Noumenal Labs White Paper: How To Build A Brain

TL;DR

This white paper outlines principles for designing grounded, scientifically-inclined artificial intelligence that enhances human understanding and action without replacing humans, emphasizing causal discovery and real-world modeling.

Contribution

It introduces a novel design framework for AI grounded in the real world, inspired by scientific inquiry and causal physics discovery.

Findings

Grounded models improve understanding of the human world

AI should be capable of autonomous causal physics discovery

Practical applications include 3D world modeling and time series analysis

Abstract

This white paper describes some of the design principles for artificial or machine intelligence that guide efforts at Noumenal Labs. These principles are drawn from both nature and from the means by which we come to represent and understand it. The end goal of research and development in this field should be to design machine intelligences that augment our understanding of the world and enhance our ability to act in it, without replacing us. In the first two sections, we examine the core motivation for our approach: resolving the grounding problem. We argue that the solution to the grounding problem rests in the design of models grounded in the world that we inhabit, not mere word models. A machine super intelligence that is capable of significantly enhancing our understanding of the human world must represent the world as we do and be capable of generating new knowledge, building on what we already know. In other words, it must be properly grounded and explicitly designed for rational, empirical inquiry, modeled after the scientific method. A primary implication of this design principle is that agents must be capable of engaging autonomously in causal physics discovery. We discuss the pragmatic implications of this approach, and in particular, the use cases in realistic 3D world modeling and multimodal, multidimensional time series analysis.