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pro vyhledávání: '"Ward, Francis"'
Autor:
Ward, Francis Rhys, Yang, Zejia, Jackson, Alex, Brown, Randy, Smith, Chandler, Colverd, Grace, Thomson, Louis, Douglas, Raymond, Bartak, Patrik, Rowan, Andrew
Language models (LMs) can exhibit human-like behaviour, but it is unclear how to describe this behaviour without undue anthropomorphism. We formalise a behaviourist view of LM character traits: qualities such as truthfulness, sycophancy, or coherent
Externí odkaz:
http://arxiv.org/abs/2410.04272
Trustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or the AI system itself, may have incentives for evaluations to understa
Externí odkaz:
http://arxiv.org/abs/2406.07358
Intention is an important and challenging concept in AI. It is important because it underlies many other concepts we care about, such as agency, manipulation, legal responsibility, and blame. However, ascribing intent to AI systems is contentious, an
Externí odkaz:
http://arxiv.org/abs/2402.07221
Deceptive agents are a challenge for the safety, trustworthiness, and cooperation of AI systems. We focus on the problem that agents might deceive in order to achieve their goals (for instance, in our experiments with language models, the goal of bei
Externí odkaz:
http://arxiv.org/abs/2312.01350
How to detect and mitigate deceptive AI systems is an open problem for the field of safe and trustworthy AI. We analyse two algorithms for mitigating deception: The first is based on the path-specific objectives framework where paths in the game that
Externí odkaz:
http://arxiv.org/abs/2306.14816
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from human feedback. Our method improves prior work by generalising human pref
Externí odkaz:
http://arxiv.org/abs/2209.14010
Autor:
Vosylius, Vitalis, Wang, Andy, Waters, Cemlyn, Zakharov, Alexey, Ward, Francis, Folgoc, Loic Le, Cupitt, John, Makropoulos, Antonios, Schuh, Andreas, Rueckert, Daniel, Alansary, Amir
Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep le
Externí odkaz:
http://arxiv.org/abs/2008.06098
Akademický článek
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Autor:
Ward, Francis
Overview This is a study of a key development in workforce data collection and the development of workforce intelligence in social care in England. -- Aims To investigate the establishment of the National Minimum Data Set for Social Care [NMDS-SC] in
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631290