Top 13 Artificial Intelligence (AI) Trends To Watch

Although the Covid-19 pandemic has impacted many aspects of our lives and business, however, it hasn’t diminished the impact of AI on our lives.

AI has seen rapid development and deployment in the last few years. Undoubtedly AI remains a key trend when it comes to picking the technologies that will change how we live, work, and play soon. Self-teaching algorithms and smart machines will play a big part in our lives going forward.

So, here’s an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.

1. AI
is growing fully commercialized, bringing profound changes in all industries.

AI technologies have been deployed in financial, healthcare, security, and several other areas, with widening application scenarios. The commercialization of AI is playing a positive role in accelerating business digitalization, improving industry chain structures, and enhancing information use efficiency.

2. AI has entered an age of machine learning, and the future of AI development will depend on the integration of key technologies and industries.

AI has been associated with breakthroughs in research methods, and deep learning is one of the most important technological breakthroughs of machine learning. As AI research and application continues to expand in scope, AI will see deeper integration and application of more technologies in the future.

3. AI investment is returning to reason, with underlying technologies and easy-to-deploy applications more favored by AI leading institutions.

As the investment and business communities deepen their understanding of AI, the AI investment and financing. The market becomes more rational, with reduced frequency of investment and financing but continued increase of investment amounts. Especially after a round of competition within the industry, underlying technology companies and deployable application areas, such as startup projects in healthcare, education, and autonomous driving, continue to be favored by leading AI institutions.

4. Cities are the vehicle that carries the innovation, integration, and application of AI technologies, and also, the center where humans build up a full sense of AI technological experience.

Different cities perform differently in the top-level design, algorithm breakthrough, factor quality, integration quality and application quality, forming diverse and individualized AI development models.

5. Policies and capital are driving the Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta to be regions with the most AI companies, with Beijing and Shanghai taking the lead.

Shanghai put in place measures in terms of tax incentives, capital subsidies, talent attraction and government process improvement to enhance its business environment, and has attracted a large amount of investment and financing capital, AI companies, and talents, significantly enhancing its R&D strengths. This would also, facilitate the scale-up of upstream and downstream enterprises on the AI industry chain and help strengthen the city's AI industry capabilities.

6. First-tier cities represented by Shanghai and Beijing have long been leading in terms of the number of talents and enterprises, capital environment, and R&D strengths.

Beijing and Shanghai each have more than 600 AI companies, and Shanghai has established enterprise AI labs with tech giants Tencent and Microsoft, as well as AI unicorns SenseTime and Yixue Education—Squirrel AI.

7. AI is driving the financial industry to build a broader high-performing ecosystem with enhanced business efficiency for financial enterprises and transformed the enterprise process of internal operation.

Traditional financial institutions and tech companies are working together to promote deeper penetration of AI in the financial industry, restructure services framework, increase service efficiency, and reduce financial risks while providing individualized services to long-tail clients.

8. As the application of AI in education further deepens, application scenarios are shifting to cover full process of teaching.

Among the types of AI applications in education, AI-adaptive learning is applied most widely in all the learning processes. Also, the intelligent adaptive learning system is expected to catch up from behind benefited from China's large population base, shortage in education resources, and commitment to education.

9. Digital government is mainly driven from the top down achieve digitalization goals of government processes to accelerate the intelligent transformation of the government.

The needs for digital government construction vary by region, thus enterprises require customized solutions. As the threshold for the public security sector grows higher, the strong remain strong in the sector with deepened industry convergence.

10. The auto industry dominated by autonomous driving will see a transformation of its industry chain. The production, channels, and sales models of traditional automakers will be replaced by emerging business models.

The boundary between rising tech companies of autonomous driving solutions and traditional automakers will be broken. With the emergence of shared cars, shared mobility under autonomous driving will replace the concept of private cars. The development of industry norms and standards for autonomous driving will facilitate the emergence of more secure and more convenient unmanned cargo delivery and logistics.

11. The potential of AI application in manufacturing have been underestimated, and quality data resources are not fully utilized.

As a highly specialized industry, manufacturing requires highly complex and customized solutions. Thus, AI technologies in this sector are mainly applied in areas that are easy to duplicate and expand, such as product quality control, sorting, and predictive maintenance. However, the massive data—reliable, stable, and updated constantly—generated by production facilities have not been fully utilized. Such data could provide good examples of machine learning for AI companies to address actual problems in the manufacturing processes.

12. Application scenarios in retail have developed from separation to convergence and traditional retailers are partnering with startups to build scenarios around humans, goods, stores, and supply chains.

The development of AI diversifies in each retail link, and application scenarios become fragmented and enter a period with scale pilots. Traditional retailers begin to engage AI technologies and will compete with tech giants in big data applications and AI, which means retailers would be more proactive in forming partnerships with startups.

13. AI applications in the healthcare sector are growing rapidly, but the sector needs to establish standardized mechanisms of market entry for AI products and speed up the construction of the healthcare database.

The emergence of AI will help address the shortage and uneven allocation of medical resources as well as many other livelihood issues in the healthcare sector. However, in a strictly regulated sector that concerns people's life and health, whether AI could be applied extensively as expected will depend on the development of medical and data regulation standards.

Industry upgrades driven by the AI technology


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Industry Upgrade driven by AI