Technology, Healthcare Author:Yingwei Fu Editor:Luke Sheehan Jul 14, 2020 10:30 AM (GMT+8)

EqualOcean held an online event on June 30. Here is what 500 Startups' Stella Zheng and Zhuiyi Technology's Terry Ke think about the pandemic and AI.

WIM Salon X Online: AI in China: 2020 and Beyond

EqualOcean held a live webinar on the topic 'AI in China: 2020 and Beyond' with Terry Ke, Strategy Director of Zhuiyi Technology, and Stella Zheng, head of Mainland China of 500 Startups. We discussed the status quo of AI’s development in China and the challenges ahead of passionate entrepreneurs. AI has become a norm these days. Certainly worth a conversation. Below are the key insights from our webinar.

In this series of follow-up articles, we will walk through selected insights shared by our guest speakers. The topics include:

AI in China: COVID19 and AI

AI in China: Challenges

AI in China: Good Practice

AI in China: Future


EqualOcean: From Moore’s Law to low-quality data – AI has been facing multiple challenges in multiple areas. The biggest issue lies in the distance between theory and practice: the application layer has always been the priority. The practical uses of AI technology in various industries are different. Which industries, especially in China, have been the most successful in leveraging AI? And which are less successful?

Stella: AI has been utilized in various sectors, like medical, healthcare, traveling, retailing and manufacturing. It is hard to say whether if AI is successful or not in any industry, given that AI tech is at an early stage of development. It is too early to judge. Indeed, some technologies are more mature, like voice semantic recognition and image recognition.

Though we have discussed the problems in delivering AI, traditional industries have a high desire to improve AI technology. For instance, we have invested in a voiceprint developing company AISense. Their core technology is not voice semantic recognition but voiceprint, which is considerably difficult to commercialize as a product. They have launched the first product Otter.ai and cooperate with Zoom (ZM:NASDAQ) to provide services for end-users.

For some technologies that cannot scale immediately, their development will lag behind those that can be adopted widely in real practice. Therefore, these technologies cannot be treated as ‘not successful’– but as ‘slowly-developed’ tech.

We also look at AI for new drug development and AI for screening, which is promising but may take longer to mature. New drug R&D’s clinical trials are the most time-consuming process in the pipeline, but I think AI for pharma will be successful in the next few years. AI for logistics is another successful application in China as its logistics system is leading in the world.

Terry: From my observation, the success of leveraging AI is based on two elements: willingness and urgency. Those industries with greater budgets are also relying on new technology to stay competitive are more incentive to adopt AI. Based on such logic, I do see that the financial and Internet sectors are more successful in leveraging AI.

First of all, there is the demand to grow fast, which cannot be entirely handled by humans. Moreover, a considerable number accumulated on a daily basis fuels the advancement of modeling and algorithms. Notably, many tasks in the financial and Internet sectors have patterns to follow. Those patterns are repetitive – having a high frequency to show up and having obvious workflow. I think AI is a sound tech for such data and AI is doing well in the financial and the Internet sectors.

Regarding the second part of the question, if we follow the same logic and invert it, some industries with unique tasks, limited data accumulation and no standard workflow will find it difficult to adopt AI. Some traditional manufacturing and energy sectors are probably among those areas that lag in adopting AI. These sectors may not have suitable scenarios to apply AI solutions.

What is going to change in the next few years? Undoubtedly, AI applications will become deeper and broader. We have heard that the smart manufacturing sector is deploying computational vision (i.e. to screen defected products). Though manufacturing is traditional, it can be smarter with AI’s assistance, which is a good sign for traditional industries.

Meanwhile, many other AI solutions (like office administration) can pass beyond the industry and concentrates on horizontal solutions. Those are the applications that I believe can cross different verticals.


The full record of WIM Salon X Online | AI in China: 2020 and Beyond