Tokyo, March 16, 2020: Fujitsu Limited, Fujitsu Laboratories Ltd., and Inria, the French national research institute for digital science and technology, recently announced the development of technology that automatically creates artificial intelligence (AI) models capable of detecting anomalies in time-series data taken from IoT devices and other sources.
According to a press release, leveraging proprietary time-series data analysis technology developed by Fujitsu Laboratories that utilises improved topological data analysis (TDA), Fujitsu and Inria project-team DataShape had developed a technology to automatically create AI models that can detect anomalies by extracting the necessary information from time-series data. Which means data from sensors on IoT devices or biological data such as heart rates and brain waves.
All of which consists of information of a wide range of types with complicated interconnections. This means that time-series data is often subject to severe volatility, making it difficult to discern when meaningful patterns or anomalies occur in the data.
This new technology enables any software engineer to easily create AI categorization and anomaly detection models for time-series data, while also reducing the man-hours required to one hundredth that of previous methods. This will ultimately help to accelerate the deployment of new AI models in a variety of business fields, allowing even engineers with no specialized training to create anomaly detection models.
Trials demonstrate significant improvement in AI models for real-world use through new technology
Trials were conducted using newly-developed technology for automatically generating anomaly detection models. These successfully demonstrated considerable gains in efficiency that will help accelerate the deployment of AI models to solve real-world problems, said Fujitsu.
Image credit: Fujitsu