AI vs Human Thinking: 6 Surprising Gaps in Large Language Models

Key Points

• Learning: Humans learn dynamically through neuroplasticity, often from just a few experiences, while LLMs rely on backpropagation and vast datasets, with static knowledge after training.
• Processing: The human brain processes information in parallel, focusing on concepts, whereas LLMs process tokens sequentially or with attention mechanisms, based on statistical patterns.
• Memory: Humans have multiple memory systems (sensory, working, long-term) that are associative, while LLMs store knowledge in fixed weights and use a limited context window.
• Reasoning: Humans use intuitive and logical reasoning (System 1 and System 2), while LLMs generate token sequences that mimic reasoning without true understanding.
• Error: LLMs can hallucinate, producing incorrect but confident outputs, similar to human confabulations, where false memories feel true.
• Embodiment: Humans learn through physical interaction with the world, while LLMs, being disembodied, lack sensory experiences and may miss common sense.

AI vs Human Thinking: Minds and Machines

Imagine you’re chatting with a friend who seems to know everything—poetry, trivia, even your favourite recipes. Now imagine that friend is a computer program. That’s what large language models (LLMs) are like: they generate responses that feel human, but under the hood, they’re crunching numbers, not thoughts. Both the human brain and LLMs rely on networks to process information, and both can learn and improve over time. But the similarities end there. Humans think with meaning, emotion, and physical experience; LLMs think with data, patterns, and probabilities. Let’s break it down across six key areas to see how they compare.

1. Learning: Building Knowledge

Learning is how we acquire new skills or facts, but humans and LLMs go about it in very different ways.

Human Learning: The Power of Neuroplasticity

The human brain learns through neuroplasticity, its ability to rewire neural connections based on experiences. When you practice a new skill, like juggling, your brain strengthens the connections between neurons involved in that task. This is summed up by Hebbian theory: “neurons that fire together wire together.” What’s amazing is that humans can learn from just a few tries. For example, a toddler might see a giraffe at the zoo once and remember it forever.

Real-World Analogy: Think of your brain as a garden. Each new experience is like planting a seed. With a little watering (repetition), it grows into a strong plant (a lasting memory). Sometimes, one vivid experience is enough to make that plant stick around for years.

AI Learning: Backpropagation and Big Data

LLMs learn through backpropagation, a process where they adjust internal parameters (weights) to reduce errors in predicting text. This requires massive amounts of data—think millions of sentences from books, websites, and social media. To learn a word like “serendipity,” an LLM might need to see it thousands of times in different contexts. Once trained, its knowledge is mostly fixed, unlike our ever-adapting brains.

Example: To learn what a “bicycle” is, a child might need a few rides or sightings, while an LLM needs thousands of text examples mentioning bicycles. If you introduce a new type of bike, humans might get it after a quick look; an LLM would need retraining with lots of new data.

Code Snippet: Here’s a simplified Python example showing how a neural network adjusts weights during training (don’t worry, it’s just to illustrate the idea):

import numpy as np

# Simple neural network with one layer
weights = np.random.rand(2, 1)  # Random initial weights
inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])  # Example inputs
targets = np.array([[0], [1], [1], [0]])  # Desired outputs (XOR problem)

learning_rate = 0.1
for _ in range(1000):  # Many iterations
    predictions = np.dot(inputs, weights)  # Forward pass
    errors = targets - predictions  # Calculate errors
    weights += learning_rate * np.dot(inputs.T, errors)  # Update weights (backpropagation)

print(weights)  # Adjusted weights after training

Comparison: Humans are flexible, learning from minimal exposure and adapting on the fly. LLMs need huge datasets and stay static unless retrained. Recent advances like few-shot learning try to bridge this gap, but AI still lags behind human adaptability.

2. Processing: Crunching the Input

How we handle incoming information is another big divide.

Human Processing: Parallel and Conceptual

The human brain is a parallel processing powerhouse, with billions of neurons and trillions of synapses firing at once. Different regions handle specific tasks—vision, language, movement—but they work together seamlessly. We process concepts, not just data. When you hear, “The moon is full tonight,” you picture the glowing orb and feel its magic, connecting it to past nights.

Analogy: Your brain is like a bustling city, with different neighborhoods (brain regions) handling traffic, utilities, and events all at once, creating a vibrant, unified experience.

AI Processing: Tokens and Attention

LLMs break text into tokens (words or subwords) and turn them into numerical vectors. In transformer models, an attention mechanism decides which tokens matter most for predicting the next one. For example, in “The dog chased the cat,” the model might focus on “dog” and “chased” to predict “cat.” While transformers process tokens in parallel within layers, they’re still limited to text patterns, not real-world understanding.

Example: Reading “The dog chased the cat,” you visualize the chase. An LLM processes it token by token, predicting “cat” based on statistical likelihood, not a mental image.

Analogy: Imagine listening to a story in a noisy café. You focus on your friend’s words, ignoring the chatter—that’s like the attention mechanism, zeroing in on relevant tokens.

3. Memory: Holding Onto Knowledge

Memory is how we store and retrieve information, but the systems are night and day.

Human Memory: Layered and Associative

Humans have three memory types:

• Sensory Memory: Brief, fleeting impressions (e.g., the afterimage of a bright light).
• Working Memory: Short-term storage for tasks, holding about 7 items (e.g., a phone number you’re dialing).
• Long-Term Memory: Vast, lasting storage for facts and skills (e.g., your first bike ride).

Our memories are associative, linked by meaning or emotion. The smell of pine trees might transport you to a childhood camping trip.

Example: Hearing “Happy Birthday” might spark memories of your last party, complete with cake and laughter.

AI Memory: Weights and Windows

LLMs store knowledge in their weights, fixed during training, like a snapshot of the internet. Their context window—the current input sequence—acts as working memory but forgets when full. Unlike human memories, AI’s aren’t tied to personal experiences.

Example: Ask an LLM about “pine trees.” It might describe them accurately if trained well, but it won’t feel their scent or recall a specific forest moment.

4. Reasoning: Thinking It Through

Reasoning is about solving problems and drawing conclusions, but the processes differ.

Human Reasoning: Fast and Slow

Humans use two reasoning systems (per Daniel Kahneman):

• System 1: Quick, intuitive (e.g., dodging a ball).
• System 2: Slow, logical (e.g., balancing a checkbook).

Example: You solve “All birds fly. A sparrow is a bird. So, a sparrow flies” using System 2 logic, instantly knowing it’s true.

AI Reasoning: Mimicking Logic

LLMs don’t reason; they generate token sequences that look like reasoning, based on training data patterns. Chain-of-thought prompting can make them list steps, but it’s still pattern matching, not understanding.

Example: Asked to count the ‘r’s in “strawberry,” an LLM might guess wrong, relying on patterns, while you’d count each letter logically.

5. Error: When Things Go Wrong

Mistakes happen, but why and how they occur sets humans and AI apart.

AI Hallucinations

LLMs hallucinate, confidently stating falsehoods due to data gaps or pattern errors.

Example: An LLM might say, “The Eiffel Tower is in Florida,” if its training data included such a mistake.

Human Confabulations

Humans confabulate, creating false memories they believe are true, filling in gaps to make sense of things.

Example: You might “recall” a family picnic that never happened, feeling it’s real because of emotional associations.

Comparison: Both produce plausible errors, but human confabulations are personal, while AI hallucinations are data-driven.

6. Embodiment: Body and Soul

The biggest gap is embodiment—living in the physical world.

Human Embodiment

Humans are embodied, shaped by physical interactions. You know “hot” from touching a stove, not just reading about heat.

Example: You understand “falling” because you’ve tripped or jumped, feeling gravity’s pull.

AI Disembodiment

LLMs are disembodied, existing as code on servers. Their knowledge comes from text, not senses, so they might miss common sense.

Example: An LLM might say, “A feather floats upward,” if it’s read too many fantasy stories, not knowing gravity firsthand.

Comparison: Human thinking is grounded in physical reality; LLMs float in a digital void, relying on human-written words.

Conclusion: Minds and Machines Together

Humans and LLMs both learn, process, remember, reason, err, and exist—but differently. Humans are dynamic, conceptual, associative, logical, experiential, and embodied. LLMs are static, token-based, pattern-driven, and disembodied. AI shines in speed and data volume; humans excel in meaning and adaptability. Together, they can achieve great things—AI crunching data, humans adding soul.

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