2019年10月25日、CTC Forum 2019＠品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者：勝山 公雄（Senior Manager, Global Data Supervisory Department, Rakuten, Inc.）
The document discusses various methods for robot navigation from simple to complex. It begins by explaining turtle graphics and sensor feedback methods. It then introduces using a coordinate system and estimating the robot's position to define waypoints and goals as coordinates. Commonly used waypoint navigation is explained along with automatic waypoint generation using RRT. Finally, it covers using graph searches like Dijkstra's algorithm and potential fields to optimize the path planning. The focus is on moving from object-based to coordinate-based representations and selecting rational routes.
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
1) Machine learning can help rationalize the "experience and intuition" of chemical research by finding patterns and exceptions from large amounts of chemical data to predict new materials and phenomena.
2) While in theory chemical structures and properties can be described by Schrodinger's equation, it is impossible to solve for realistic systems, requiring approximations. Machine learning may help address this challenge.
3) Chemists have successfully created compounds with desired properties through "experience and intuition", which involves inductive reasoning from experiments rather than purely deductive logic, incorporating serendipitous findings.
2019年10月25日、CTC Forum 2019＠品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者：勝山 公雄（Senior Manager, Global Data Supervisory Department, Rakuten, Inc.）
The document discusses various methods for robot navigation from simple to complex. It begins by explaining turtle graphics and sensor feedback methods. It then introduces using a coordinate system and estimating the robot's position to define waypoints and goals as coordinates. Commonly used waypoint navigation is explained along with automatic waypoint generation using RRT. Finally, it covers using graph searches like Dijkstra's algorithm and potential fields to optimize the path planning. The focus is on moving from object-based to coordinate-based representations and selecting rational routes.
The document provides a self-introduction by Takigawa Ichigaku, who specializes in machine learning and data-driven natural science research, particularly those involving discrete structures. It outlines his work experience and current affiliations with RIKEN and Hokkaido University. It then previews the topics to be covered in the talk, including machine learning applications in molecular representation and chemical reaction design, as well as challenges in interpreting machine learning models.
1) Machine learning can help rationalize the "experience and intuition" of chemical research by finding patterns and exceptions from large amounts of chemical data to predict new materials and phenomena.
2) While in theory chemical structures and properties can be described by Schrodinger's equation, it is impossible to solve for realistic systems, requiring approximations. Machine learning may help address this challenge.
3) Chemists have successfully created compounds with desired properties through "experience and intuition", which involves inductive reasoning from experiments rather than purely deductive logic, incorporating serendipitous findings.
