Fractal Graph-of-Thought. Rhizomatic Mind-Mapping for Ai-Agents, Web-Links, Notes, and Code.
AI Data Centers Will Be Obsolete (Geometric Reasoning Explained)
57 Ideas & Questions about Cognitive Infrastructures
The Fabric and the Brain
Articulating agent ecologies with high-personality planetary computation
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
AI: A Guide for Thinking Humans | Melanie Mitchell | Substack
I write about interesting new developments in AI. Click to read AI: A Guide for Thinking Humans, by Melanie Mitchell, a Substack publication with tens of thousands of subscribers.
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
MindNode
MindNode helps you capture, connect, and evolve ideas visually as mind maps and outlines.
Mathematical methods and human thought in the age of AI
Artificial intelligence (AI) is the name popularly given to a broad spectrum of computer tools designed to perform increasingly complex cognitive tasks, including many that used to solely be the...
AI That Evolves in the Wild | Edge.org
I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.
Cognitive Architectures for Language Agents
Recent efforts have augmented large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning, leading to a new class of language agents. While these agents have achieved substantial empirical success, we lack a framework to organize existing agents and plan future developments. In this paper, we draw on the rich history of cognitive science and symbolic artificial intelligence to propose Cognitive Architectures for Language Agents (CoALA). CoALA describes a language agent with modular memory components, a structured action space to interact with internal memory and external environments, and a generalized decision-making process to choose actions. We use CoALA to retrospectively survey and organize a large body of recent work, and prospectively identify actionable directions towards more capable agents. Taken together, CoALA contextualizes today’s language agents within the broader history of AI and outlines a path towards language-based general intelligence.
Reticulum Network
For the better part of a generation, we have been taught to visualize the digital world through the lens of hierarchy. The mental maps we carry are dominated by a single, misleading image: The Cloud.
The Global Brain Argument: Nodes, Computroniums and the Ai Megasystem (Target Paper for Special Issue)
The Global Brain Argument contends that many of us are, or will be, part of a global brain network that includes both biological and artificial intelligences (AIs), such as generative AIs ...
