Cortical Algorithms v. Large Language Models

Artificial Intelligence has come a long way, with Large Language Models (LLMs) like GPT and BERT leading the charge. These models have transformed industries, making AI capable of writing essays, summarizing documents, and even mimicking human creativity. Yet, they are far from perfect. Their limitations—such as reliance on massive datasets, inability to generalize, and lack of real-world grounding—highlight a deeper question: Are we approaching AI the wrong way?

Enter Numenta and its cortical theory, a biologically inspired approach to AI that could redefine the field. By mimicking the human brain’s neocortex, Numenta’s algorithms aim to solve the problems that LLMs cannot. Here’s how they differ—and why cortical theory might be the future of AI.

LLMs: Statistical Powerhouses with Limits

Large Language Models operate on a foundation of statistics. They predict the next word or token by identifying patterns in vast datasets. Their performance is undeniably impressive, but their architecture is fundamentally flawed when compared to human intelligence.

• Static Learning: LLMs are trained on fixed datasets. Once the model is deployed, it cannot learn incrementally without retraining from scratch. This “frozen” nature is in stark contrast to how humans continuously learn and adapt.

• Data Dependency: Training LLMs requires terabytes of data and immense computational resources. Despite their scale, they lack the ability to make sense of the world outside the dataset they are trained on.

• Lack of Embodiment: LLMs operate abstractly, with no connection to the physical world. They can’t understand the sensory or motor experiences that humans rely on to navigate reality.

Cortical Theory: A New Paradigm Inspired by the Brain

Numenta’s cortical theory takes a different approach, inspired by the neocortex—the part of the brain responsible for perception, reasoning, and learning. Instead of relying on brute-force computation and massive datasets, cortical algorithms focus on continuous learning, sensorimotor integration, and efficiency.

Key Advantages of Cortical Theory:

1. Continuous Learning: Unlike LLMs, cortical-based systems can learn in real time without requiring retraining. This mirrors how humans absorb new information without forgetting old knowledge.

2. Efficiency: Numenta’s approach is far less resource-intensive. It aims to mimic the brain’s remarkable ability to learn from minimal data and operate efficiently, even on small hardware.

3. Sensorimotor Grounding: Cortical theory emphasizes the integration of sensory inputs with motor actions. This sensorimotor loop allows for a deeper understanding of the environment, enabling AI to “learn by doing” rather than merely processing static data.

4. General Intelligence: While LLMs are narrow in scope, cortical theory seeks to replicate the brain’s ability to learn multiple modalities—language, vision, movement, and more. This positions it as a candidate for achieving Artificial General Intelligence (AGI).

Why Cortical Theory Hasn’t Taken Over Yet

Despite its promise, the cortical approach is still in its early stages. The AI community has focused on LLMs because they are easier to implement within existing infrastructures. Numenta’s vision, while ambitious, requires breakthroughs in both hardware and algorithms. Additionally, funding and research for biologically inspired AI have lagged behind the investment in more traditional AI architectures.

The Future of AI: Beyond the LLM

As industries push the boundaries of what AI can do, it’s essential to question whether current approaches are sustainable. LLMs have brought us far, but their reliance on brute force is a bottleneck. Numenta’s cortical theory represents a leap forward—one that could unlock more efficient, adaptable, and intelligent systems.

The next wave of AI innovation may come from mimicking nature’s most successful design: the human brain.

What do you think? Are we ready to move beyond LLMs toward biologically inspired AI?