Join the Club

Your Bi-Weekly Dose Of Everything Optimism

Home/AI

Tag: AI

Our efforts to municipalize and to right-size Folsom Street remind me of the passion we felt in the 1960s about ending the war. Our absolute certainty about the dangers of the military-industrial complex and the war’s immorality trumped all else. The majority voting bloc on Boulder’s City Council seems to feel just as strongly that …

From the flickering shadows of a cave, where early humans first harnessed fire, to the dazzling glow of today's digital screens, humanity's journey has been a relentless pursuit of progress. Across millennia, diverse political subdivisions – be they ancient kingdoms, burgeoning republics, or modern democracies – have emerged, each a unique organizational attempt to navigate …

As AI advances at an accelerating pace, humanity finds itself at a crossroads between immense progress and critical ethical responsibility. The convergence of AI with human society raises profound questions, not only about its technical capabilities but about its philosophical and societal implications. At its most ambitious, AI has the potential to unify humanity's collective …

The defining capability of AI is its unprecedented ability to cross-correlate vast and disparate data sets—scientific, economic, historical, environmental, and social—simultaneously and without fatigue. This is not simply about data analysis; it is about discovering truth through pattern recognition at scales beyond human cognitive limits. The more data points—or “vectors”—an AI can analyze in relation …

In 2024, global military spending reached $2.7 trillion. Solving global homelessness? Estimated at around $28 trillion. At first glance, housing everyone seems far more expensive—until you realize that just five or six years of current military budgets could fund it entirely. This isn’t just about morality. It’s about math, predictability, and progress. AI Makes the …

Retrieval-Augmented Generation (RAG) models combine a retrieval component—often a vector‐based search over an external knowledge source—with a generative language model. The basic RAG pipeline is: Query encoding & retrieval Cross-correlation among retrieved contexts Generator conditioning Why cross-correlation matters in RAG Reducing redundancy: When multiple retrieved chunks say the same thing, naive concatenation wastes tokens. Cross-correlation …

Ask Richard AI Avatar