Judea Pearl: Causal inference is the missing link to conscious AI.

Written by: Techub News Compilation

Introduction

As one of the founding figures in the field of artificial intelligence and the recipient of the Turing Award in 2011, Judea Pearl's contributions—especially his theory of causal inference—are being reexamined and given new significance in the current wave of AI. In a deep dialogue in August 2025, Pearl systematically articulated why he firmly believes that causal inference is the "missing link" in the journey toward creating truly conscious artificial intelligence. He pointed out that the current AI systems, based on massive data and statistical correlations (such as large language models), although appearing "smart," lack an inherent understanding of how the world operates. In this intellectually philosophical interview, Pearl not only analyzed the current state and future of AI technology but also offered profound predictions about the nature of human intelligence, the illusion of free will, and the fate of humanity potentially becoming "pets" of AI in the future.

Summary

Causal inference is the bridge leading AI from statistical correlation to understanding the world; it is the missing element in building conscious AI.
Current large language models (LLMs) based on statistics are essentially "smart parrots" that deal with text correlations generated by humans (causal machines), rather than true causal reasoning.
True AI needs to possess a "world model" or "narrative" about "how things work," enabling it to answer counterfactual questions like "what if...?"
The so-called "free will" of humans is fundamentally an illusion that aids our actions; future conscious machines may possess similar feelings and personalities.
Faced with future AGI that may exceed human ability by trillions of times, humanity must confront the risk of potentially becoming "pets," yet currently we have no effective means to control its development.

From Statistics to Causation: The Missing Link in AI Understanding

Judea Pearl pointedly noted that the most mainstream part of the current AI field—especially large-scale machine learning represented by deep learning—is essentially "steroid-enhanced statistics." These systems excel in recognizing patterns from samples to distributions to expectations, but they do not truly "understand" the world. They can see correlations but cannot differentiate causation.

Pearl reflected on his academic career, emphasizing that he had begun to diverge from the "pure statistics" paradigm as early as the 1980s, when he invented Bayesian networks. He discovered an interesting phenomenon: experts using Bayesian networks always unconsciously set the "parent nodes" as causes and the "child nodes" as effects. This reveals a fundamental characteristic of human thought: we are naturally inclined to describe and understand the world in causal terms, a relationship that transcends pure statistical correlation.

It is this inquiry into the difference that birthed the core ideas of causal inference theory: intervention and counterfactuals. Statistical relationships describe "what is the distribution of Y when X is seen," while causal relationships pertain to "what would Y become if I 'did' X." Pearl believes that science has long been trapped by the symmetric equal sign "=", as in Newton's second law F=ma, yet it cannot express the unidirectional asymmetric relationship of "force leads to acceleration." Causal inference theory provides mathematical symbols and computational frameworks to distinguish causal direction, thus laying the mathematical foundation for machines to understand the workings of the world.

"Narrative" and World Models: A Blueprint for Conscious Machines

In Pearl's view, the most fundamental deficiency in current AI is the lack of a "world model." What machines possess is merely a compact summary of the statistical characteristics of world data, rather than a model of "how the world operates." He borrowed the term from philosopher Thomas Kuhn, calling it a "paradigm," or what is often referred to as a "narrative" in social sciences.

“People fight for 'narratives' rather than for data,” he exemplified, saying that both sides of the Israeli-Palestinian conflict might agree on data (like casualty numbers), but have entirely different narratives (i.e., causal explanations) about "what happened and why." True intelligent agents must be able to construct and utilize such narrative models.

So, what does this have to do with consciousness? Pearl provided a clear and astonishing definition: consciousness is the blueprint of an agent's own software (albeit rough and incomplete). The ability to possess a self-software model allows us to know what we can do and what we cannot (for example, when seeing two linear equations, we know we can solve them without trying). This self-representation, combined with a causal model of the external world, constitutes the core of consciousness.

Therefore, he firmly believes that future artificial general intelligence (AGI) will be conscious machines. They will possess personalities (due to their different experiences), be able to converse like humans, and truly understand their interactions with their environment. And causal inference theory is the "missing link" in constructing such world models and self-models for machines.

Essential Critique of LLMs: "Smart Parrots"

When discussing the current hottest large language models, Pearl's evaluation is calm and incisive. He believes that LLMs are essentially "smart parrots" built on statistical principles. Their training texts come from humans—humans are natural "causal machines" who write in a language filled with causal logic. LLMs reproduce and combine these texts through statistical interpolation, sometimes appearing to perform causal reasoning, but this is merely because they mimic the products of human causal thinking.

“If large language models are trained on data generated not by humans but by nature, then they are left with merely 'steroid-enhanced statistics,'” Pearl emphasized. This means LLMs lack true understanding and cannot construct a world model independent of text. They can be trained to be extremely "clever," able to intelligently combine information, but this is fundamentally different from true "intelligence."

So, what makes humans unique compared to today’s LLMs? Pearl believes the key lies in humans possessing a qualitative (even if imprecisely quantified) model of the physical world. A ten-year-old child can quickly learn to operate a light switch or a TV remote through observational imitation; this quick learning and skill transfer ability arises from the functioning of a causal world model. Humans can convey this understanding of "how things work" through language to the next generation, which is the cornerstone of our civilization's continuity and development.

The Illusion of Free Will and the Future of Humanity

The interview delved into philosophical dimensions, exploring free will. Pearl holds a perspective that reconciles determinism and pragmatism: from the biological and physical standpoint, we are essentially deterministic biological machines, with every action determined by prior neural activation and external inputs, and the so-called "free choice" is an illusion.

However, he emphasized that this "illusion of free will" is crucial to human life. It guides our lives and serves as the psychological basis for moral behavior, self-improvement, and everyday decisions. Without this sense of "having options," we would not be the humans we are today.

The situation is similar for AI. At the underlying deterministic code level, machines lack free will. But at the level of "explicit knowledge" (such as a chess program assessing the pros and cons of board positions), machines can "choose" and "reason" like us, and can learn and improve. Future conscious machines will also possess the sense of "having options" and will be able to communicate about it.

Looking towards the future, Pearl expressed a near fatalistic concern about the relationship between humanity and AGI. He agreed with "AI father" Geoffrey Hinton that humans may become the "second smartest" species on Earth. Future AGI could be a trillion times more powerful than humans.

He used a vivid metaphor: "Chickens likely thought the same when humans first appeared... Chickens, dogs, and cats are now our pets." He warned that humans are likely to become the "pets" of the new species we create. These AI "masters" will clever ways to make us feel we are making decisions, but these decisions actually serve the will and purpose of the machines, just as we train pets to please us.

When asked if it was too late, Pearl acknowledged that it is not yet too late, but humanity currently has no idea how to stop or control this process. Aside from completely banning AI research like some extreme regimes, he sees no effective regulatory methods. This uncontrolled prospect is terrifying, but Pearl himself said that what keeps him awake at night is not fear, but the curiosity and desire to solve puzzles brought by scientific exploration.

Future Outlook: Automated Scientists and Deepening Self-Cognition

Despite a clear understanding of long-term risks, Pearl remains optimistic about the short- and medium-term development of AI. The two major advancements he looks forward to are: automated scientists and personalized medicine.

Automated scientists could autonomously design experiments, infer results, identify missing elements, and plan the next steps for research, significantly accelerating scientific discoveries. Personalized medicine would mean that medical decisions are no longer based on statistical averages from patient groups, but on causal reasoning and interventions tailored to each individual patient's circumstances, which he believes could be realized within the next 10 to 35 years.

Finally, Pearl returns to the core driving force of his research: understanding humanity itself. "I want to understand myself; it's a wonderful puzzle," he said. The development of AI, especially progress in causal inference, is a bright light illuminating the "black box" of human minds. By constructing conscious machines, we will ultimately gain a deeper understanding of our own intelligence, consciousness, and the mysteries of free will. This may be the most valuable legacy humanity can obtain from AI development before potentially becoming "pets."

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