AVILEN's exhibition booth

At our booth, we introduced our training program to develop AI personnel who can play an active role in practical business. AVILEN Corporation conducts more than 50 training sessions every month for corporate and individual clients, and is constantly pursuing ways to educate AI personnel. At the exhibition booth, we introduced the most needed business exercises and our company's largest learning course in Japan. Many people visited our exhibition booth and consulted with us about in-house AI training. Among them, the most interested visitors were about business exercises using in-house data.

The business training exercises utilizing in-house data that attracted particular attention were

We offer a short-term training program to develop "a team that can start promoting projects tomorrow. The key is to make the training content as close as possible to the actual business. We create the most appropriate curriculum for each business and provide business exercises using internal data. A key feature of this program is that participants not only become accustomed to handling the data they will be handling on a regular basis, but also can formulate business strategies together with the AI consultants who are the instructors. We often provide long-term support for AI projects that are born through short-term training programs.

For more information on our comprehensive training program on AI business, please click here↓.
https://avilen.co.jp/ai-business-course/

In addition to exhibiting a booth at the event, AVILEN's management team will present the results of their research

AVILEN's management team and most of its members are AI engineers.

Three members of the management team presented their research results at the conference. The outline of their research is as follows

• Extraction of technical terms in chemical documents by self-learning (Yi-Ming Choi)
• Know-how site identification by classifier learning using the distribution of topics within a site (Haruhira Ohkawa)
• Prediction of Maximum Tsunami Height from Seafloor Water Pressure Data Using Gaussian Process Regression (Kotaro Takahashi)