Conceptual articles, historical explainers, and terminology guides. Updated regularly as the field develops.
Every neural network shares the same basic structure, regardless of what it does. Understanding that structure is the single most useful step for anyone trying to follow AI discussions. This guide covers the three building blocks and how they interact during training and use.
Two distinct periods of reduced funding and lost confidence shaped the trajectory of artificial intelligence research. Neither collapse was permanent. Understanding why they happened helps explain the conditions that eventually led to the current period of development.
Three words that appear constantly in coverage of large language models, rarely defined in the same place. This article covers all three, explains how they relate to each other, and clarifies the common misconceptions around each one.
Both retrieve and present information. But the mechanisms are fundamentally different. This explainer traces how each system works and why those differences matter for how we interpret and verify outputs.
The two most commonly referenced learning paradigms in machine learning. The names are intuitive but the details matter. This article covers how each approach is used, when each is appropriate, and what the training process looks like in both cases.
A summer workshop at Dartmouth College produced the term "artificial intelligence" and gathered the researchers who would define its first generation. What was proposed, what was achieved, and what the conference reveals about how new scientific fields are constructed.
The 2017 paper "Attention Is All You Need" introduced a model architecture that now underlies most large AI systems. This guide explains the key concepts behind transformers without requiring familiarity with linear algebra or gradient descent.
All three terms are widely used in discussions about AI risk and reliability. All three are frequently misunderstood or used loosely. This article provides working definitions and explains why precision in terminology matters for public understanding.
No AI model creates knowledge from nothing. The data used during training determines what patterns a model can recognize and what responses it can generate. This article examines the relationship between training data and model output.
The AI Glossary covers the vocabulary used across all our articles. A useful companion to the reading list above.
Go to the Glossary