Can AI Ever Truly Understand the World Like We Do?

Exploring the difference between how humans and large language models (LLMs) think is like comparing two different ways of seeing the world. Humans build a comprehensive "world model" over time—a deep, evolving understanding that includes everything we see, hear, and experience. It's like constructing a mental map, shaped by learning from life, interactions, and countless sensory experiences. This world model helps us make sense of new situations, interpret context, and make predictions based on our past knowledge.

Humans also learn extensively through trial and error. We interact with the world, make mistakes, and adjust our behavior based on the outcomes. This continuous cycle of observation, action, feedback, and adaptation is crucial to how we build a rich and nuanced understanding of our environment. Our sensory experiences—sight, sound, touch, and more—combined with the consequences of our actions give us a grounded perspective that helps us navigate complex situations and relationships.

LLMs, on the other hand, don't have a world model in the same way. Instead, they rely on statistical patterns. They have ingested huge amounts of text data, and from that, they've learned to predict what word should come next based on the words before it. The strength of an LLM is not in truly "understanding" the world, but in recognizing and replicating the relationships between words and phrases as they've appeared before. It's akin to looking at millions of puzzle pieces without ever seeing the full picture—but still managing to put many pieces together convincingly.

Imagine a conversation: a human's response draws on memory, imagination, and an understanding of the emotional subtext. They might think about how the other person feels, what their experiences have been, or what hidden meanings lie beneath the words. An LLM, however, constructs a response by statistically piecing together the most probable sequence of words. There's no deeper understanding of the emotion or intent behind the question—just a powerful capacity for putting together likely responses.

The human world model includes not only the physical world but also the ability to understand abstract concepts, human motivations, ethics, and social nuances. LLMs can approximate parts of these by mimicking language, but they lack the grounding in experience. They don't have intentions, beliefs, or the ability to make sense of the world beyond language. They can simulate, but they can't genuinely relate.

This difference is what makes human intelligence so adaptable. We learn from every mistake, we carry lessons from one context into another, and we understand consequences in a way that LLMs can't. While LLMs can provide impressive answers, generate content, or synthesize information, they're ultimately missing that deeper, integrated understanding—the world model that allows humans to truly see beyond the surface and adapt to the complexities of real life.

This article was originally published on LinkedIn.