Professor Wang Xiaogang, Co-Founder of and Chief Scientist at SenseTime
SenseTime hosted a Tech Day event, sharing their strategic plan for advancing Artificial General Intelligence (AGI) development through the combination of “foundation models + large-scale computing” systems.
Under this strategy, SenseTime unveiled the SenseNova foundation model set, introducing a variety of foundation models and capabilities in natural language processing, content generation, automated data annotation, and custom model training.
At the event, SenseTime not only showcased its large language model’s capabilities but also demonstrated a series of generative AI models and applications, such as text-to-image creation, 2D/3D digital human generation, and complex scenario/detailed object generation. Additionally, they introduced their AGI research and development platform facilitated by the integration of foundation models + large-scale computing systems.
Providing the Infrastructure for AGI
The current demand for computing power to train large models is extremely strong and continues to increase, yet the useful infrastructure is quite scarce. Over the course of five years, SenseTime has built SenseCore, a leading AI infrastructure with 27,000 GPUs and capable of delivering a total computational power of 5,000 petaflops, making it one of the largest intelligent computing platforms in Asia. With the infrastructure’s capabilities, SenseTime has trained foundation models in various fields, such as computer vision, natural language processing, AI content generation, multimodality, and decision intelligence.
Dr Xu Li, Chairman and CEO at SenseTime, said, “In the era of AGI, the three elements of data, algorithms, and computing power are undergoing a new evolution. The number of model parameters will increase exponentially, and the volume of data will grow massively with the introduction of multi modalities, leading to a continuous surge in demand for computing power. We have built the infrastructure for the AGI era with SenseCore and named our foundation model set as ‘SenseNova,’ implying ‘constant renewal, daily renewal, and further renewal’. We hope to continuously update the models’ iteration speed and their problem-solving capabilities, unlocking more possibilities for AGI.”
Professor Wang Xiaogang, Co-Founder of and Chief Scientist at SenseTime, added, “AGI has given rise to a new research paradigm, which is based on powerful foundation models, unlocking new capabilities through reinforcement learning and human feedback, therefore efficiently solving open-ended tasks. AGI will evolve from a ‘data flywheel’ to a ‘wisdom flywheel’—ultimately leading to human-machine symbiosis.”
A Host of Offerings
SenseNova offers various flexible API interfaces and services for enterprise customers, enabling them to access and utilise various AI capabilities of the SenseNova foundation models to their actual needs, with low barriers, low costs, and high efficiency.
Natural language serves as a crucial means of communication between humans and machines. SenseNova has introduced SenseChat, the latest large-scale language model (LLM) developed by SenseTime. As an LLM with hundreds of billions of parameters, SenseChat is trained using a vast amount of data, considering the Chinese context to better understand and process Chinese texts.
At the event, SenseChat demonstrated its capabilities in multi-turn dialogues and comprehending extensive texts. SenseTime also showcased several innovative applications powered by LLM, including a programming assistant to help developers write and debug code more efficiently, a health consultation assistant to provide personalized medical advice for users, and a PDF file reading assistant that can effortlessly extract and summarise information from complex documents.
Diffusion models have sparked the popularity of AIGC applications. SenseTime showcased various generative AI models and applications of SenseNova, such as text-to-image creation, 2D/3D digital human generation and complex scenario/detailed object generation:
- SenseMirage text-to-image creation platform, showcasing powerful image capabilities with realistic lighting, rich details and diverse styles, supporting 6K ultra-high-definition image generation. Customers can also train and fine-tune their own generative models tailored to their own styles.
- SenseAvatar AI digital human generation platform can create natural-sounding and natural-moving digital human avatars with accurate lip-sync and multi-lingual proficiency using just a five-minute real-person video clip.
- SenseSpace and SenseThings 3D content-generation platforms can efficiently and cost-effectively generate large-scale 3D scenes and detailed objects, providing new possibilities for metaverse and mixed-reality applications.
Providing Large-Scale Computing Power for All Tasks
Whether it is the large language model or text-to-image creation or digital human generation, they all require large-scale computing power. SenseCore has industry-leading computing power output, ultra-large model training and large-scale inferencing capabilities. It aims to be the service leader in the AGI era.
Leveraging SenseCore infrastructure and “SenseNova” foundation models, SenseTime offers a range of Model-as-a-Service solutions to industry partners, encompassing automated data annotation, customised model training and fine-tuning, model inference deployment and development efficiency enhancement:
- Automated data annotation based on pre-trained foundation models can achieve nearly a hundred times efficiency improvement compared to manual data annotation.
- Large-scale model training and fine-tuning services can help customers quickly train models using their own data, including the development of vertical models based on pre-trained foundation models.
- Model inferencing services can increase large-scale model inference efficiency by more than 100%, reducing the cost significantly.
- SenseTime also provides numerous pre-trained models and AI development toolkits to industry developers, empowering clients to enhance their development efficiency.
SenseTime will continue to advance the construction of the SenseNova foundation model set. Striving for constant renewal, daily renewal, and further renewal, SenseTime aspires to make ongoing improvements to the models in terms of data volume, parameter structure and problem-solving capabilities. Together with industry ecosystem partners, SenseTime aims to advance breakthroughs in AGI, bringing the benefits of AI to everyone.
Archive
- October 2024(44)
- September 2024(94)
- August 2024(100)
- July 2024(99)
- June 2024(126)
- May 2024(155)
- April 2024(123)
- March 2024(112)
- February 2024(109)
- January 2024(95)
- December 2023(56)
- November 2023(86)
- October 2023(97)
- September 2023(89)
- August 2023(101)
- July 2023(104)
- June 2023(113)
- May 2023(103)
- April 2023(93)
- March 2023(129)
- February 2023(77)
- January 2023(91)
- December 2022(90)
- November 2022(125)
- October 2022(117)
- September 2022(137)
- August 2022(119)
- July 2022(99)
- June 2022(128)
- May 2022(112)
- April 2022(108)
- March 2022(121)
- February 2022(93)
- January 2022(110)
- December 2021(92)
- November 2021(107)
- October 2021(101)
- September 2021(81)
- August 2021(74)
- July 2021(78)
- June 2021(92)
- May 2021(67)
- April 2021(79)
- March 2021(79)
- February 2021(58)
- January 2021(55)
- December 2020(56)
- November 2020(59)
- October 2020(78)
- September 2020(72)
- August 2020(64)
- July 2020(71)
- June 2020(74)
- May 2020(50)
- April 2020(71)
- March 2020(71)
- February 2020(58)
- January 2020(62)
- December 2019(57)
- November 2019(64)
- October 2019(25)
- September 2019(24)
- August 2019(14)
- July 2019(23)
- June 2019(54)
- May 2019(82)
- April 2019(76)
- March 2019(71)
- February 2019(67)
- January 2019(75)
- December 2018(44)
- November 2018(47)
- October 2018(74)
- September 2018(54)
- August 2018(61)
- July 2018(72)
- June 2018(62)
- May 2018(62)
- April 2018(73)
- March 2018(76)
- February 2018(8)
- January 2018(7)
- December 2017(6)
- November 2017(8)
- October 2017(3)
- September 2017(4)
- August 2017(4)
- July 2017(2)
- June 2017(5)
- May 2017(6)
- April 2017(11)
- March 2017(8)
- February 2017(16)
- January 2017(10)
- December 2016(12)
- November 2016(20)
- October 2016(7)
- September 2016(102)
- August 2016(168)
- July 2016(141)
- June 2016(149)
- May 2016(117)
- April 2016(59)
- March 2016(85)
- February 2016(153)
- December 2015(150)