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Lightweight and Explainable AI.

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Press releases

21Apr 21

HACARUS Brings SPECTRO to the Taiwanese Market with G4

HACARUS partners with G4 to release its award-winning visual inspection AI in Taiwan.   April 21, 2021 - Taipei, Taiwan - HACARUS, the leading provider of lightweight and explainable AI, and G4, a leader in Taiwan’s image processing industry, today…

09Apr 21

HACARUS enters the Middle East with SMARKK

By joining forces HACARUS and SMARKK are set to provide a complete platform for end-to-end innovation enablement   April 9th, 2021 – Dubai, United Arab Emirates– HACARUS, the leading provider of lightweight and explainable AI, and SMARKK, the region’s premier…

02Apr 21

HACARUS Completes Deutsche Bahn’s Startup Program

After a 100-day pilot HACARUS delivered the final results of its AI-powered radar data analysis project – firms now exploring further collaborations   April, 2, 2021 – Berlin, Germany– HACARUS, the leading provider of lightweight and explainable AI today announced…

22Mar 21

HACARUS & VeriServe Collaborate for Online XAI Seminar

We are excited to announce that HACARUS INC, along with VeriServe Corporation, will be holding a free online seminar about AI quality testing and the newest explainable AI (XAI) trends. Sessions will be held by Takashi Yamasaki, the Quality Assurance…

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Benefits of Our AI Technology

No Big Data Required

We use sparse modeling technology that can extract features with a small amount of training data or without any training data. You can use AI in areas where large amounts of training data are not available and where collecting training data is too expensive.

Explainable AI

Unlike deep learning, in which the decision-making process of AI is a black box,  ours is visualized in a form that can be interpreted by humans. Our AI provides high interpretability in medical and mission-critical areas where it is essential to understand AI’s decision-making process.

Training and Inference at the Edge

Our technology can prevent the performance degradation of AI due to environmental changes by additional learning on the edge side. Supports both learning and inference completely offline without the need for a server or internet connection.