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) reporter (hereinafter referred to as NBD) conducted an exclusive interview with Tan Tieniu, 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 Jusin Sun Award, the highest award in the field of pattern recognition internationally—awarded for the first time to scholars outside North America and Europe since the award’s establishment in 1988.

This year marks Tan Tieniu’s 40th year working in artificial intelligence. From early image recognition to biological feature recognition and video analysis, he has continuously pioneered new research directions. He is among the earliest scholars in China to work on iris recognition and gait recognition, with his scientific achievements 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,” “unexpected.” He recalled, “About ten years ago, we still regarded natural language interaction between humans and robots as a major goal. Now, this problem has basically been solved.”

But 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 Tieniu emphasized that developing AI should be “rational and pragmatic, not follow the trend blindly, adapt to local conditions, and focus on implementation,” ensuring 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 spring festival Gala stage featured robot performances again, sparking nationwide discussion. What signals do you think this release conveys? 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 went from unsteady walking to somersaults within a year, with rapid progress. This fully reflects our achievements in自主创新 (independent innovation), proving that Chinese people can also lead global technological innovation trends. So, we should maintain confidence in自主创新, and in this wave of humanoid robot enthusiasm, at least in movement and control capabilities, we are already among the world’s front runners.

Image source: video screenshot

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

Robots and intelligence are two highly related but conceptually different notions. 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 popular humanoid robots 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, a true demonstration of AI.

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

There is a historical lesson worth learning. Japan started early in humanoid robot research, launching the globally renowned “ASIMO” in 2000. But 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

Massively entering households will take at least 5 more years

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

Tan Tieniu: There are many, such as manufacturing, inspection—like inspecting roads, high-speed rail, high-voltage lines. But inspection tasks require high standards, demanding robots to have “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 being implemented in inspection, but challenges remain in open scenarios.

NBD: What key obstacles remain before robots truly enter homes and factories? When do you think highly intelligent robots will reach thousands of households?

Tan Tieniu: Robots are already in many homes, like common vacuum cleaners with certain intelligence. But for humanoid robots to truly enter homes, they need to help with more chores and achieve seamless human-machine collaboration. There are many hurdles. In my view, at least five more years are needed.

The reason is that robots must have strong scene perception. They need to 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 must judge human behavior and intentions, which is very difficult. If they don’t understand what others want to do, collaboration is impossible, and misoperations could pose risks.

AI-generated

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

NBD: The industry is optimistic, believing that in 3–5 years, intelligent robots will enter households. Do you think this optimism is overly optimistic or influenced by hype?

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

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

Second, investment bubble. OpenAI has burned through huge sums without turning a profit.

Third, valuation bubble. Despite not being profitable, OpenAI’s valuation has soared to hundreds of billions of dollars, which is obviously inflated. Some AI companies, even with only decent products, are valued at tens of billions, which is clearly overhyped. Media hype and self-media amplification have also exaggerated 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, rationality is especially needed.

3

Achieving Artificial General Intelligence (AGI) remains a long way off

NBD: You once said “Elon Musk and others are overly optimistic,” and believe AGI is still distant. But 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 AGI. My definition: AI that can match and surpass human intelligence. It must at least be comparable to humans, capable of doing everything humans can do. If defined this way, I believe 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—especially grasping implied meanings and reading between the lines. Current AI sometimes lacks basic common sense because it is trained on big data and does not truly understand causal relationships and physical laws of the material world.

“Intelligence” currently lacks a unified definition, and the mechanisms of human intelligence and wisdom are not fully understood. Surpassing something that is not fully understood is logically problematic. It’s only possible on the surface, but superficial indicators cannot exhaust all aspects or pass comprehensive tests. There is also a misconception that AI already has consciousness and emotions; in reality, it only mimics feelings and consciousness—imitation is not equivalent to possession or mastery.

I always have two doubts about AGI:

First, is AGI really needed in practical applications? Simply put, “general” means capable of doing everything. In my view, the answer is no, because specialization is more effective. We talk about cultivating versatile talents, but that doesn’t mean one person can do everything well—there’s no such thing as a true “generalist.”

Therefore, why not develop a group of highly specialized AI agents, each with a clear division of labor, working collaboratively? Even in household scenarios, cooking, cleaning, caring for the elderly—these could be handled by different specialized AI, which would be multi-purpose but not truly general.

AI-generated

Second, can AGI be achieved? Since AGI must surpass human intelligence, and human intelligence mechanisms are not fully understood, how to surpass them? So, my view is: it remains a long way off or perhaps unattainable.

4

Embodied Intelligence is the inevitable path to approaching human intelligence

NBD: There is a view that embodied intelligence is a necessary stage in realizing AGI. What is 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, currently, the term “embodied intelligence” is also 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 with the environment is only for executing predefined tasks, that cannot be called embodied intelligence.

There are misunderstandings that having a physical entity and some intelligence is enough to be called embodied intelligence. 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 a hardware entity; an intelligent robot is a robot with AI capabilities. For example, a typical industrial robotic arm is pre-programmed and lacks intelligence; an intelligent robotic arm that can autonomously change its path around obstacles and continue its task is different.

What distinguishes embodied intelligence robots from intelligent robots? First, if a robot is physical, it is inherently embodied. An embodied intelligent robot must become smarter through interaction with the environment, learning continuously and acquiring abilities not pre-programmed. If its capabilities are fixed and loaded in advance, it’s just an intelligent robot, not embodied intelligence.

Why say embodied intelligence is 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. Only after experiencing wind and rain, seeing the world, can one grow skills. Isn’t this the core of embodied intelligence? The character “智” (wisdom) combines “知” (know) and “日” (day), 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 is only interaction but the intelligence level remains fixed, it can only be called 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, lacks tactile sensing and feedback, it cannot be called 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 intelligence 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 the current AI boom. They are based on deep neural networks, simulating the layered information processing mechanism of the human brain, learning from coarse to fine, from broad to precise. Roughly, a large model is a huge neural network with massive parameters trained on vast data. It is just one way to realize AI, not the whole picture. Mimicking human intelligence does not necessarily require mimicking the human neural network; that is the most direct approach.

I proposed last April 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 rising cost of graphics cards is the most direct experience for ordinary people stacking computing power. Image source: Meijing Media Library

DeepSeek’s success is due to not fully relying on stacking compute and data, but through algorithm innovation, achieving comparable or even better results with fewer chips and data.

Large models cannot keep growing indefinitely; there is a limit to scale benefits. Therefore, alternative approaches are necessary. Embodied intelligence is one such path, which does not fully depend on existing internet data but dynamically acquires data through interaction with the environment—like 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: In the next 3–5 years, what disruptive breakthroughs in AI and embodied intelligence do you think are most worth paying attention to?

Tan Tieniu: I believe several directions are worth watching.

First, breakthroughs in underlying structures and new machine learning paradigms. The data-driven approach alone is unsustainable; new models combining data and rules are needed. Use rules for deterministic parts, data for uncertain parts. Also, explore models that integrate data and knowledge, driving both with data and knowledge. These are the key technological breakthroughs to watch in the next 3–5 years.

Second, breakthroughs in sensing technology, especially high-sensitivity, multi-functional 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. Leveraging insights from brain science and cognitive science could lead to new intelligent paradigms beyond Transformers, opening pathways that do not rely on large models.

Additionally, multi-agent systems and human-machine collaboration are important areas. Brain-computer interfaces that facilitate human-machine interaction may also see breakthroughs.

7

To avoid “AI divide” caused by differences 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: AI replacing some jobs is inevitable; it’s a normal part of technological progress. But overall, it’s not about destroying all human jobs.

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, optimizing the structure.

However, the new jobs 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.

Human-machine collaborative inspection. 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 nationwide compulsory AI literacy courses for all students and train teachers, because AI will淘汰 (eliminate) those who do not learn AI; third, adapting to student development—teaching according to aptitude.

This is the fundamental logic of our educational reform, not just for hype.

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

Tan Tieniu: I believe there are a few 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 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

| Daily Economic News nbdnews Original Article|

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