Scientific Papers
Explore a selection of publications authored by ASI Alliance researchers and developers
Last updated
Explore a selection of publications authored by ASI Alliance researchers and developers
Last updated
Autonomous Intelligent Reinforcement Inferred Symbolism
This paper introduces AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism) to enable causality-based artificial intelligent agents. The system builds sets of causal rules from observations of changes in its environment which are typically caused by the actions of the agent. These rules are similar in format to rules in expert systems, however rather than being human-written, they are learned entirely by the agent itself as it keeps interacting with the environment.
ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via Accelerating Active Predictive Coding with Information Geometry & Diverse Cognitive Mechanisms
This paper introduces ActPC-Geom, an approach to accelerate Active Predictive Coding (ActPC) in neural networks by integrating information geometry, specifically using Wasserstein-metric-based methods for measure-dependent gradient flows. We propose replacing KL-divergence in ActPC's predictive error assessment with the Wasserstein metric, suggesting this may enhance network robustness.
Metagoals Endowing Self-Modifying AGI Systems with Goal Stability or Moderated Goal Evolution: Toward a Formally Sound and Practical Approach
We propose metagoals for AGI systems using fixed-point theorems from functional analysis, such as the Contraction Mapping Theorem and constructive approximations to Schauder's Theorem, applied to probabilistic models of behavior. These metagoals address goal stability and moderated goal evolution, balancing self-modification with the preservation of key goal invariants.