[October 1, 2nd free webinar for companies] Creating data sets for AI learning: How to collect and create efficient data for AI learning using tools

In recent years, efforts to improve work efficiency and save labor have expanded by utilizing artificial intelligence (AI) in various industries. On the other hand, various data-related problems are occurring as we proceed with the introduction of AI.
Looking at the results of the "Survey on the utilization status of artificial intelligence and machine learning in the industrial field and the safety of artificial intelligence technology" conducted by the New Energy and Industrial Technology Development Organization in 2018, AI Among the issues in utilizing the above, the issue that occupies the second place is "acquisition of data quantity and quality".
This time, we will provide data collection and annotation services for AI learning for the two issues related to data , "the cost of creating data is high" and "there is insufficient data". We would like to share our views and solutions based on our four years of experience in carrying out the projects of our Japanese customers.
If you are interested, please join us.
Application URL: http://bit.ly/lqa-webinar-2

Agenda
16: 00-16: 05: Introduction of speakers
16: 05-16: 45:
　　　　　　　1) Importance of data in machine learning
　                      2) How to collect data
　　　　　　　3) Efficiency of annotation work using tools
　　　　　　　4) Case Study / Example Introduction
16:
45-17
: 00: Q & A Webinar Overview Date: Thursday, October 1, 2020 16:00 – 17:00 (Reception starts 15:50) (Japan time)
Format: Zoom Online Seminar

Recommended for those who are
currently involved in AI projects
Those who want to reduce costs in AI development Those who want to
implement with AI but are in trouble due to lack of data

Speaker
Goku Chan: Lotus Quality Assurance Sales representative of Japanese market
Takuro Ohara: Marketing Department, Lotus Quality Assurance Co., Ltd.