Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

An important challenge for safety in machine learning and artificial intelligence systems is a~set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive Computing, Special Issue "Artificial Superintelligence: Coordination & Strategy

The impact of service change on doctors' training (GMC 822)

The aim of this research is to gain a deeper understanding about the direct and indirect impacts of service change on doctors’ training: to understand if specific types of service change pose a risk to the training experience, and to gain insights into the contextual influences that undermine or enhance the education of doctors during service change

Digital Storytelling, comics and new technologies in education: review, research and perspectives

This work reviews the current application of one of the most widely used techniques in education around the world: Digital Storytelling (DS), along with comic and animation tools, and presents a study about the Greek educational system as well as posing questions concerning the form of a new study, design, implementation and assessment of educational project across all educational levels. Nowadays, people and students at all educational levels in the developed world are surrounded by multiple electronic media and are familiar with a variety of pictures, video and information from early childhood. The educational process, as it proceeds in parallel with fast technological and societal evolution, tries to smoothly adjust new educational methods without abandoning traditional teaching and moving away from its main aim: the establishment of knowledge

Language model AI and international commercial arbitration

This thesis dives deep into the world of a specific type of artificial intelligence (AI), Large Language Models (LLMs), and how they might impact international business disputes, or more specifically, international commercial arbitration. In an age where rapid advancement in technology is quickly reshaping our world, the legal field isn't immune to this transformation. Among the game-changers, language model AI could, due to its promising capacity of data-processing and outcome prediction, potentially make international arbitration quicker and less expensive, thereby providing easier access to justice for the commercial sector across the globe. However, it's not all smooth sailing. The study also identifies legal limitations regarding the use of LLMs in arbitration - issues related to bias, maintaining fair processes, keeping data private, and determining who is accountable when AI is involved. Overcoming these obstacles is crucial before AI can be confidently incorporated into arbitration. While LLMs hold exciting potential for international commercial arbitration, careful implementation is important. We need comprehensive rules and guidelines to ensure language model AI operates effectively and ethically in this arena. The use of AI should be a considered decision, keeping in mind the potential hurdles and working towards mitigating them.This thesis dives deep into the world of a specific type of artificial intelligence (AI), Large Language Models (LLMs), and how they might impact international business disputes, or more specifically, international commercial arbitration. In an age where rapid advancement in technology is quickly reshaping our world, the legal field isn't immune to this transformation. Among the game-changers, language model AI could, due to its promising capacity of data-processing and outcome prediction, potentially make international arbitration quicker and less expensive, thereby providing easier access to justice for the commercial sector across the globe. However, it's not all smooth sailing. The study also identifies legal limitations regarding the use of LLMs in arbitration - issues related to bias, maintaining fair processes, keeping data private, and determining who is accountable when AI is involved. Overcoming these obstacles is crucial before AI can be confidently incorporated into arbitration. While LLMs hold exciting potential for international commercial arbitration, careful implementation is important. We need comprehensive rules and guidelines to ensure language model AI operates effectively and ethically in this arena. The use of AI should be a considered decision, keeping in mind the potential hurdles and working towards mitigating them