Яндекс Метрика
Мультимодальная модель, Языковая модель, Компьютерное зрение

Gauss2

Samsung
Генерация текстаОтветы на вопросыГенерация кодаВизуальные ответы на вопросы

Samsung Gauss2 представляет собой второе поколение мультимодального ИИ, способного одновременно работать с текстом, программным кодом и изображениями. Модель оптимизирована для экосистемы устройств Samsung, обеспечивая высокую скорость работы и улучшенный пользовательский опыт.

During the keynote address, the company unveiled Samsung Gauss2, the second generation of its proprietary AI model, and highlighted its improved performance, efficiency and various application possibilities. “Samsung Electronics is committed to developing cutting-edge software, including AI and data analytics, to enhance user experiences,” said Paul Kyungwhoon Cheun, President, CTO of the Device eXperience (DX) Division and Head of Samsung Research. “With three distinct models, Samsung Gauss2 is already boosting our internal productivity, and we plan to integrate it into products to deliver higher levels of convenience and personalization.” Samsung Gauss2 is the follow-up to the company’s internal generative AI model unveiled last year, and it offers improved performance and efficiency in simultaneously handling various data types as a multimodal model integrating language, code and images. It is available in three distinct models catered to different purposes: Compact, Balanced and Supreme. The Compact model is a small-sized model specifically designed to work efficiently, even in limited computing environments. It delivers optimized performance for on-device usage by maximizing the utilization of the device’s computing resources. On the other hand, the Balanced model focuses on achieving balance between performance, speed and efficiency, providing consistent performance across diverse tasks. Finally, the Supreme model aims for top-notch performance by leveraging Mixture of Experts technology1 atop the Balanced model, allowing significant reductions in computational costs during training and inference processes while maintaining high levels of both performance and efficiency.

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