498 citations · 1.8k across the 31 of their papers we have counts for
7 papers · 1 filter
From cacophony to hierarchy: a principled framework for assessing AI consciousness
Shamil Chandaria, Arvo Muñoz Morán, Fernando Rosas +11
The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing theories th…
Faithful Reasoning Using Large Language Models
Antonia Creswell, Murray Shanahan
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. Thi…
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Antonia Creswell, Murray Shanahan, Irina Higgins
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reas…
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
Borja G. León, Murray Shanahan, Francesco Belardinelli
We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinfor…
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath
Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgett…
Towards Deep Symbolic Reinforcement Learning
Marta Garnelo, Kai Arulkumaran, Murray Shanahan
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demo…