The concept of artificial intelligence has gone from theory to reality in the past few years. From recommended algorithms for software to ai-powered voice assistants, the seeds of AI have sprouted in our lives. Today, the power of AI is slowly growing, entering traditional industries and moving in the direction of scale.
AI out of the lab
After AI technology has entered our field of vision through electronic products, we continue to explore more possibilities of artificial intelligence. In particular, it is necessary to explore the combination of artificial intelligence with traditional industries, hoping that AI can inject new energy into the development of traditional industries.
But the landing of artificial intelligence in traditional industries does not seem to be as easy as people think. Wu Enda, a well-known scholar in the field of artificial intelligence, raised a question in an article last year: Why is artificial intelligence, which is "handy" in the Internet industry, "unsatisfactory" in traditional industries? Why is the application speed and scope of AI technology in traditional industries far less than that of industries such as consumer Internet?
On the one hand, people's understanding of artificial intelligence is still insufficient. The rational use of AI capabilities in traditional industries requires innovation and continuous exploration. Only when a suitable application scenario appears, the related application of artificial intelligence will come into being.
Unmanned delivery robots, disinfection drones, and non-contact body temperature detection equipment that have emerged with the outbreak of the epidemic are good examples. When the epidemic appeared, new application scenarios in traditional industries were created, and corresponding artificial intelligence applications came into being.
The potential of artificial intelligence as a new thing in traditional industries still needs to be tapped in practice.
On the other hand, the scenes in which artificial intelligence has been widely used are different, and traditional industries have the characteristics of scale and industrialization. This requires artificial intelligence to complete "industrialization", bid farewell to "small fights", and truly move towards scale. At present, this process is also facing challenges in many aspects such as talent reserves and software and hardware ecology.
Towards scale, AI landing "industrial revolution"
At present, AI is still facing a difficult problem in the transformation to truly large-scale applications: the current entry threshold for AI is not low, especially for traditional enterprises. At present, although many traditional enterprises are quite interested in artificial intelligence, their accumulation of talents, capabilities and experience is relatively weak. As a result, these traditional companies can't really put ideas into practice.
In this case, although traditional enterprises are eager to try AI, and AI solution providers also want to expand their business scope as much as possible, there are information barriers on both sides.
On the one hand, there is a gap between the provider of AI solutions and enterprises, and it is impossible to know exactly what the needs of enterprises need and design optimization solutions according to specific needs. On the other hand, developers are not aware of the latest achievements of the provider in development tools and development solutions.
In order to solve this problem, some AI solution providers have begun to focus on innovation sharing and ecological openness. Intel's "AI Practice Day" is an attempt to communicate directly with AI solution providers and developers.
At the online "Intel Embraces Developers Software and Hardware Collaborative Innovation Ecosystem to Accelerate AI Landing" event held online on March 15, 2022, Xia Lei, chief engineer of Intel and chief architect of Artificial Intelligence Technology China, introduced the relevant situation of AI Practice Day.
Xia Lei is one of the initiators of ai practice days. He said that the original intention of the AI Practice Day two years ago was to hope that Intel could pass on the innovations it had invested a lot of energy in to developers through effective channels. We want to be able to shorten the distance from Intel to the market and to the customer.
In the process, Intel has successfully promoted the implementation of some artificial intelligence projects in traditional industries.
Intel has partnered with Goldwind Huineng in the energy sector to build a model for accurate prediction of wind energy using Intel's AI technology. Wind power generation has always had the problem of unstable power generation: when the wind is large, the power generation is large, and when the wind is small, the power generation is small.
This fluctuation in wind power output will not only produce a lot of curtailment, resulting in waste of energy, but also affect the stability of the power grid. Therefore, wind power generation requires more accurate prediction of the power output of the entire wind farm, which is conducive to the integration of wind power generation into the grid.
According to Xia Lei, Intel's AI solution has made Jin Fenghuineng's accuracy rate reach 80%, which is 20% higher than before. This means that carbon emissions can be reduced by 120 tons per day during the power generation process. A year would reduce the felling of 24,000 tons of trees.
Coincidentally, Intel's cooperation with WeiNing in the medical field has also made artificial intelligence show its might in precision medicine. In the medical field, the dosage should also be different according to each person's physical condition. With the development of modern medicine, bone age testing is helping doctors to achieve precise dosing from person to person.
Intel and Weining have teamed up to build an AI solution for bone age detection based on the Intel Xeon platform. The time to process an image can be reduced from 11 seconds to 6 seconds, and the efficiency is nearly doubled.
As Intel and developers have achieved more and more success, the nature of AI practice days has quietly changed.
Google, Amazon, Baidu, Ali and other manufacturers have also joined the ranks of ecological sharing, using Intel's AI practice day to communicate with the industry. AI Practice Days have gone from Intel's event to an industry-based ecosystem-sharing platform.
Xia Lei also mentioned that Intel's next step will be to segment the audience of AI Practice Day. For developers who focus on different fields, Intel will classify algorithm innovation and rapid deployment to provide more accurate information that different groups are interested in. At the same time, it will also open a special session for developers in different fields to provide developers with more accurate solutions.
Today, with AI moving towards scale and industrialization, the increasingly perfect ecology and open communication environment are accelerating the landing of AI. Perhaps in the near future, AI will be able to play its role in every corner of life, as people expect. Leifeng Network