AI vs Human Thinking: How Large Language Models Really Work

This article explores the fascinating differences between artificial intelligence models, specifically Large Language Models (LLMs), and the human brain. We'll look at how both learn, process information, remember, and even make mistakes. While AI can mimic human-like outputs, the underlying mechanisms are quite distinct, revealing unique strengths and weaknesses for both.

How AI and Human Thinking Compare

When we talk about AI and human thinking, it's easy to assume they work similarly, especially with how advanced AI has become. But a closer look shows some big differences. Let's break down how they stack up in a few key areas.

Learning: Different Paths to Knowledge

Humans and LLMs both learn, but they do it in very different ways. Our brains learn through something called neuroplasticity. This means our brains can change their connections based on what we experience. If you learn a new skill, your brain adjusts. We can learn from just a few examples, sometimes even a single one, and it sticks.

LLMs, on the other hand, learn through a process called backpropagation. This involves processing millions of text examples and adjusting internal settings to get closer to the right answer. It takes a huge amount of data and many passes. Think about it: you might learn a new word after hearing it once or twice, but an LLM might need to see it thousands of times before it can use it correctly. Also, once an AI model is trained, its settings are pretty fixed. Humans, though, are always learning and changing as new information comes in.

Information Processing: Concepts Versus Tokens

Our brains process information in a massively parallel way. Billions of neurons work at the same time, with different parts of the brain handling different jobs, like seeing or hearing. We don't just decode words one by one; we grasp chunks of meaning and connect them to what we already know. We work at the level of ideas.

LLMs work differently. They use sequences of discrete symbols called tokens. When an LLM gets input, it turns the text into a series of representations. These representations go through layers where the model figures out which tokens are important for predicting the next one. While humans process concepts, LLMs are more about pattern completion based on their training data.

Memory: Associative vs. Context Window

Humans have several memory systems:

• Sensory memory: For information from our senses, lasting only a few seconds.

• Working memory: A temporary storage space, short-term and limited.

• Long-term memory: Much larger capacity, holding information for years.

Human memory is also associative. Memories are linked by meaning, context, and even emotion. For example, a certain smell might remind you of a specific place.

LLMs have a simpler memory. Their knowledge is what they absorbed during training, stored in their internal settings. The AI version of working memory is the context window. This is the sequence of tokens the model is currently looking at. Once this window fills up, older information is forgotten. It's like a very short-term scratchpad.

Reasoning: Apparent vs. Conscious

Humans use different types of reasoning. We have quick, intuitive judgments (System One thinking) and slower, more logical reasoning (System Two thinking). LLMs are mainly trained on the outputs of System Two thinking—well-structured information.

While LLMs can produce steps that look like reasoning, they aren't consciously reasoning like we do. They are generating a plausible sequence of tokens that appears to be reasoning. If an LLM gets an answer right, it's because the token sequence happened to match logical rules, not because the model truly understands those rules. This is why LLMs can struggle with simple tasks that are easy for humans, like counting letters in a word.

Error: Hallucinations and Confabulations

One common issue with LLMs is hallucinations, where they confidently state things that are factually wrong. For humans, a better comparison might be confabulation. This is when a person unknowingly creates a false memory or explanation. It's not a deliberate lie; the person genuinely believes the information is true, even if it isn't. Our brains tend to fill in missing details, which can lead to these kinds of errors. So, while LLMs hallucinate, humans can confabulate, both showing how our systems can create believable but incorrect information.

Embodiment: Real-World Experience

Perhaps the most fundamental difference is embodiment. Humans are embodied beings; we exist in the real world. Our thoughts and actions are shaped by our interactions with our physical environment. Our understanding of things like 'wetness' comes from direct experience.

AI models, however, are disembodied. They exist as software on servers. An LLM doesn't taste, smell, or feel. Its knowledge of the physical world is secondhand, learned from words written by humans who do have embodied experiences. This is why LLMs often lack common sense. You know if you drop a marker, it will fall, not because you read about gravity, but because you live with it. An LLM might know the same fact if it's been stated enough times in text, but it could also suggest a scenario where a marker floats, perhaps from reading too much science fiction.

Key Takeaways

• Learning: Humans learn through neuroplasticity from few examples; LLMs learn through backpropagation from vast datasets.

• Processing: Humans process concepts associatively; LLMs process tokens sequentially.

• Memory: Humans have associative long-term memory; LLMs use a limited context window.

• Reasoning: Humans reason consciously; LLMs generate plausible token sequences that appear as reasoning.

• Error: LLMs hallucinate; humans can confabulate.

• Embodiment: Humans are embodied and learn from physical interaction; LLMs are disembodied and learn from text.

While AI models and human minds can produce similar outputs, the way they think is very different. Humans bring meaning and true understanding, while AI brings speed and a wide range of knowledge. When these different ways of thinking are combined, we can achieve the best results.