Fan Ling Visits CCTV Live Studio to Discuss AI Industry Development

On July 17, the first day of WAIC, Fan Ling, a professor at Tongji University, director of the Design Artificial Intelligence Laboratory, and founder of Tezign Technology, was invited to the CCTV News live studio to deeply analyze the development trends of the AI industry with the host.

The original text of Fan Ling's speech is as follows:

Q1:

This year's conference adopts a layout of three locations and four venues,

What are your feelings and expectations for attending the conference?

Fan Ling: I just came from the West Bank Exhibition Hall, which is a venue more suitable for the public to experience various aspects of life brought by artificial intelligence technology. This year is also very lively; it's not just about viewing exhibitions behind closed doors in one place, but there is a real opportunity to connect various aspects of artificial intelligence throughout the city through exhibitions. So I think this is a very significant highlight this year—the entire city has become a space to experience artificial intelligence, with viewpoints, exhibitions, and various life experience scenarios extending out. I think this reflects the very vigorous trend of our artificial intelligence development, especially in the past three to four years, transforming from a niche deep technology into a new social and ecological state empowered by technology.

Q2:

What kind of connection does AI have with our lives?

What does the change from being able to chat to being able to do mean?

Fan Ling: In the past three to four years, generative artificial intelligence technology has actually gone through three stages. The first stage, represented by GPT, can generate images, text, and videos, which we refer to as being able to chat. Then in the second stage, represented by DeepSeek, AI began to express how it conceives and thinks, which we call reasoning models or thinking models. In the past year, the most capable thing AI can do is to start writing code to operate computers, so everything that can be done on a computer can now be autonomously handled by AI. Therefore, the change from being able to chat to being able to do is essentially from AI being able to converse with you to actually helping you get work done.

This change is actually very beneficial for individuals. Previously, we only regarded AI as a tool for obtaining information, for example, if I want to explore WAIC today, AI would tell me which places are worth visiting. But now, if I think of it as helping you get work done, it will help you plan where to go this afternoon. In the past, an individual needed many different functions to assist in completing a task. But now, because AI can take on many roles, a person's capacity is greatly enhanced; AI has the opportunity to make our weaknesses stronger and make us more versatile.

For society, this morning the General Secretary mentioned that intelligence should also be inclusive. When AI has broader behavioral capabilities, those organizations, countries, and groups that previously did not have the resources to utilize these capabilities may now be able to significantly reduce the difficulty of resource allocation through AI, bringing more universal impacts to society.

Of course, this also prompts us to reflect: When AI can do many things, what is our role as humans? The development of AI represents a kind of intellectual equality, while human qualities, socialization, aesthetics, and taste become the capital that distinguishes us from machine intelligence. My laboratory is called the Design Artificial Intelligence Laboratory, which not only uses artificial intelligence for design but also aims to design a symbiotic relationship between humans and intelligence. I particularly like this year's WAIC theme—one called partnership and one called co-creation.

I want to discuss two topics that may not be mainstream concerns this year, but I believe will become important issues in the AI field in the next two to three years. One is that besides working, AI is also starting to understand humans—understanding a person's psychology and understanding those descriptions that cannot be expressed. So can AI better help us understand individual humanity? This is a research area we are exploring, and we have created something called subjective world models, which is actually trying to do this. The second is that AI continuously works in the background 24/7 without human intervention—this is called long-range intelligence, where AI works while humans provide judgment, but humans do not need to drive it. AI can automatically perceive changes; in smart factories, security, and many other scenarios, AI can continuously operate in the background 24/7, with humans providing some judgments.

Q3:

You are both a scholar and an entrepreneur,

What qualities do you think AI should have to enter thousands of households?

Fan Ling: Indeed, the era of artificial intelligence has given researchers like us the opportunity to become entrepreneurs. Many artificial intelligence companies are actually called researcher founders; the founders are not businessmen but researchers, which is a unique characteristic of starting a business in the era of artificial intelligence. However, there is a significant problem with researchers starting businesses, which is that they focus only on the technical attributes and lack an understanding of the user layer. Therefore, to enter thousands of households, artificial intelligence must become a technology that serves people, rather than just a technology that rolls parameters and rankings.

I believe good products should have these three attributes.

First, it must understand the user. There is a concept in artificial intelligence called context; all models must operate within a context that understands the scene and the people for the model to truly be useful to you.

Second, we need to be able to trust AI, knowing that it will not do harm to you or infringe on your interests. Establishing a mutual trust mechanism between humans and machines is very important. There is a technology that is being widely discussed now called harness engineering, which is actually addressing the issue of trust.

Third, technology should not be the protagonist; good technology should be invisible, supporting us in our lives, not in the foreground but in the background. Therefore, user experience is extremely important. So, AI technology that understands you, you can trust, and is ultimately omnipresent but not the protagonist can eventually reach thousands of households. Of course, the premise is that the underlying technology must still be very excellent, truly becoming our partner.

Q4:

As everyone shifts their focus to the implementation of the AI industry,

What challenges and opportunities do we currently face?

Fan Ling: If we first do not talk about artificial intelligence, let's look at the previous generation of disruptive technologies, such as from steam engines to electric motors. It took about 30 years for steam engine factories and companies to transform into electric motor native enterprises. So we indeed need to be more patient with the overall implementation of the industry.

We conducted a study ourselves, and large models are the starting point for all AI technology applications, but large models only play a part in whether the industry can be implemented. We have a ratio—not very scientific, just a qualitative expression in quantitative terms, called 1:2:7.

One is the capability of the model, which only accounts for 10% of the weight in a successful industrialization. Two is data, there are various kinds of data in the industry, and not only data collection, we also need to specifically collect new data for the model and explore new data opportunities. Seven is people and organizations, which is the change in people. There is a theory in the technology industry called "crossing the chasm"—a group of passionate people started using this technology early on and grew rapidly, but to become universal, they need to cross this chasm. I believe that AI technology, with conferences like WAIC and our country's relevant policies, should cross the chasm quickly, not needing 30 years like the last electrical era. However, it may still not happen overnight; it won't be that everyone has a Doubao or Qianwen app on their phones, and tomorrow our industry will become an AI-native industry.

This requires patience, the kind of patience represented by 1:2:7. The seven may involve organizational adjustments, improvements in people's capabilities, and the reshaping of processes. Our current stage needs to address those seven issues before we can achieve true industrial implementation.