Announcement for capital tie-up between FANUC CORPORATION and Preferred Networks Inc.
August 21, 2015
Preferred Networks Inc.
FANUC CORPORATION (FANUC, President and CEO: Dr. Yoshiharu Inaba) and Preferred Networks Inc. (PFN, President and CEO: Toru Nishikawa) came to an agreement on capital tie-up to promote collaboration in the research and development, utilizing machine learning and deep learning technologies, which will enable machine tools and robotics to further enhance intelligence.
* Overview of PFN
PFN is engaged in development of technologies such as "Edge-heavy computing", "Distributed intelligence", "Machine learning & Deep learning", "Video analytics", and "Sensor fusion" in order to make everything intelligent and to achieve high-level distributed intelligence, which may create business from real-time machine learning with a focus on the IoT. (Capital 112 million yen)
1. FANUC's Investment to PFN
- Amount of investment: 900 million yen
- Stock holding ratio: equivalent to 6.0% of the total issued stock of PFN
- Method of stock acquisition: allocation of new stock issued by PFN to a third party
- Planned date of stock acquisition: Before end of September, 2015
2. Background of business alliance and capital tie-up:
IoT has attracted a lot of attention as a key technology to support next generation manufacturing technologies such as Industry 4.0 or Industrial Internet. With the rapidly increasing amount of data, question on how to utilize big data, or how to process the data in real time, remain unsolved.
To resolve this question, we focus on machine learning and deep learning technologies which intelligently process big data at edge, at real time and enable high level of automation at manufacturing sites such as machine tools and robotics. Following previously announced business alliance, we have come to the agreement on capital tie-up in order to promote this approach.
3. Direction aimed by business alliance and capital tie-up:
Application of machine learning and deep learning has been limited to cyber space. It has not been applied to machine tools and robotics in physical manufacturing sites. We will combine PFN’s expertise in machine learning and deep learning, and FANUC's numerous technologies in the machines and robotics. We will work to achieve an unprecedented level of sophistication of automation processes in many layers of manufacturing sites including those where Industry 4.0 applies. The alliance with PFN will cover FANUC's operations as a whole. Here are some examples that we are aiming for.
Machine Tools and Robotics perform the followings:
- Self learning and cooperation
- Self learning of the method of cooperation
- Self detection of deficiencies and self repair
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Fujii, Public Relations