Hacarus specializes in AI (Artificial Intelligence). We use Sparse Modeling as the main method for extracting meaning from small data sets.
When fast and efficient results are expected, our technology makes a strong difference.
Sparse Modeling has several key benefits compared to Deep Learning – which is the most common approach in AI:
Hacarus’ Sparse Modeling Technology
Common Deep Learning AI Technology
Proven by Results – Our Sparse Modeling Technology works
Our technology is based on Sparse Modeling. That means it focuses on only the most essential elements of the underlying information. With this approach, the complexity of the data analysis is reduced. As a result, we can derive meaningful information from small amounts of input data.
Here is how it works
If you have only a few samples with a large variety of characteristics, it’s generally impossible to apply AI algorithms with reasonable performance. However, focusing on a few really important characteristics to outcomes can solve the issue. Sparse Modeling has the capability to identify such important characteristics automatically. with very reasonable accuracy in prediction or classification of the problem.
Another feature of Sparse Modeling technology is that it can clarify the causal relationships between different pieces of data. We can use it to derive “the correct data itself”, which is not possible with deep learning.
Limitations of Sparse Modeling
Depending on the use case, the benefits of our technology cannot always be directly applied. Some AI scenarios, such as autonomous driving or natural language processing, require Deep Learning.
In these types of scenarios, our technology can be combined with Deep Learning to combine the benefits of both approaches.
Our experts are here to help. Please contact us to find out how Hacarus’ technology can be applied for your specific set-up.
Since 2016, we have successfully applied the newest scientific insights to develop and enhance our Sparse Modeling technology for different use cases. This makes Hacarus a pioneer in machine learning with Sparse Modeling.
Chief Science Advisor
Masayuki OHZEKI is an associate professor at the Graduate School of Information Science at Tohoku University. His research interests are broad, including machine learning and its potential, not only in terms of theoretical physics, but in a variety of other fields as well. He is actively promoting “Sparse Modeling” techniques. In 2008, he graduated with a Ph.D. in physics from Tokyo Institute of Technology and subsequently spent one and a half years as a postdoctoral fellow. He worked as an assistant professor in the Kyoto University and also as a researcher at Rome University. Since 2016, he has held his present position. He was awarded the 6th Young Scientists’ Award of the Physics Society of Japan, and the Young Scientists’ Prize by The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology in 2016.
Kaoru KAWAMOTO was appointed as a professor at Shiga University’s Department of Data Science in April 2018. Prior to his current role, he was the chief of Osaka Gas’ Business Analysis Center. Through his work and influence, Osaka Gas became a data-centric organization, that guides all strategic decisions of the company based on data. Mr. Kawamoto received many awards for his achievements, including the Nikkei Information Strategy’s Chosen Data Science of the Year. One of his publications, “How to Change a Company Through the Power of Analysis” (Koudansha Modern Age Paperback) is very popular among data science experts and recently became the topic of many lectures and media appearances.