General
LLMs vs Human Brain
May 5, 2025
From neural pathways to neural networks: Understanding the brain-inspired evolution of AI models.
Child Brain: The Original Foundational Model
LLM can be thought of as a human brain. A child’s brain is like a foundational model, it comes with some built-in capabilities injected via your DNA:
Reflexes like sucking and grasping
Basic pattern detection like recognising faces and objects
Emotional responses like crying and feeling happy
“Your brain is like the ultimate foundational model — evolved with general abilities, then fine-tuned through life experiences!”
Learning Through Experience: Fine-Tuning Of Brain
As the child grows, he learns from his surroundings. He starts recognizing the faces of family members, begins to understand language, starts walking, speaking, and expressing his feelings. These everyday experiences quietly shape and fine-tune his brain.
Each time he smiles at a familiar face, takes a step, or says a new word, his brain builds and strengthens tiny roads between brain cells — called pathways. The more he repeats an action or remembers something, the smoother and faster these mental roads become, making it easier next time.
And each time he stumbles, forgets, or gets corrected, his brain repairs old roads or builds new ones, adjusting its map to do better next time.
Bit by bit, through every smile, every stumble, and every sound, his brain is learning — turning experience into stronger, smarter pathways, like turning dirt trails into highways that help him grow.
“The brain learns by trial and error — strengthening good pathways, changing bad ones, all based on experience.”
How LLMs Learn: Prediction, Feedback, and Rewards
Inside an LLM, instead of neurons and brain pathways, there are nodes and weights — huge networks of math connections.
In many ways, large language models (LLMs) learn much like the human brain does. Before it can chat, answer questions, or write stories, the LLM is trained on vast amounts of text — books, articles, and conversations — much like a child absorbing the world around him.
As it reads, the model tries to predict the next word in a sentence.
When it gets it right, the model receives a kind of reward: its internal weights — the numerical connections between its neurons — are strengthened.
When it gets it wrong, the loss function calculates the error, and through a process called gradient descent, the model adjusts its weights, fine-tuning itself to improve.
This is much like the child’s brain, which rewires its pathways after every new experience, strengthening successful patterns and correcting mistakes.
But learning doesn’t stop there. Just as children learn not only from observation but also from feedback and rewards — a smile from a parent, applause for a word well spoken — modern LLMs undergo an extra phase called reinforcement learning. In this stage, human trainers review the model’s responses and provide feedback, giving ‘rewards’ for good answers and penalties for poor ones.
Over time, through billions of such small updates, the model grows more accurate, more helpful, and even more aligned — much like a child turning life’s rewards and corrections into wisdom.
“Both — the human brain and the LLM — learn through a dance of prediction, feedback, correction, and reward, gradually shaping raw potential into real intelligence.”
Brain Is More Than One Model — It’s Many Working Together
While large language models mirror some aspects of how we learn, the human brain is far more complex and powerful. In fact, if we look deeper, the brain is not like one giant LLM — it is more like a collection of many fine-tuned models working together.
Each part of the brain has its own specialisation:
Visual cortex processes what we see
Language centres handle speech and comprehension
Motor cortex controls movement
Emotional centres like the amygdala manage feelings and reactions.
These specialized ‘models’ are fine-tuned through experience, just like an LLM is fine-tuned on specific tasks. But the magic lies in how they collaborate.
Unlike a language model that focuses mainly on text, the brain seamlessly blends sight, sound, touch, memory, and emotion. It can instantly connect a spoken word to a familiar face, recall past experiences, predict outcomes, and even generate creative ideas — all at once.
It is this parallel processing and integration across multiple specialized areas that makes human intelligence so rich and adaptable.
So while LLMs learn through prediction and feedback much like a child does, the brain operates at an entirely different scale — a dynamic network of many ‘mini models,’ constantly communicating and evolving together.
“The human brain is more like a collection of many fine-tuned LLMs working together, rather than a single giant LLM.”
AI’s Future: Many Models Thinking Together
Perhaps this is where the future of AI is headed. Instead of building ever-larger single models, researchers are now exploring ways to create systems where multiple specialized models — for vision, language, reasoning, and even emotion detection — work together, much like the different regions of the brain collaborate.
By moving from isolated models to integrated, multi-modal systems, AI may take a step closer to the rich, flexible intelligence that humans display. Just as the brain’s power comes from its ability to blend diverse capabilities, the next generation of AI might be built not as a single giant model, but as an ecosystem of fine-tuned models working in harmony.
We are already seeing this idea take shape through emerging agent-based AI frameworks. Instead of relying on one large model to handle everything, these systems deploy multiple specialized agents — each designed for a particular role, such as retrieving information, writing code, summarizing text, or planning the next steps.
In simple terms, an agent in AI is like an independent expert: it can take input, make decisions, and interact with its environment or with other agents to move closer to a goal. For example, one agent might search for facts, another might organize them, while a third figures out the best action to take next.
Like skilled members of a team, each agent contributes its part — and when they collaborate, exchanging results and refining their approach based on feedback, they can solve more complex problems than any single model could handle alone.
This shift echoes how different regions in the human brain work together, combining vision, memory, language, and reasoning to create rich, flexible intelligence.
Early frameworks like AutoGPT, LangChain agents, and Microsoft’s Jarvis hint at this next frontier: AI systems evolving from single-task responders into coordinated teams of agents working toward larger, more dynamic goals.
“The next leap in AI won’t come from building bigger models — but from teaching many models to think, learn, and act together. Just as the human brain does.”
Concluding Thoughts
As AI systems evolve, we are moving from isolated models toward collaborative, context-sharing teams of agents — much like the human brain’s own network of specialized yet interconnected regions.
Concepts like agent frameworks and protocols like Agent-to-Agent protocol (A2A by Google), Model Context Protocol (MCP by Anthropic), and Agent Communication Protocol (ACP by IBM) are early steps in this journey, hinting at an AI future that is more dynamic, adaptable, and intelligent.
Just as a child’s brain grows richer through experiences and collaboration among its parts, AI is beginning to learn, reason, and act not as a solo performer, but as an orchestra of models working in harmony.
This shift doesn’t just bring AI closer to human-like intelligence — it opens up new possibilities for systems that can tackle real-world complexity with flexibility and depth.
“The future of AI isn’t one giant model — it’s many models, thinking together.”
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