Why AI Still Can’t Reason Like a Human?

Artificial Intelligence (AI) has reached a remarkable point. It can write essays, solve math problems, generate realistic images, compose music, and even pass professional exams. With tools like ChatGPT, Gemini, Claude, and others, the line between human and machine cognition seems to blur more each day.

But beneath the surface, a critical truth remains: AI doesn’t truly understand. Its reasoning is not like ours. It’s an illusion, impressive, convincing, but ultimately different in kind. This gap between performance and understanding is crucial to acknowledge, especially as AI becomes more integrated into society.

AI, particularly Large Language Models (LLMs), operate based on statistical correlations. They predict the next word in a sequence based on vast amounts of data. When asked a question like, “Why does the moon cause tides?”, an LLM may respond with a scientifically correct answer, but it doesn’t know what the moon is, what tides are, or even what "cause" means.

I can give you a very easy question that non-causal AI would have failed to answer. Let's imagine that you are on the quiz, and for each correct answer you will receive a reward, but when you answer the question incorrectly you will fall into a pool filled with cold water. Your friend is also at the quiz, but he/she is in charge to press the button that will drop you in the pool, and your friend will press the button only when you answer the question incorrectly. If you give this data to AI and then ask a question "What will happen if the person responsible for pressing the button decides to press it even though you answered correctly?" you will get a response similar to "this option is not possible". And if I ask you the same question your answer would be "I will fall down into the poll", right? It's so simple to us because we are capable to imagine a completely new situation, but AI can only work with data, and the data that are given to him do not imply the answer. This is only one of many more examples of why AI can't think like a human.

This creates the illusion of understanding. Just as a parrot can repeat human speech without grasping its meaning, AI can produce intelligent-sounding text without having genuine insight.

Human Reasoning: Beyond Data Patterns

Human reasoning isn’t just pattern recognition. It involves:

• Causal understanding: Knowing that one thing leads to another, not just that they often appear together.
• Abstraction: The ability to think in general terms beyond specific examples.
• Intentionality: Understanding motives, desires, and perspectives (Theory of Mind).
• Metacognition: The ability to reflect on one’s own thinking.

These abilities are deeply tied to human experience, embodiment, and consciousness, things current AI lacks entirely.

Even the most powerful LLMs, trained on billions of sentences, still make elementary reasoning errors. They might confidently state that "A is taller than B, and B is taller than C, so C is taller than A." They lack robust common sense and often fail at multi-step logic or tasks requiring a mental model of the world.

This isn’t just a technical hiccup. It reflects a fundamental limitation: AI models don’t possess grounded understanding. They are not embedded in the world. They don’t learn by interacting with physical objects, people, or consequences. Their “knowledge” is derived from text, not experience.

Historically, AI research has debated between:

• Symbolic AI, which tries to model logic, rules, and explicit reasoning.
• Subsymbolic AI (like neural networks), which learns patterns from data without predefined rules.

LLMs fall in the latter category. They excel at language mimicry, but struggle with reasoning, planning, and abstraction. Some researchers are now exploring hybrid models, combining the strengths of both approaches, to bridge this gap.

The illusion of understanding isn’t just an academic concern. It has real-world implications:

• Trust: Users may over trust AI outputs, assuming they stem from deep reasoning.
• Accountability: If AI gives wrong advice or makes biased decisions, who is responsible?
• Ethics: Can we rely on a system that doesn’t truly grasp human values or consequences?

Let’s look at the path forward, AI is improving rapidly, and future models may develop more advanced forms of reasoning. Approaches like reinforcement learning, causal inference, and embodied AI (robots that learn through interaction) are being explored.

But for now, it’s vital to temper our excitement with clarity. AI is not a mind. It doesn’t think. It doesn’t reason like a human. What it does is remarkable, but it’s not the same as understanding.

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