ソフトウェア業界ではワクワクする新しいテクノロジーがどんどん生まれ、それが世の中で使われるまでも早くなっています。2018年に革新があった Deep Learning は、既に民主化・日常化もしてます。この講演では、そのソフトウェアの今を俯瞰し、今後どうなっていくのか? その未来予想とともに。職業として20年以上の経験を得た私の学びをお伝えします。
NTT Communications' Initiatives to Utilize Infrastructure DataDataWorks Summit
We will guide you on the status of utilization of the big data analysis infrastructure for infrastructure data, centering on initiatives at the Next Generation Platform Promotion Office.
1) Affective computing aims to expand human emotional intelligence to machines by creating socially intelligent machines that can respond appropriately according to the situation and interlocutor.
2) There are two main approaches to modeling emotions in affective computing: discrete theories that identify basic emotions like Ekman's six emotions, and continuous theories that describe emotions along dimensions of arousal and valence.
3) Empath's goal is to recognize emotions from speech regardless of language, which presents challenges of combining speech processing with emotion recognition from voice cues alone. Empath is developing methods to extract pitch, intensity, and speech rate from voice samples to train models to classify emotions.
ソフトウェア業界ではワクワクする新しいテクノロジーがどんどん生まれ、それが世の中で使われるまでも早くなっています。2018年に革新があった Deep Learning は、既に民主化・日常化もしてます。この講演では、そのソフトウェアの今を俯瞰し、今後どうなっていくのか? その未来予想とともに。職業として20年以上の経験を得た私の学びをお伝えします。
NTT Communications' Initiatives to Utilize Infrastructure DataDataWorks Summit
We will guide you on the status of utilization of the big data analysis infrastructure for infrastructure data, centering on initiatives at the Next Generation Platform Promotion Office.
1) Affective computing aims to expand human emotional intelligence to machines by creating socially intelligent machines that can respond appropriately according to the situation and interlocutor.
2) There are two main approaches to modeling emotions in affective computing: discrete theories that identify basic emotions like Ekman's six emotions, and continuous theories that describe emotions along dimensions of arousal and valence.
3) Empath's goal is to recognize emotions from speech regardless of language, which presents challenges of combining speech processing with emotion recognition from voice cues alone. Empath is developing methods to extract pitch, intensity, and speech rate from voice samples to train models to classify emotions.
1. DMM uses Apache Spark for its recommendation system, analyzing user behavior data to provide personalized recommendations.
2. Spark enables real-time analysis through APIs while leveraging machine learning libraries like MLlib and tools like GraphX.
3. DMM discusses how it leverages various Spark features including Spark SQL, MLlib, GraphX, and integration with other technologies like Hive, Sqoop and databases.
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