About

Our Name, Our Vision

Why eigenheads? Our AI assistant Claude did the research:

"'Eigenhead' refers to a facial representation technique derived from the eigenface method introduced by Turk and Pentland in 1991. This approach extracts essential facial features as eigenvectors, creating a reduced-dimensional space for efficient face recognition."

The scientific definition came later for us. Our journey with the term "eigenheads" began in the realm of science fiction—specifically William Gibson's 1996 novel Idoru, which our founder discovered while living in San Francisco in 1999. Claude has read it too:

"In William Gibson's novel Idoru, eigenheads are virtual personality constructs derived from mathematical analysis of human behavioral patterns. Gibson repurposed the mathematical term to represent digital identity models that capture the essential qualities of human consciousness in virtual space."

Captivated by the concept, our founder registered the eigenheads.com domain in 1999, waiting for its purpose to reveal itself. When establishing our consulting practice in 2014, the name choice became obvious. Our network responded enthusiastically to "eigenheads"—a name that elegantly balances analytical precision (the mathematical reference) with humanity (heads).

At the Intersection of Mathematics and Intelligence

Now in 2025, amid extraordinary advancements in Artificial Intelligence, the name "eigenheads" resonates more profoundly than ever. The "eigen" prefix connects us to linear algebra—the mathematical foundation powering today's AI revolution. When we asked Claude about this field, it explained:

"Linear algebra covers the study of vectors, vector spaces, linear transformations, and systems of linear equations. It includes concepts such as matrices, determinants, eigenvalues, eigenvectors, and linear independence. The field provides fundamental mathematical tools used across many disciplines including physics, computer science, engineering, and data science, particularly for solving systems of equations and analyzing geometric transformations."

Claude further described its impact on AI in approachable terms:

"Linear algebra is basically the backbone of all AI today. When you hear about neural networks or large language models, they're really just doing fancy math with matrices and vectors—transforming data from one space to another. Everything from how words get converted to numbers, to how the computer "learns" patterns, relies on these core linear algebra operations happening at massive scale."

The Human-AI Frontier

The groundbreaking book Why Machines Learn illuminates AI's fascinating evolution since the 1940s, revealing surprisingly accessible mathematical foundations behind systems that increasingly feel human in their interactions. These developments have brought us to a point where conversing with AI can sometimes feel remarkably like talking to another person. Yet, as Claude informed us when we asked about the current state of AI benchmarks:

"As of my last knowledge update in October 2024, no AI has officially passed the annual Loebner Prize competition (the most well-known formal Turing test competition) in its strictest interpretation, where judges can't distinguish at all between the AI and a human."

We believe eigenheads perfectly captures our mission: facilitating meaningful conversations between humans and artificial systems to explore concepts, develop solutions, and create implementations that help humanity evolve—whether AGI arrives imminently or remains forever on the horizon.