ML Fundamentals

Cognitive Architectures

Blueprints for AI that try to mimic the structure of the human mind, including memory, goals, and perception.

Definition

Computational frameworks that model the structure of human cognition to guide AI design. Rather than solving one narrow task, they integrate distinct modules for perception, working and long-term memory, goal management, reasoning, and action into a unified system. Long-standing examples include SOAR and ACT-R, and the ideas increasingly inform how autonomous agents combine memory, planning, and tool use.

Why it matters

Cognitive architectures represent the effort to move AI beyond pattern matching toward systems that reason, remember, and pursue goals over time. Their concepts are resurfacing in agent design, where durable memory and structured planning separate a capable agent from a stateless chatbot.

Related terms in ML Fundamentals

Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
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