After the outbreak of the novel coronavirus, AI has become a special “rebel”, and the increasingly mature application of AI has become more prominent in treating patients and prevention and control work.
Intelligent delivery robot, intelligent robots, unmanned express delivery vehicles in use, protect safety and health of front line workers, to a certain extent, use of the AI technology of type thermal imaging temperature measuring instrument to ensure the rapid real time temperature measurement of public places, AI + data successful back seal in wuhan city on the eve of the 5 million population, It has helped the smooth progress of epidemic prevention and control. As Joseph Krutch, a famous American writer, said, technology made a large population possible, and now a large population makes technology indispensable.
AI’s outstanding performance in responding to health emergencies helped it become the center of the storm, and became one of the key points for many small and medium-sized enterprises severely affected by the epidemic to break out of the storm. It can be predicted that AI will usher in a more rapid development after the end of the epidemic, and a wave of innovation is coming.
The success of AI in the epidemic could not have been achieved without the efforts and efforts of many technicians, and the future development of AI will also benefit from their exploration and research. From the hardware layer, data layer, algorithm layer, then to the perception layer and cognitive layer, and finally to the application and service layer, a mature AI application faces difficulties and twists and turns in the process of evolution, and problems are always accompanied by development.
For example, the complexity of AI model training leads to the slow and inefficient development of most AI models, which seriously affects the flexibility of business. If the industry wants to make progress, it is necessary to ask questions and solve problems. How to mine knowledge quickly from massive documents and data? How to make it easier to develop, deploy and implement software? How to achieve end-to-end process simplification of machine learning lifecycle? Whether a series of problems derived from this can be solved is related to the future development of enterprises, which is worth pondering by industry practitioners.
AI, meanwhile, under the help of the rapid development of the IoT, smart IoT era seems to be coming, but many problems also began to show, a lot of Internet of things device was hacked for safety is not strong extortion and destroyed, and some equipment can be used to form a botnet and even lead to a wide range of network attacks, even lead to leakage of users’ privacy. Security is a top priority for iot projects, with some companies citing the complexity and technical difficulties of iot solutions as the biggest obstacles to further deployment, while others cite a lack of professional talent and skills training as the biggest challenges.
There is no shortcut to success, but standing on the shoulders of giants is a shortcut to success.
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