Despite their sophistication, LLMs are fundamentally language simulators. They mimic the functions of specific regions of the human brain - chiefly, Broca’s and Wernicke’s areas, **responsible for processing and generating language.** They do not perceive the world, form memories, reason causally, or reflect on their own outputs. This article explores: - What neural networks are and how they were inspired by the brain - Why LLMs resemble the brain’s language cortex - What parts of the brain are still “missing” in AI - How researchers are building toward AGI, piece by piece - Why AGI remains an open, complex challenge ## **1. Brains, AI, and Neural Networks** Brains, artificial intelligence (AI), and the aspiration for artificial general intelligence (AGI) are deeply interconnected. **The very idea of creating intelligent machines originated from our desire to replicate the capabilities of the human brain.** At the heart of modern AI systems, especially language models like GPT, lies a technology directly inspired by the brain itself: **neural networks**. Before we dive into what’s missing on the path to AGI, it’s essential to understand what neural networks are, how they work, and why their biological inspiration matters. ### **What Is a Neural Network?** A neural network is a type of **machine learning model designed to process data in a way that vaguely mimics how neurons in the brain function.** At its core, a neural network consists of many simple units called “artificial neurons” or “nodes,” organized in layers. Each node takes input, performs a simple computation, and passes the result to the next layer. Neural networks are universal function approximators, meaning they can model almost any pattern in data given enough depth, data, and compute. They are used today in everything from voice assistants to image recognition, autonomous vehicles, and large language models. Neural networks were originally modeled after the **neurons in the human brain**. A biological neuron: - Receives signals from other neurons through structures called **dendrites** - Integrates those signals in its **cell body** - Fires a signal through its **axon** to other neurons if it reaches a certain activation threshold Artificial neurons simplify this into: - Receiving inputs - Multiplying each input by a weight (representing its importance) - Summing the results - Applying an **activation function** to decide whether to “fire” the signal These simplified neurons are arranged in **layers**: an input layer (receiving data), one or more hidden layers (processing it), and an output layer (producing the final result). By chaining many of these layers, neural networks can model very complex behaviors, like recognizing faces or generating natural language. Neural networks also form the foundational architecture of large language models (LLMs), **serving as the computational engine that enables them to learn, represent, and generate human-like text.** Specifically, LLMs are built using deep neural networks, often transformer-based architectures, that process vast amounts of text data by learning statistical patterns between words, phrases, and contexts. But while the structure may echo the brain, the similarity is **shallow**. Real neurons are vastly more complex, operate asynchronously, communicate chemically and electrically, and interact in ways current models can only approximate in the broadest sense. ### **If Neural Networks Were Inspired by the Brain, Let’s Compare What We’ve Built to What We’re Missing** Now that we’ve seen how neural networks mimic brain structure in a simplified way, we can ask: **how close are we to replicating actual brain functions?** If the brain inspired LLMs and other AI systems, where do current technologies map to brain regions and what parts of the brain remain unmodeled or underdeveloped in AI? ## **2. Language Models as the Brain’s Language Center** LLMs are incredible at: - Predicting the next word - Rewriting, translating, summarizing - Synthesizing knowledge from huge amounts of text This function is akin to the **language centers of the human brain**: - **Broca’s Area:** Grammar, structure, speech production - **Wernicke’s Area:** Comprehension and meaning LLMs process massive corpora of text and learn statistical patterns between words. They have **no understanding of meaning**, just a powerful statistical mimicry of it. Nevertheless, LLMs can’t: - Understand physical reality - Form long-term memories - Take independent actions - Set and pursue goals - Reflect on their own reasoning They’re brilliant parrots, not thinking minds. ## **3. The Rest of the Brain — What AGI Still Needs** Artificial General Intelligence (AGI) refers to **a machine’s ability to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or beyond that of a human.** Unlike today’s narrow AI systems, which excel in specific domains, AGI would possess the flexibility to reason, plan, adapt, and solve problems in unfamiliar situations, demonstrating true general-purpose intelligence. To build AGI, we must go far beyond mimicking language. Here’s a guided tour of key brain structures and what they might look like in an AGI system. ### **1. Prefrontal Cortex: Planning, Reasoning, Executive Control** - **Biological Role:** Orchestrates goal-setting, logic, inhibition, long-term planning - **AGI Equivalent:** A control module that plans actions, decomposes tasks, delegates subtasks **Research Directions:** - Agent architectures (Auto-GPT, OpenAGI) - Hybrid symbolic-neural systems - LLM-powered toolchains (e.g. LangChain) Read more: [[AI Agents]] ### **2. Hippocampus: Episodic Memory** - **Biological Role:** Stores long-term memories, recalls experiences, tracks spatial context - **AGI Equivalent:** External memory modules for storing and retrieving information across sessions **Research Directions:* - Vector stores (Pinecone, FAISS) - [[RAG (Retrieval-Augmented Generation)]] - Memory-augmented transformers ### **3. Basal Ganglia: Action Selection** - **Biological Role:** Chooses which motor action to perform based on context and past reward - **AGI Equivalent:** A router that selects tools, APIs, or subagents based on learned utility **Research Directions:** - Reinforcement learning (RLHF) - Toolformer, Function-calling agents - Multi-agent orchestration ### **4. Cerebellum: Coordination and Timing** - **Biological Role:** Fine-tunes motion and timing - **AGI Equivalent:** Real-time control for robotics and temporal awareness **Research Directions:** - Robotics with reinforcement learning (DeepMind’s Control Suite) - Sim-to-real learning for robot manipulation ### **5. Amygdala: Emotion and Reward Estimation** - **Biological Role:** Evaluates threats, rewards, and emotional salience - **AGI Equivalent:** Value estimation functions, risk assessment **Research Directions:** - Reinforcement learning models - Safety alignment algorithms - Affective computing ### **6. Sensory Cortex: Perception** - **Biological Role:** Processes sight, sound, touch, smell - **AGI Equivalent:** Multimodal learning systems **Research Directions:** - CLIP, Flamingo, Perceiver (vision + language) - GPT-4V (text and image) - Gemini and other unified multimodal models ### **7. Default Mode Network: Introspection and Simulation** - **Biological Role:** Enables reflection, imagination, and mental simulation - **AGI Equivalent:** Recursive thinking, agent modeling, hypothetical reasoning **Research Directions:** - ReAct-style agents - Recursive self-asking prompts - Internal simulators (e.g., auto-reflective agents) ### **8. Cognitive Architectures for AGI** LLMs won’t become AGI by simply adding parameters. Real AGI will likely require **cognitive architectures** with specialized modules. Some leading concepts are: - **Global Workspace Theory:** Consciousness arises from shared access to a central “workspace” (akin to RAM). - **Blackboard Systems:** Modules post and read knowledge from a central board. - **Agentic Systems:** Modular agents for planning, memory, and action use LLMs as a language layer. ## **4. Building AGI is an Engineering Problem** The shift toward AGI isn’t about building smarter models, but better **systems**. This involves: - Connecting memory to reasoning - Handling feedback and failure gracefully - Creating simulation environments for testing - Designing human-in-the-loop training pipelines >[!note] >AGI is not a model. It’s an engineered mind. To reach AGI, we’re missing several key components: | Missing Feature | Why It Matters | Current Progress | | -------------------- | --------------------------- | ------------------------------------ | | Real-world grounding | Connect words to experience | Embodied agents, robotics | | Continual learning | Learn without forgetting | Few-shot learning, online RL | | Self-awareness | Monitor internal state | Meta-learning, reflection prompts | | Theory of mind | Model other agents' goals | Simulation agents, ToM benchmarks | | Value alignment | Understand ethics | Constitutional AI, RLHF, red teaming | Large language models represent a powerful step forward. They are the **language cortex** in silico, trained on the written history of humanity. But they are **not minds**. To build AGI, we must construct systems that reflect the **whole brain** - integrating perception, memory, reasoning, and self-awareness. This is not a project of scale alone, but of systems design, safety thinking, and deep interdisciplinary engineering. AGI may arrive. But it will arrive **not as magic**, but as a mosaic, painstakingly built from cognitive science, neuroscience, machine learning, and human ingenuity.