Can AI Really Understand? Exploring ChatGPT, Meaning, and the Symbol Grounding Problem

Ever wonder if ChatGPT truly understands the words it generates or if it’s just really good at mimicking human responses?

You’re not alone—this question has intrigued experts in artificial intelligence, including cognitive scientist Stevan Harnad, who explored this topic in his paper “Language Writ Large: LLMs, ChatGPT, Grounding, Meaning, and Understanding.”

https://arxiv.org/pdf/2402.02243?

Let’s break it down in a way that is easier to follow.

ChatGPT: A Master Mimic?

ChatGPT can generate incredibly human-like responses to a wide range of prompts. It can answer questions, tell stories, and even engage in deep philosophical discussions. But does that mean it truly understands what it’s saying? Harnad argues that it doesn’t—at least, not in the way humans understand things.

ChatGPT works by processing enormous amounts of data (like all the text it’s trained on) and then predicting the most likely next word in a sentence. This process is based on patterns and probabilities, not understanding. So, while it might seem like ChatGPT is “thinking” or “understanding,” what it’s really doing is more akin to completing a puzzle without knowing the bigger picture.

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The Turing Test and AI’s Limitations

One of the main tests for AI is something called the Turing Test. The idea is simple: if a machine can converse in such a way that a human can’t tell if it’s talking to another human or an AI, then it’s passed the test. ChatGPT is excellent at passing this language-based test (called T2). However, Harnad explains that there’s more to true understanding than just using the right words in the proper order.

There’s also a test called T3, which adds a crucial dimension: the ability to interact with the physical world. Think of a robot that talks about a cat and can recognize, touch, and interact with an actual cat. This is where ChatGPT falls short—it lacks sensorimotor grounding, meaning it can’t connect the words it generates to real-world experiences.

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The Symbol Grounding Problem

This brings us to a big concept in AI: the symbol grounding problem. In simple terms, it’s the idea that for words to have true meaning, they must be connected to real-world things. For example, a child learns the word “cat” not just by hearing it, but by seeing and touching a cat, which helps the word take on real meaning. ChatGPT, on the other hand, has never “met” a cat. It knows what the word “cat” should look like in a sentence, but that’s where its knowledge ends.

Imagine learning a language by only using a dictionary. You might figure out how words relate to each other, but without experiencing what those words refer to, your understanding will always be incomplete. That’s the problem AI faces—it can describe and talk about things, but without experiencing them, its understanding remains superficial.

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Can AI “Ground” Meaning?

You might wonder: Can’t we just teach AI using a set of basic, grounded words that can define all the others? This is where the concept of Minimum Grounding Sets comes in. It’s like finding a small set of core words that help define all other words in the dictionary. While this might work for a human who has already experienced the real world, it doesn’t solve the problem for AI. Why? Because dictionaries assume you’ve lived a bit—you’ve seen a cat, felt the sun's warmth, or smelled fresh bread. AI lacks this sensorimotor experience, so even with a perfect dictionary, it can’t truly grasp the meaning behind the words.

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Mirror Neurons and Iconicity: How We Understand

?One of the fascinating ways humans learn is through imitation and observation, thanks to mirror neurons. These neurons fire when we perform an action and see someone else do it. This helps us understand what others are doing and why—a form of grounded learning beyond AI’s capabilities for now.

Take iconic words like “buzz” or “crash.” These words resemble the sounds they describe, giving us a more intuitive understanding. Most of our language, however, doesn’t work like this. The word “cat” doesn’t sound or look like a cat. However, humans use experience and social interaction to build meaning. For AI, which lacks this experience, even abstract words like “democracy” or “fairness” are a challenge.

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Does AI “Perceive” Categories Like We Do?

Another idea Harnad explores is categorical perception. Humans naturally group things into categories—like distinguishing between colors on a spectrum. This skill helps us navigate the world efficiently. For example, a doctor learns to tell healthy cells from cancerous ones through experience. Could AI develop a similar ability by processing vast amounts of data?

Harnad suggests that AI might develop its categorization based on the patterns it recognizes in language. It doesn’t need to physically experience the world to notice patterns in how words are used. In this sense, AI might be mimicking a form of human learning without the sensorimotor grounding that humans rely on.

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The Great Debate: Can AI Ever Truly Understand??

Harnad’s paper also dives into a debate with AI expert Yoshua Bengio. Bengio believes that AI, while different from humans, might have its own understanding. Harnad disagrees, insisting that true understanding requires grounding in the physical world—something AI just doesn’t have.

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Here’s the crux of the debate: If AI can mimic understanding so well, does it even matter if it doesn’t truly “get it” the way humans do? And if we ever create an AI that can understand, would its understanding be so different from ours that we might not even recognize it?

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What Does This Mean for the Future of AI?

As AI continues to develop, it’s important to remember its limitations. While models like ChatGPT are incredible tools, they operate in a world of words, not experiences. They can simulate understanding, but their lack of grounding in the real world means they’ll never fully grasp concepts like humans.?

Still, this doesn’t mean AI can’t be incredibly useful. It pushes us to think more deeply about what “understanding” truly means—and how we might define intelligence in a world where machines can talk like us but don’t think like us.

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Key Takeaway: ChatGPT is an extraordinary language model, but its intelligence is based on patterns, not real-world experiences. It can generate human-like responses but doesn’t “understand” as we do. The future of AI may bring new forms of intelligence, but for now, AI remains a masterful mimic rather than a true thinker.

What do you think? Can AI ever truly understand, or will it always just be an impressive mimic? ?