As an important driving force of the new round of scientific and technological revolution and industrial revolution, AI is profoundly changing people’s way of life, work, education and learning. However, the speed of AI application landing does not match the speed of technological breakthrough and innovation, and the gap between supply and demand of AI talents is further expanding.
ai develops rapidly, but landing is limited
At the opening ceremony of the Beijing Winter Olympics, the finale program “snowflake” made a stunning appearance. With the support of powerful computing power and efficient AI algorithm, AI made the purity of ice and snow and the vitality of spring complement each other, meeting the requirements of “fewer people but not empty, ethereal and romantic” for the opening ceremony of the Winter Olympics with the help of science and technology.
“The amazing performance of the Winter Olympics makes the strong potential of AI technology more intuitively presented to the audience, and then makes the public have unlimited reverie about this technology. However, the continuous iteration of AI technology has not alleviated the difficulty of its industrial application.” Wang Chao, a researcher at Wenyuan think tank, said in an interview with reporters that for a long time, data, computing power and algorithms have been regarded as the “troika” of AI development. However, for a large number of small and medium-sized enterprises, they have become a difficult threshold to cross, restricting the further development of AI industry and even the process of industrial digital transformation.
Under the tide of industrial digitization, AI algorithm has extremely high requirements for R & D ability, and the data mastered by small, medium-sized and micro enterprises do not have advantages. In addition, the training of AI model depends on a large amount of data, so the improvement of the requirements for computing power further pushes up the cost of computing power. All these make it difficult for AI technology to be effectively applied.
Recently, Tesla‘s first AI open innovation center was launched. While focusing on building an AI computing center, it also has the ability of algorithm incubation, scientific research sharing and talent training, which quickly attracted attention.
“The establishment of the science and innovation center is intended to fully connect the academic ecology and industrial ecology, so that the academic ecology can develop corresponding pre training models based on industrial data, and provide small, medium-sized and micro enterprises with the core elements such as computing power, data and algorithm models required by AI in the way of ‘cost sharing’.” Liu Bin, senior vice president of Tesla Union, said in an interview with reporters that this move can enable enterprises with different sizes and different AI bases to incubate their own intellectual property algorithms and call existing mature algorithms in a low code and modular production mode according to their own needs through the models developed by academic institutions, so as to promote more efficient AI industry practice.
deepen AI education and training system to feed industrial demand
Goldman Sachs recently released the global AI industry layout, which predicts that China’s AI talent gap will exceed 5 million by 2030. As vocational education has become a new choice for educational development under the double reduction policy, how to cultivate professionals and feed back the demand for industrial talents has become a new exploration path for the development of AI industry and vocational education.
In the joint training of AI talents in Colleges and universities, Baidu has actively invested in AI teaching, training, data, computing power, algorithms, engineers and other resources to jointly cultivate innovative and practical AI talents with colleges and universities. At present, Baidu has signed college level in-depth cooperation agreements with more than 100 colleges and universities, and some 985 colleges and universities have signed school level strategic cooperation agreements.
Dr. Ma Yanjun, general manager of Baidu AI technology ecology, said that in addition to giving all-round talent training support to national campuses in research, teaching, learning and other aspects, Baidu has successfully jumped to the first flying paddle platform with comprehensive market share of China’s deep learning platform and actively carried out social AI talent training.
The Ministry of education, the national development and Reform Commission and the Ministry of Finance issued several opinions on building “double first-class” universities, promoting discipline integration and accelerating the cultivation of Postgraduates in the field of artificial intelligence in January 2020, which proposed to build a training system that pays equal attention to basic theoretical talents and “artificial intelligence + X” compound talents, and strive to improve the cultivation level of Postgraduates in the field of artificial intelligence. Including Baidu, Alibaba, Tencent, Huawei and other Internet and AI leading enterprises, have joined hands with colleges and universities to speed up the training of AI talents.
At the same time, under the “double reduction” policy, primary and secondary schools have gradually incorporated AI into one of the teaching contents. On March 24, based on the questionnaire sampling survey of primary and secondary school principals, teachers and students in 25 provinces and cities across the country, the authoritative blue book on Artificial Intelligence Education in 2022 showed that more than half of the surveyed schools have opened or are preparing artificial intelligence education and teaching activities.
At the time of the opening of the science and innovation center, Tesla also upgraded its talent strategy and actively built a perfect “blue collar” Ai education and training system. Liu Bin said that AI talents mainly include R & D talents and technical application talents. To solve the application problem of AI industry, a large number of technical application talents who can promote the industrial implementation and application are needed. “This is the goal of AI popularization, which is also the guarantee of AI popularization.”
After more than 60 years, AI has developed from the initial stage of computing and perception to the cognitive stage. The breakthrough in the next stage is inseparable from the application landing closer to the market. What the industry needs to do is to let more players participate in the wave of AI development and help AI become more “grounded”.
