Interview with Tan Tieniu, Standing Committee Member of the Chinese People's Political Consultative Conference and Party Secretary of Nanjing University: AI Development Needs to Remove the Bubble

Daily Economic News Reporter | Zhang Rui Daily Economic News Editor | Wen Duo

On March 5th, the government work report was released, mentioning “artificial intelligence” multiple times, and embodied intelligence was once again included in the report.

Focusing on hot topics in artificial intelligence and embodied intelligence, Daily Economic News (NBD) reporters interviewed Tan Tieniu, a Standing Committee member of the 14th National Committee of the Chinese People’s Political Consultative Conference, Academician of the Chinese Academy of Sciences, and Secretary of Nanjing University, during the National Two Sessions.

Tan Tieniu previously served as Vice President of the Chinese Academy of Sciences; in August 2022, he received the Fu Jing Sun Award, the highest award in the field of pattern recognition internationally. This was the first time since the award’s establishment in 1988 that it was awarded to scholars outside North America and Europe.

This year marks Tan Tieniu’s 40th year working in artificial intelligence. From early image recognition to later biological feature recognition and video analysis, Tan has continuously pioneered new research directions. He is among the earliest scholars in China to work on iris recognition and gait recognition, with his research results widely applied in coal mines, criminal investigations, and other key fields.

In the interview, Tan Tieniu lamented that the progress of AI technology in recent years has indeed been “beyond imagination” and “unexpected.” He recalled, “About ten years ago, we still regarded natural language interaction between humans and robots as an important goal. Now, this problem has basically been solved.”

However, he also warned, “This does not mean that AI is万能 (all-powerful) now; it still has many ‘cannot’—although, without AI, many things would be impossible.” Tan emphasized that the development of AI should be “rational and pragmatic, not follow the trend blindly, and adapt to local conditions for implementation.” It is essential to ensure AI is used for good, truly promotes new productive forces, and supports Chinese-style modernization.

1

It’s still too early to talk about industry maturity

NBD: This year’s Spring Festival Gala featured robot performances that once again became a nationwide topic. What signals do you think this releases? Is this concentrated exposure a sign of industry maturity?

Tan Tieniu: Humanoid robots are indeed a hot area of technological revolution and industrial change, widely appreciated by the public. But we need to see the underlying trends, not just enjoy the spectacle.

First, it’s worth noting that from last year’s “YangBot” to this year’s “WuBot” [Bot is short for robot], the level demonstrated by Yushu humanoid robots is impressive. They progressed rapidly within a year—from unsteady walking to somersaults. This fully reflects our independent innovation achievements and proves that Chinese people can also lead global technological innovation trends. So, we should maintain confidence in自主创新 (independent innovation). In this humanoid robot craze, at least in terms of movement and control capabilities, we are already at the forefront worldwide.

Image source: video screenshot

But we must also view this objectively. Humanoid robots are not equivalent to artificial intelligence. Seeing cool actions like somersaults, one might think they possess high intelligence. It’s important to clarify a basic concept: humanoid robots are not equal to AI.

Robots and intelligence are two highly related but conceptually different things. Robots do not necessarily have intelligence; they are more like carriers of AI capabilities. Only robots with certain intelligence can be called intelligent robots, and the same applies to humanoid robots.

Currently, the hot humanoid robots online mainly demonstrate advances in control and movement. Like drone formations, they are pre-programmed and pre-trained in known processes, actions, and scenarios, which does not fully represent progress in AI. If during a performance, a prop is moved unexpectedly, the robot might fail to respond. If it can autonomously find the prop, that would be a higher level—true AI.

Therefore, it’s still premature to talk about industry maturity. I believe that if humanoid robots only dance and do somersaults, they will ultimately be fleeting. It’s crucial to find killer applications. Many orders are placed after the Spring Festival Gala, which is not surprising, but the novelty and curiosity won’t last. The key is whether it’s a real need and can solve actual problems. Without killer applications, they will eventually be eliminated by history.

A lesson from history is worth noting. Japan started early in humanoid robot research, launching the famous “ASIMO” robot in 2000. After 22 years, due to high costs and limited practicality, it failed to find killer applications and eventually exited the stage.

Of course, Yushu robots have far surpassed ASIMO in movement control, but their intelligence level remains limited.

2

Smart humanoid robots

Will truly large-scale household entry take more than 5 years?

NBD: What fields do you think killer applications might appear in?

Tan Tieniu: Many fields, such as manufacturing, inspection—like inspecting roads, high-speed rail, high-voltage lines. But inspection tasks require high standards, demanding robots with “sharp eyes,” meaning strong visual capabilities and fast processing speeds. This involves not only control and movement but also environmental perception and understanding. Some applications are already in place, but open scenarios still pose challenges.

NBD: What do you think are the key obstacles preventing robots from truly entering homes and factories? When will highly intelligent robots enter thousands of households?

Tan Tieniu: Robots have already entered many homes, like common vacuum robots with certain intelligence. But for humanoid robots to truly enter homes, helping with more chores and achieving seamless human-machine collaboration, many hurdles remain. I believe at least five more years are needed.

The reason is that robots need strong scene perception abilities. They must understand their surroundings, know their own location, and interpret human intentions and actions. They shouldn’t block pathways or start pouring hot water when someone is about to take a cup. They need to judge human behavior and intentions, which is very difficult. If they don’t understand what the other person wants to do, collaboration is impossible, and misoperations could pose risks.

Another key shortcoming is dexterous manipulation, especially the “hands.” Current tactile sensing capabilities are far from sufficient to accurately perceive smoothness, material, temperature, humidity, etc. When will humanoid robots be able to play table tennis with humans and win? That’s when I’d be truly impressed, but we are still far from that.

NBD: Industry optimism suggests that in 3–5 years, intelligent robots will enter households. Do you think this optimism is overly optimistic or driven by hype?

Tan Tieniu: Some believe there’s a bubble in the industry, and I agree. I see three bubbles:

First, expectation bubble. People have high hopes for AI and humanoid robots. The recent progress has indeed been beyond imagination, but that doesn’t mean AI is万能 (all-powerful). Rapid development leads to overoptimism that general AI (AGI) will be achieved within a few years, which is overly optimistic.

Second, investment bubble. OpenAI has burned huge amounts of capital and still isn’t profitable.

Third, valuation bubble. Despite not being profitable, OpenAI’s valuation has reached hundreds of billions of dollars—obviously inflated. Some AI companies, with only decent products, are valued at tens of billions, which is clearly overhyped. Media hype and self-media amplification also inflate the bubble.

Nobel laureate Herbert A. Simon predicted during the first AI wave in 1965 that machines would do all human work within 20 years. That prediction has not come true. It shows that in the hype cycle, rationality is needed.

3

Achieving general AI remains a long-term challenge

NBD: You once said “Elon Musk and others are overly optimistic” and believe AGI is still far off. Yet, the industry’s pursuit of AGI remains intense. Between “rational pragmatism” and “technological idealism,” how should China’s AI development pace be managed?

Tan Tieniu: The key is how to define general AI. My definition: AI that can match and surpass human intelligence (wisdom). It should at least be comparable to humans, capable of doing everything humans can do. If we define it this way, I think it’s difficult to realize in the foreseeable future.

The reason is that humans have insights, common sense, can infer from one thing to another, and understand context—like catching the subtle meaning behind words. Currently, AI sometimes lacks even basic common sense because it is trained on big data and doesn’t truly grasp causal relationships and physical laws of the material world.

“Intelligence” currently lacks a unified definition, and the mechanisms behind human intelligence and wisdom are not fully understood. Surpassing something that is not fully understood is logically problematic. Superficially, it might seem possible, but appearances cannot cover all aspects, and complete testing is impossible. There is also a misconception that AI already has consciousness and emotions; in reality, it only mimics feelings and awareness. Miming does not mean possessing or mastering.

I have two questions about general AI:

First, is there a real need for general AI in practical applications? Simply put, “general” means capable of doing everything. In my view, the answer is no, because specialization is more effective. We cultivate versatile talents, but that doesn’t mean one person can do everything well—there’s no such thing as a true “polymath.”

Therefore, why not develop a group of deep specialized AI agents, each with their own roles, working together? Even in household scenarios, cooking, cleaning, caring for the elderly—if done by the same AI, it’s just multi-purpose, not truly general.

Second, can general AI be achieved? Since general AI must surpass human intelligence, and human intelligence mechanisms are not fully understood, how can it surpass? My conclusion is that it remains a long-term or even distant goal.

4

Embodied intelligence is the inevitable path to approaching human intelligence

NBD: There’s a view that embodied intelligence is a necessary stage in achieving general AI. What’s your opinion?

Tan Tieniu: Of course, if the goal is to infinitely approach human intelligence and wisdom, embodied intelligence is a path—or rather, an essential route. However, the term “embodied intelligence” is sometimes misused or overly labeled.

Embodied intelligence has two core elements: first, having a physical body—an observable, tangible physical entity; second, continuous interaction with the environment, becoming smarter through “learning by doing.” If interaction only involves executing predefined tasks, that cannot be called embodied intelligence.

There are misunderstandings that having a physical entity and some intelligence is enough. That’s incorrect. We need to distinguish between robots and AI, robots and intelligent robots, intelligent robots and embodied intelligent robots.

Simply put, a robot is hardware; an intelligent robot is a robot with added intelligence capabilities. For example, a typical industrial robotic arm is pre-programmed and lacks intelligence; an intelligent mechanical arm, when encountering obstacles, can autonomously change its path and continue working.

What’s the difference between embodied intelligent robots and intelligent robots? First, if a robot is physical, it is embodied. Embodied intelligent robots must become smarter through interaction with the environment, learning continuously and acquiring abilities not pre-programmed. If its capabilities are fixed and preloaded, it’s just an intelligent robot, not embodied.

Why is embodied intelligence considered the path to approaching human intelligence? Because human intelligence evolved this way. To approach human-level intelligence, the most direct and effective way is to learn and evolve like humans, which may enable surpassing.

My understanding of embodied intelligence has evolved over recent years. Initially, I was skeptical because human and animal natural intelligence develops gradually through experience and adversity. It’s about weathering storms and seeing the world to grow skills. Isn’t that the core of embodied intelligence? The character “智” (wisdom) combines “知” (know) and “日” (sun), symbolizing daily personal experience. Wisdom, intelligence, and smartness all mean gaining through experience and practice.

Therefore, the essence of embodied intelligence is to gain dynamic improvement through interaction with the external world. If there’s only interaction but the intelligence level remains fixed, it’s just an intelligent robot, not embodied intelligence. For example, a robotic hand picking up a cup involves interaction, but if it doesn’t learn to grip more tightly or doesn’t have tactile sensing and feedback, it’s not embodied intelligence.

5

Stacking computing power and data

Relying solely on this approach to develop AI is unsustainable

NBD: You often mention intelligence—can this be understood as the capability of large models? What role do large models play in embodied intelligence? Is there a risk of “over-reliance on large models” now?

Tan Tieniu: Several concepts need clarification here. Large models are not equivalent to AI; embodied intelligence is a development path, a method, and an essential route to approaching human intelligence.

Large models are the core technology of this AI boom. They are based on deep neural networks, simulating the layered information processing of the human brain, learning from coarse to fine, from broad to precise. Large models can be roughly understood as huge neural networks with massive parameters, trained on vast data. They are just one way to realize AI, not the whole picture. Mimicking human intelligence doesn’t necessarily require simulating the human neural network; that’s the most direct approach.

Last April, I proposed that relying entirely on stacking computing power and data to develop AI is unsustainable. There are three reasons: first, performance gains are diminishing; second, computing power is limited; third, data is nearly exhausted on the internet. All physical systems have limits, and new approaches are needed.

The recent surge in GPU prices reflects the public’s perception of increasing computing power. Image source: Meijing Media Library

DeepSeek’s success lies in not fully depending on stacking compute and data but in algorithm innovation—achieving comparable or better results with fewer chips and less data.

Large models cannot keep growing indefinitely; there’s a limit to scale benefits. Therefore, alternative paths are necessary. Embodied intelligence is one such path—it doesn’t rely solely on existing internet data but dynamically acquires data through environmental interaction, such as sensing material and smoothness when grasping a cup.

6

In the next 3–5 years,

Focus on breakthroughs in sensing technology and brain-computer interfaces

NBD: What are the most promising disruptive breakthroughs in AI and embodied intelligence in the next 3–5 years?

Tan Tieniu: I see several directions worth attention.

First, breakthroughs in underlying structures and new machine learning paradigms. Relying solely on data is unsustainable; new models combining data and rules are needed. Integrate data-driven and rule-based approaches—use rules for deterministic parts, data for uncertain parts. Also, explore models that combine data and knowledge, driven by both, to achieve technological breakthroughs in the next 3–5 years.

Second, breakthroughs in sensing technology, especially high-sensitivity, multifunctional sensors. This directly impacts the capabilities of end-effectors like dexterous hands, crucial for embodied intelligence.

Third, new machine learning methods that are low-cost and efficient, reducing dependence on massive compute and data. Insights from brain and cognitive sciences could lead to new intelligent models, possibly moving beyond Transformer architectures, opening new development paths without relying on large models.

Additionally, multi-agent systems and human-machine collaboration are important areas to watch. Brain-computer interfaces that facilitate better interaction and cooperation between humans and machines may also see breakthroughs.

7

To avoid “AI divide” caused by disparities in AI endowments

NBD: What are your thoughts on the societal anxiety that “AI will replace humans,” especially concerns that embodied intelligence might replace blue-collar jobs? We note that Nanjing University is promoting “1+X+Y” AI literacy education. Can this general education meet the talent needs of the AI era?

Tan Tieniu: It’s inevitable that AI will replace some jobs—that’s normal technological progress. But overall, it’s not about destroying all human employment.

The World Economic Forum’s “Future of Jobs Report 2025” predicts that between 2025 and 2030, about 92 million jobs worldwide will be replaced, but 170 million new jobs will be created. Historical experience shows that technological progress replaces some jobs in the short term but increases employment in the long run, leading to structural optimization.

However, the new jobs created may not be accessible to those displaced. If people do not pursue lifelong learning and re-skilling, unemployment risks increase. Conversely, proactive planning, on-the-job training, and innovative curricula can help workers adapt to new demands.

Image source: video screenshot

Therefore, Nanjing University’s talent training emphasizes “three adaptations”: first, adapting to national needs—adjusting majors accordingly; second, adapting to the era’s characteristics—most notably, intelligence. In 2024, we will launch the country’s first compulsory AI literacy course for all students and train teachers, because AI will淘汰 (eliminate) those who don’t learn AI; third, adapting to student development—personalized education.

This is the fundamental logic of our educational reform, not just a gimmick.

NBD: Do you have other suggestions or reflections on AI development?

Tan Tieniu: I believe there are several points to emphasize—

First, pay special attention to regional and industry disparities in AI empowerment to avoid widening the “AI divide,” which could exacerbate regional and sectoral imbalances and intensify social conflicts.

Second, expanding domestic demand is the primary driver of economic growth. We should vigorously promote AI-enabled consumption, creating new consumption scenarios, such as home services, elderly care (“aging”), and education (“children”). For example, if companion robots truly become empathetic, safe, and affordable, they could be killer applications. But many issues remain, including standards, ethics, and safety, which need to be addressed gradually during development.

Reporter | Zhang Rui

Editor | Wen Duo

Visual | Chen Guanyu

Layout | Wen Duo

Overall Coordination | Yi Qijiang

Original article | Daily Economic News nbdnews

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