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X、テレビ用アプリ「X TV App」を近日公開へ 4/24(水) 0:21配信 Impress Watch X(旧Twitter)のリンダ・ヤッカリーノCEOは23日、テレビ向けの動画アプリ「X TV App」を間もなく提供開始することを明らかにした。 【この記事に関する別の画像を見る】 テレビ向けのXの動画アプリで、「ほとんどのスマートテレビで近日中に対応」とする。トレンド動画アルゴリズムで、ユーザー向けにカスタマイズされた人気コンテンツを配信するほか、AIによるトピック抽出で、パーソナライズされた動画体験をシームレスに届けるという。 From the small screen to the big screen X is changing everything. Soon we’ll bring real-time, engaging content to your smart TVs with the X TV App. This will be your go-to companion for a high-quality, immersive entertainment experience on a larger screen. We’re still building it… pic.twitter.com/QhG6cVDpZ8 ― Linda Yaccarino (@lindayaX) April 23, 2024 また、モバイルデバイスで視聴開始し、テレビで続きを見るなどの「クロスデバイス」にも対応。動画検索やモバイルデバイスからの「キャスト」機能も搭載する。ヤッカリーノCEOは、「まだ開発途中だが、大画面で高品質で没入感あるエンターテインメントを提供する」と説明している。
Technology deve…
2024/05/17 11:22
Technology development trends - Mobility In the field of environmental recognition, advances are being made in the use of inexpensive vision cameras and depth cameras, and in understanding the meaning of acquired data.In the future, it is expected that navigation systems that can cope with unknown environments and environments with dynamic changes will be developed. In terms of control, integrated control of manipulation and autonomous mobile robots is expected. A comprehensive action plan for research and development in the field of robots and social implementation (robot action plan) Direct Visual SLAM (Kudan) By combining GN-Net feature extraction and Visual SLAM, we perform semantic peripheral recognition and realize self-position estimation that is robust to dynamic changes. Consideration of depth cameras and depth cameras is also seen (mid 2010s) 2010 Heihagi Scale 3D SLAM using LILJAK 2010 Hachishunkan The use of vision cameras has been increasing. For indoor use, the use of fat cameras has been attracting attention. There are also technologies that achieve highly accurate environmental recognition by integrating multiple types of sensor information or combining neural networks. Kudan made SLAM robust to dynamic changes by combining it with semantic understanding of the scene. Yonetani et al. developed a machine learning-paced A* search algorithm and succeeded in generating an optimal path plan even for unknown rings. The first action plan in the mobility area is to respond to unknown and dynamically changing environments such as outdoors. development of robust autonomous mobility technology. It is necessary to develop algorithms that allow robots to operate stably and autonomously even in unknown environments where there is no prior map information or in environments where dynamic changes occur.It is necessary to aim to simplify and eliminate the need for advance map creation and updating. Taken as a reference example, Yonetani et al. developed a