Text/Wu Chenguang, Gao Heng
Photo: Wu Chenguang
"The deep learning framework is the key to the next ERA of AI." On March 31, at the Baidu AI Open Day, Ma Yanjun, general manager of Baidu's AI technology ecosystem, said this when talking about the importance of deep learning frameworks. He pointed out that the deep learning framework in the artificial intelligence technology system, in the waist position of connecting the upper and lower levels, connecting the chip and inheriting the application, is the operating system of the intelligent era, which together with the chip constitutes the infrastructure of artificial intelligence, and the importance is no less than that of the chip.
With the increasing importance, in the 14th Five-Year Plan, the deep learning framework was included in the field of "new generation artificial intelligence" and became a cutting-edge innovative technology supported by the state.
In the deep learning framework, which is the core technology of AI, even if it faces difficulties such as high thresholds and difficult ecological construction, Chinese enterprises must also grasp the initiative. At present, artificial intelligence has entered the stage of large-scale landing, and more and more developers and enterprises are carrying out intelligent transformation applications based on domestic deep learning platforms.
As of December 2021, Baidu's "Flying Oar" deep learning platform has broken through the monopoly of Google and Facebook in the Chinese market in the past, and has become the first in the comprehensive market share of China's deep learning platform.
However, in the field of global deep learning, there is still a certain gap in the prosperity of the community and the number of developers in the deep learning framework developed by Chinese intelligent enterprises.
In this regard, Ma Yanjun said: "Chinese enterprises and industries have their own characteristics, for example, in the fields of industry, agriculture, logistics, finance and other fields, the demand for AI technology of Chinese enterprises also has its own uniqueness. If the domestic deep learning framework can meet the needs of China's industry in a large number of functions, and at the same time, it is low threshold and simple and easy to develop, it will have a great opportunity to achieve curve overtaking on the industrial level. ”
(Ma Yanjun, General Manager of Baidu AI Technology Ecosystem)
Taking Baidu Flying Propeller as an example, after repeated polishing of a large number of real production scenarios, traditional enterprises have been able to achieve high-performance development, large-scale training, different scenarios and agile deployment of different software and hardware platforms in the intelligent transformation. More importantly, Feipao has completed the adaptation and optimization of 31 kinds of chips with 22 domestic and foreign hardware manufacturers, including Baidu Kunlun Core, Huawei Ascend, Intel, and Nvidia, covering all mainstream chips at home and abroad, helping enterprises reduce costs and increase efficiency to the greatest extent.
At present, the flying propeller platform has gathered 4.06 million developers, created 476,000 AI models, and served 157,000 enterprises and institutions in total, covering industrial, agricultural, medical, urban management, transportation, finance and other fields.
However, Ma Yanjun also pointed out that the current development of China's deep learning framework still needs to break through three key points: technical strength, functional experience, and ecological scale.
First of all, in terms of technological innovation, the research and development of deep learning frameworks requires low-level technical talents in the field of artificial intelligence, and the mainland's reserves in this field are still insufficient.
Secondly, since China is the country with the most complete industrial chain in the world, the industrial system is complex, and the transformation needs of small and medium-sized enterprises are imminent. However, in terms of application experience, in the process of applying AI and promoting the intelligent transformation of enterprises, only one technology application, from the laboratory to the industrial landing, takes at least 3-6 months, and a low threshold or even zero threshold development platform is extremely important.
In terms of developing the application ecology, deep learning is a typical co-creation technology field, and only by building its own ecology can iterate and develop continuously. However, the construction ecosystem cycle is long and the cost is high, and only when the technology and functional experience of the domestic framework are sufficient to meet the needs of developers, will there be an opportunity to cultivate an independent and innovative AI development application ecology.
Ma Yanjun believes that although there is still a long way to go, building an independent and controllable deep learning and artificial intelligence industry ecology may determine the pattern and industrial level of AI technology in the next 5 years.