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Artificial Intelligence Yairi - Takeishi Laboratory

Artificial Intelligence
Yairi - Takeishi Laboratory

Artificial intelligence for revealing data-generating mechanisms and monitoring health status of systems

We explore the foundational techniques for developing artificial intelligence, including machine learning and probabilistic inference, and apply them to practical challenges in various fields, such as aerospace engineering.

Unsupervised learning

We are interested in unsupervised learning for identifying cluster structures and low-dimensional latent spaces hidden in large amounts of high-dimensional data. We are studying not only the methods of clustering and dimensionality reduction but also exploring the ways to apply these techniques to tasks such as high-dimensional data visualization, anomaly detection, localization and mapping for mobile robots.

Inference and learning of dynamical systems

Dynamical systems with states changing from moment to moment are ubiquitous in natural and artificial phenomena. We are studying methods for the probabilistic inference of the states of the mathematical models of such systems, as well as methods for identifying the models from observed data. For example, we apply such methods to the reconstruction of an asteroid's shape and a spacecraft's position and attitude from images taken by an asteroid explorer. It can also be used to predict the behavior of a robot in a time series.

Data-driven system status monitoring

We apply the methods of unsupervised learning and learning dynamical systems to large amounts of sensor data from complex systems such as artificial satellites and production plants with the aim of studying techniques for monitoring whether the systems are operating properly. We are also researching methods to estimate how much longer equipment can operate normally.

Combination of machine learning and scientific models

In order to enhance the accuracy and stability of machine learning predictions, we explore the potential of combining mathematical models based on scientific theory with machine learning models. In addition to improving predictions, we also examine how such hybrid models can be interpreted and understood.

Localization and mapping using nonlinear dimensionality reduction
Estimation of asteroid shape and spacecraft position from images
Anomaly detection of satellite telemetry using unsupervised learning

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We usually focus on researching AI theory and technology, but we're also users of various moden AI tools and services. Looking at the recent evolution of AI, exemplified by LLMs and generative AI, it sometimes makes us wonder, as many probably do, if a large portion of human jobs will soon be replaced by AI. If that happens, what will be left for us, who have been taught that “labor is a virtue” ? And what, in the first place, is the meaning of human existence? We feel that a time is coming when humanity as a whole, and each individual, must confront such fundamental questions. Personally, I believe the clues might lie within the diverse legacies left by our predecessors. (Takehisa Yairi)