Sanyan Finance reported on March 31 that as a key force to promote the large-scale landing of AI applications, the importance of deep learning frameworks has become increasingly prominent. It is not only related to the wide application of industries and fields related to the national economy and people's livelihood, but also has a decisive significance for the scientific and technological security of information systems.
"In the artificial intelligence technology system, the deep learning framework is in the waist position that runs through the upper and lower levels, and it is connected to the chip and the upper application." On March 31, at the Baidu AI Open Day, "AI, I go!" At the fifth session of the event, Dr. Ma Yanjun, General Manager of Baidu's AI Technology Ecology, systematically shared the competitive landscape in the field of deep learning, the development breakthroughs and future trends of China's self-developed deep learning framework.
(Dr. Ma Yanjun, General Manager of AI Technology Ecology, shared on the spot)
Similar to the operating system Windows in the PC era, IOS and Android in the mobile Internet era, the deep learning framework is the operating system of the intelligent era, which together with the chip constitutes the infrastructure of artificial intelligence, and the deep learning framework is as important as the chip. 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. 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. 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.
How to achieve technological breakthrough in China's industrial intelligent transformation?
Domestic deep learning frameworks face three major difficulties
Deep learning frameworks are making AI applications simpler. Based on the deep learning framework, enterprises can develop AI applications faster and more conveniently according to the characteristics and scenarios of their own industries, and no longer need to build foundations from 0 to 1, which greatly improves the efficiency and level of industrial intelligence.
Whether from the perspective of AI technology development or industrial application, deep learning frameworks are in a very core position. Since 2013, the global artificial intelligence academia and various research and development entities in the industry have successively opened up their own research and development deep learning frameworks, and built an open platform for artificial intelligence with the framework as the core to promote the establishment of an artificial intelligence industry ecology. The deep learning framework represented by Google's TensorFlow and Facebook's PyTorch two deep learning frameworks started early and developed quickly, occupying a dominant position in the industry.
As early as 2017, the National Development and Reform Commission officially approved the establishment of a national engineering laboratory for deep learning technology and application, and China's deep learning framework gradually broke through from international competition. In 2021, IDC reported that Baidu Feipao, China's first open source and open deep learning platform, has surpassed other international giants in its comprehensive share in China's deep learning market and become the first in China. This makes mainland artificial intelligence technology developers and users do not have to rely on foreign platforms, and can further rely on domestic platforms to cultivate industrial ecology.
However, China's self-developed deep learning framework still has a long way to go to gain a leading position in international competition. Ma Yanjun 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, in terms of application experience, 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 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.
The deep learning framework may determine the AI industry pattern in the next 5 years
Baidu Flying Propeller has become the first in the Chinese market
In the field of global deep learning, foreign developers mainly develop, train and deploy artificial intelligence algorithms and models based on foreign deep learning frameworks such as TensorFlow, PyTorch, and MxNet. There is still a certain gap in the deep learning framework developed by Chinese intelligent enterprises in terms of community prosperity and number of developers.
However, China's deep learning framework, represented by Flying Propeller, is developing into an open source open platform that is more suitable for industrial needs and more popular with Chinese developers. On the one hand, China's deep learning framework continues to take root in practical application scenarios, firmly grasps the needs of developers and enterprises to upgrade intelligently, and reduces the application threshold of artificial intelligence technology. On the other hand, China's deep learning framework has been deeply adapted and integrated with more chip manufacturers, forming a soft-hard synergy advantage.
"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 said.
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.
(Overview of the adaptation of the propeller and the chip)
As of December 2021, Feipao has broken through the monopoly of Google and Facebook in the Past in the Chinese market and become the first in the comprehensive market share of Deep Learning Platforms in China. 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.
(Flying paddle panorama)
With the deepening of the current digital transformation of China's industries, the ecological layout of China's deep learning framework is "blossoming" in thousands of industries such as industry, transportation, energy, and cities. Taking the field of intelligent transportation as an example, the high-speed rail catenary hanging foreign objects caused by train delays occurs from time to time, and a small foreign object may affect the travel of millions of people. Previously, relying on traditional manual inspection required 10 to 20 track maintenance workers per line every day, which not only had high labor costs, but also made it difficult to ensure timely detection and processing. After some attempts, Chengdu State Railway finally developed a set of "orbital online intelligent inspection system" using flying propellers, which realized the intelligent judgment of track defects around the clock. A set of flying paddle intelligent inspection system, so that the guardians of the city no longer have to wear stars and wear the moon.
Ma Yanjun said that with the open source and opening of China's deep learning framework and the landing of larger-scale industrial applications, the application scenarios of China's deep learning framework will be more abundant in the future, and the cost and threshold will be further reduced. At the same time, the deep learning framework will be integrated with more cutting-edge industries such as scientific computing, quantum computing, and life sciences.
What cannot be ignored is that China's deep learning framework is still facing difficulties such as complex adaptation and deployment, difficult application development, etc., and the construction of an independent and controllable deep learning and artificial intelligence industry ecosystem is obstructed and long, but it may determine the AI technology pattern and industry level in the next 5 years. Ma Yanjun said: "Although the deep learning framework belongs to the competition of high investment, long cycle and ecological grabbing, it has received strategic support from the state and enterprises, and is the key to opening the next AI era. “