Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures

Abstract reasoning is a cornerstone of human intelligence, and replicating it with artificial intelligence (AI) presents an ongoing challenge. This study focuses on efficiently solving Raven's progressive matrices (RPM), a visual test for assessing abstract reasoning abilities, by using distributed computation and operators provided by vector-symbolic architectures (VSA). Instead of hard-coding the rule formulations associated with RPMs, our approach can learn the VSA rule formulations (hence the name Learn-VRF) with just one pass through the training data. Yet, our approach, with compact parameters, remains transparent and interpretable. Learn-VRF yields accurate predictions on I-RAVEN's in-distribution data, and exhibits strong out-of-distribution capabilities concerning unseen attribute-rule pairs, significantly outperforming pure connectionist baselines including large language models. Our code is available at https://github.com/IBM/learn-vector-symbolic-architectures-rule-formulations.

Unified machine learning tasks and datasets for enhancing renewable energy

Multi-tasking machine learning (ML) models exhibit prediction abilities in domains with little to no training data available (few-shot and zero-shot learning). Over-parameterized ML models are further capable of zero-loss training and near-optimal generalization performance. An open research question is, how these novel paradigms contribute to solving tasks related to enhancing the renewable energy transition and mitigating climate change. A collection of unified ML tasks and datasets from this domain can largely facilitate the development and empirical testing of such models, but is currently missing. Here, we introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets from six different application domains related to enhancing renewable energy, including out-of-distribution validation and testing data. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model. We further analyse the dimensions of each dataset; investigate what they require for designing over-parameterized models; introduce a set of dataset scores that describe important properties of each task and dataset; and provide performance benchmarks.