The field of neuro-symbolic systems is an exciting area of research that combines the power of machine learning with the rigour of symbolic reasoning. Neural systems have shown great promise in a wide range of applications, from robotics and autonomous systems to...
Modern financial markets represent fertile soil for AI systems. As of October 2019, at least two thirds of the UK financial services companies use AI, with its growing adoption in trading, risk management and pricing. Should the regulator trust the AI technology that...
Matching is a fundamental problem in combinatorial optimisation with multiple applications in AI, like in belief propagation [10], multi-agent resource allocation algorithms [6], and constraint solving [16], and in economics, like the stable marriage problem [17] and...
As part of the model-based approaches to safe and trusted AI, this project aims to shed light on the phenomenon of robust generalisation as a trade-off in geometric deep networks. Unfortunately, classical learning theory is incapable of explaining the behaviour...
Temporal logic (TL) is arguably the primary language for formal specification and reasoning about system correctness and safety. They enable the specification and verification of properties such as “will the agent eventually reach the target while avoiding...
Recent efforts to Natural Language Understanding (NLU) have been largely exemplified in tasks such as natural language inference, reading comprehension and question answering. We have witnessed the shift of paradigms in NLP from fine-tuning a large-scale pre-trained...
We developed a framework for causal explanations of image classifiers based on the principled approach of actual causality [1] and responsibility [2], the latter pioneered by Dr Chockler. Our framework already resulted in a number of publications in top-ranked...
We developed a framework for causal explanations of image classifiers based on the principled approach of actual causality [1] and responsibility [2], the latter pioneered by Dr Chockler. Our framework already resulted in a number of publications in top-ranked...
An important long-term concern regarding the ethical impact of AI is the so called ‘value alignment problem’; that is, how to ensure that the decisions of autonomous AIs are aligned with human values. Addressing this problem, as well as the broader...
Neurosymbolic systems seek to combine the strengths of two major classes of AI algorithms: neural networks, able to recognise patterns in unstructured data, and logic-based systems, capable of powerful reasoning. One of the bottlenecks of current neurosymbolic systems...
Our modern lives are increasingly governed by ubiquitous AI systems and an abundance of digital data. More and more products and services are providing us with better tools and recommendations for our professional, personal, and entertainment activities. With the...
The current state of the art in generative modelling is dominated by neural networks. Despite their impressive performance on many benchmark tasks, these algorithms do not provide tractable inference for common and important queries, e.g. marginalization and...
