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Enhancing R&D Capabilities Through AI Data Infrastructure

As technology continues to evolve, the integration of Artificial Intelligence (AI) and machine learning into research and development (R&D) has become paramount. The AiR Technology Education Institute is hosting an important seminar aimed at equipping professionals with the necessary skills to manage and utilize data effectively within R&D departments.

Seminar Overview

The seminar titled "Optimizing Data Infrastructure Construction and Management for 'AI & Machine Learning' x 'Traditional Research and Development'" is designed to address the pressing challenges in data sharing and utilization within R&D environments. With the rapid adoption of IoT and AI, the management of experimental data and research information is increasingly becoming personalized, making it difficult to share and leverage insights across teams. This lack of effective data management not only slows down research efforts but also complicates the reuse and validation of past findings.

The seminar is open to participants via online live streaming (Zoom) and will also be available for archived viewing. It will take place on October 7, 2026, from 10:30 AM to 4:30 PM (local time), with replay options available from October 9 to October 23.

Key Details

• - Cost: 49,500 yen (including tax) per person, with discounts available for groups.
• - Instructor: Hiroshi Ueshima (CEO of Quattro Eye Science)

What Participants Will Learn

During this seminar, participants will:

• - Analyze the current status of data sharing in R&D departments.
• - Identify the causes of personalized data management.
• - Explore strategies to transition from personalized data accumulation to effective sharing frameworks.
• - Discuss the benefits and challenges of utilizing AI and machine learning in experimental research.

The following topics will be covered in detail:
1. Introduction
- Overview of the instructor's experience in R&D and data management efforts.
2. The Reality of Data Sharing in R&D
- Current state of data accumulation, the causes of personalized management, and arising issues.
3. Realizing Data Sharing and Expected Outcomes
- Methods to extract personalized data management and enhance research activities through shared reports.
4. Data Accumulation with a Focus on Exploration and Analysis
- Techniques for effective data accumulation by emphasizing data exploration, analytical processes, and team composition.
5. Incorporating AI and Machine Learning into Research
- Characteristics of machine learning and its integration into experimental research, along with crucial considerations.
6. Challenges in Database Integration and Maintenance
- The necessity of databases in R&D and considerations during integration with machine learning, plus common challenges and solutions post-implementation.
7. Conclusion
- Summary of key points for advancing data sharing and utilization in R&D.

Who Should Attend?

This seminar targets:

• - Professionals who have established an environment for AI and machine learning but have struggled to transition into practical application.
• - R&D department members who face challenges related to data sharing and utilization.
• - Individuals seeking methods to analyze accumulated research data effectively.
• - Anyone interested in understanding the general state of data sharing in R&D or wanting to leverage AI in their research data.
• - Participants debating whether to implement a database or data-sharing system.

For more details on this seminar, visit AiR Technology Education Institute Seminars.

The AiR Technology Education Institute aims to continuously provide valuable knowledge and skills tailored for professionals in the manufacturing industry through seminars, e-learning, training, and publications.

For more information about Japan AiR Inc., visit Japan AiR or the AiR Technology Education Institute at AiR Education. Our headquarters are located at:

• - 15-1 Kanda-Iwamoto-cho, Chiyoda-ku, Tokyo, Japan 101-0033
• - Phone: 03-6206-4966