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AI adoption has skyrocketed during the COVID-19 pandemic. According to the results of a global survey of more than 5,000 companies conducted by a global decision intelligence company in 2021, 43% of enterprises reported that their companies accelerated the introduction of artificial intelligence due to the new crown epidemic.

Second, cloud services accelerate the adoption of AI. Tractica predicts that by 2025, AI will account for as much as 50% of total public cloud service revenue. The research firm also expects the global AI market revenue to grow at a rate of 57% annually.

Separately, a 2021 global survey of more than 3,000 participants published by O’Reilly revealed that the second most important industries for AI applications are technology, finance, and healthcare.

While enterprises are deploying AI at scale, several concerns remain common, including AI security and compliance.

Gartner predicts that by 2026, organizations implementing AI transparency, trust, and security will see a 50% improvement in the outcomes of their AI models in terms of adoption, business goals, and user acceptance. Gartner findings indicate that organizations have deployed hundreds or thousands of AI models that IT leaders cannot explain. A lack of knowledge and understanding can have serious consequences. The impact of underperforming AI models is magnified when dependencies increase. Organizations that do not manage AI risk are more likely to experience negative AI outcomes and breaches. Models will not perform as expected, and there will be security and privacy concerns, financial and reputational damage, and harm to individuals. Mis-executed AI can also lead organizations to make poor business decisions.

Actively “embracing” artificial intelligence has become the consensus of many enterprises to transform and upgrade. Zhu Hui, general manager of the Customer Success Division of IBM Greater China, said that IBM conducted a globalAIAccording to the survey adopted by the index, the adoption of AI by global enterprises is growing steadily and showing a more mature and gradual trend. Zhu Hui revealed that about 60% of enterprises in China have begun to actively deploy artificial intelligence solutions in their business; 99% of enterprises believe that artificial intelligence solutions can be built, deployed and run anywhere, which is very important for enterprises; About two-thirds of enterprises are using or considering automation software and tools to improve the efficiency of IT operations and business processes. Zhu Hui said that the implementation of AI provides effective support for the innovation of enterprises and will become the general trend.

The “White Paper on Artificial Intelligence Development in 2022” issued by the Shenzhen Artificial Intelligence Industry Association shows that by the end of 2021, the scale of Shenzhen’s core artificial intelligence industry will have reached 31.2 billion yuan, and the number of artificial intelligence-related enterprises has reached 1,432, ranking third in the country. The total number of authorized invention patents in the field of artificial intelligence in Shenzhen reached 41,000. The number of authorized artificial intelligence invention patent applications is 6,586, ranking second in the country. The “Shenzhen Special Economic Zone Artificial Intelligence Industry Promotion Regulations” has been implemented since November 1. With the promulgation of policies and regulations conducive to Industrial development, AI-related scientific and technological achievements will continue to be implemented and integrated with the real economy at an accelerated rate, boosting the transformation and upgrading of traditional industries.

The method of machine learning solves the problems faced by artificial intelligence. Machine learning is through some algorithms that allow computers to automatically “learn”, analyze and obtain laws from data, and then use the laws to predict new samples.

The method of machine learning solves a series of problems faced by artificial intelligence. Machine learning is the continuous training of computer algorithms to discover patterns and correlations from large data sets, and then make the best decisions and predictions based on the data analysis results. Machine learning applies real-world experience to improve its capabilities, and the more data it has, the more accurate it will be.

In the industrial field, machine learning is a branch supported by artificial intelligence. Both deep learning and neural network are subcategories of artificial intelligence. In it, the accuracy of prediction is almost the same as that of manual judgment by using deep learning algorithms to calculate data training models. Applying deep learning algorithms to industrial visual inspection Robots can greatly improve operating performance and realize the automation and unmanned manufacturing process.