by optimizing agricultural transactions.
Currently, trade information in wholesale markets remains largely undigitized, with face-to-face transactions still common across Japan. This inefficiency often leads to mismatched supply and demand, increased transportation costs, and issues such as reduced agricultural product quality and food loss.
Through AI-driven simulations of supply-demand and delivery planning in virtual wholesale markets, NTT aims to optimize these transactions. By aligning supply and demand in advance using collected data, inefficiencies can be mitigated. This initiative is currently undergoing pilot testing.
In August 2024, NTT launched NTT AI-CIX (AI-Cross Industry Transformation), a new company dedicated to industrial efficiency and transformation through AI.
The new company is leading collaborations with Trial, a supermarket chain, to initially focus on shelf-space optimization and automated ordering using AI. By connecting individual AI systems, the ultimate goal is to optimize the entire supply chain of the retail and distribution industry.

Toward a Sustainable Future

As AI adoption accelerates across various domains, the issue of increased power consumption arises. AI computational infrastructure, built on large-scale data centers, consumes significantly more energy than conventional servers.
By 2030, it is estimated that energy consumption by Japan’s domestic data centers alone will exceed the total energy demand of Tokyo in 2022 (Fig. 3).

By 2050, Japan's total energy consumption is projected to exceed the levels recorded in 2022. To address this growing demand, NTT is actively pursuing research and development to create a sustainable AI-powered society. This effort includes developing the lightweight, low-power AI model tsuzumi and implementing the IOWN 2.0 energy-efficient computing infrastructure.
In 2023, NTT launched tsuzumi, an original, lightweight language model optimized for Japanese. It began customer deployment in March 2024. Depending on the type of data handled in various industries, lightweight models like tsuzumi are often better suited to specific use cases.
Since its commercialization, tsuzumi has undergone continuous advancements. To meet diverse needs, efforts are underway to enable tsuzumi to process and interpret visual data, such as photos and graphs. Beyond improving its comprehension capabilities, research is focused on empowering tsuzumi to interact with web interfaces and systems, performing tasks collaboratively with humans.
For example, if a user requests tsuzumi to order a product from an e-commerce site, the model can navigate the website, locate the desired item, complete the purchase, and subsequently access internal company systems to generate the necessary payment documents (Fig. 4).

Fig. 3. Projected power consumption of data centers in Japan

The evolution of tsuzumi continues, with plans to incorporate user feedback into future updates.
In a remarkable achievement, tsuzumi recently became the first Japanese LLM to be included in Microsoft’s Modeler Service lineup. This milestone was announced at Ignite, Microsoft’s tech conference held in Chicago, and tsuzumi became available for use starting November 20, 2024.

Progress of Low-power Computing with IOWN

The rollout of IOWN 2.0 is slated for 2025, marking a significant step toward low-power computing. This initiative integrates photonic-electronic convergence devices into computing infrastructure, aiming to reduce the energy consumption of servers and APN transmission equipment.
The computing framework for the IOWN era is termed Data-Centric Infrastructure (DCI), which reduces power consumption through two key approaches:

Disaggregated computing
Traditional servers are housed within a single unit containing multiple components. Disaggregated computing involves separating these components and enabling shared use. This allows optimal combinations of CPUs, memory, and other parts based on actual needs, while powering down unused components to conserve energy. The concept can be likened to breaking down and reorganizing server components for greater efficiency.
Photonics-electronics convergence
This approach replaces traditional electronic circuits that process electrical signals with optical waveguides, reducing the energy used for electrical wiring (Fig. 5).

Reducing power consumption of computing resources through data-centric infrastructure (DCI) — Fig. 5. Reducing power consumption of computing resources by DCI

From the IOWN Concept to reality and beyond

As part of the IOWN 2.0 initiative, NTT is advancing the development of DCI-2 (Data-Centric Infrastructure version 2) with the goal of commercial deployment by around 2026. DCI-2 leverages Composable Disaggregated Infrastructure (CDI) servers, which break down computing resources into board-level components. These components are interconnected using optical switches powered by photonic-electronic convergence devices, all optimized by a DCI controller. The target is to achieve a dramatic one-eighth reduction in power consumption.
A major milestone in this effort will be showcased at the Osaka-Kansai Expo, opening in April 2025, where NTT plans to implement these photonic-electronic convergence-based servers in its pavilion. This marks a significant step toward realizing the IOWN Concept, offering visitors the chance to witness these next-generation servers in action. Commercialization of the technology is anticipated by 2026.
Looking further ahead, NTT aims to achieve optical communication between chips by 2028 and extend this innovation to intra-chip communication by 2032, ultimately targeting a 100-fold reduction in power consumption (Fig. 6).

By building on IOWN's cutting-edge technology and harnessing the potential of the Industry AI Cloud, NTT is committed to addressing social challenges in a sustainable and transformative way.

■ Reference

[1] [1] Ministry of Internal Affairs and Communications, 2024 White Paper on Information and Communications in Japan, Chapter 9.

2024 REPORT

2024 REPORT