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.
The document discusses a proposed method for detecting and estimating the position and orientation of deformed AR markers using machine learning. Specifically, it uses SSD to detect AR markers in images and an augmented autoencoder (AAE) to remove deformation from markers and estimate orientation based on latent variables obtained during this process. The method was evaluated on its ability to detect markers at different distances and degrees of deformation, showing improved performance over existing methods. Estimated orientation accuracy was within 2-3 degrees on average.
□Author
Masaya Mori, Global Head of Rakuten Institute of Technology, Executive Officer, Rakuten Inc.
森正弥 楽天株式会社 執行役員 兼 楽天技術研究所代表
□Description
そもそもなぜ人工知能(AI)をビジネスで活用する必要があるのかの視点に基づいて、AI活用戦略について述べた講演の資料です。
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.
The document discusses a proposed method for detecting and estimating the position and orientation of deformed AR markers using machine learning. Specifically, it uses SSD to detect AR markers in images and an augmented autoencoder (AAE) to remove deformation from markers and estimate orientation based on latent variables obtained during this process. The method was evaluated on its ability to detect markers at different distances and degrees of deformation, showing improved performance over existing methods. Estimated orientation accuracy was within 2-3 degrees on average.
□Author
Masaya Mori, Global Head of Rakuten Institute of Technology, Executive Officer, Rakuten Inc.
森正弥 楽天株式会社 執行役員 兼 楽天技術研究所代表
□Description
そもそもなぜ人工知能(AI)をビジネスで活用する必要があるのかの視点に基づいて、AI活用戦略について述べた講演の資料です。
WIT Impact Report 2016 illustrates how WIT has accelerated the impact of social entrepreneurs of disaster-hit areas of Japan and beyond, in collaboration with global expertise.
2017 media communication_mgmt_v9_0616_2017_slideshareTakao Chitose
Media Communication Session Material.
This presentation was made only for IFI Business School Master and Professional course in 2017.
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