In this module you will examine the socio-technical influence of Artificial Intelligence (AI) on governance practices and global economic systems. You will explore how AI shapes decision-making processes, labour markets, international trade, public policy, ethics, and security, while also highlighting how these in turn affect the data, intellectual resources and human engagement on which the success of AI depends. The module also explores the challenges related to trust, regulation, inequality, and global cooperation. The course will address both the theoretical and practical aspects of AI integration into governance and economic structures.
The module will introduce the fundamentals of a range of important techniques, so that the way that these models learn from data is understood. Applications will also be shown of applying AI and ML to chemistry. Workshops will give hands-on experience with running and training ML models and these methods will be applied to specific examples as part of the mini-project.
This module will provide an overview of how machine learning and Artificial Intelligence can be used to answer questions in different fields of psychology.
This course focuses on the ethical integration and applications of Artificial Intelligence (AI) in business settings, with a specific emphasis on the challenges and opportunities related to algorithmic bias, inequalities, and diversity management as part of social sustainability. Taking a holistic approach to sustainability, students will explore the theoretical concepts and practical applications to understand how AI technologies can perpetuate or mitigate biases, create environmental issues and how this is all linked to economic sustainability. Based on critical evaluation of case studies regarding AI-induced inequalities, students will learn how to develop strategies for managing diversity and apply ethical frameworks to AI development and implementation.
This module focuses on AI technologies and its applications within sustainability. The core aim is to develop a conceptual understanding of the research challenges for the SustAI CDT themes. This will help to build a common language among the cohort, enabling them to work more effectively together. The module draws heavily on research, case studies and tutorials delivered by academics as well as our partners in industry, government and the third sector.
This course focuses on the ethical integration and applications of Artificial Intelligence (AI) in business settings, with a specific emphasis on the challenges and opportunities related to algorithmic bias, inequalities, and diversity management as part of social sustainability. Taking a holistic approach to sustainability, students will explore the theoretical concepts and practical applications to understand how AI technologies can perpetuate or mitigate biases, create environmental issues and how this is all linked to economic sustainability. Based on a critical evaluation of case studies regarding AI-induced inequalities, students will learn how to develop strategies for managing diversity and apply ethical frameworks to AI development and implementation.
This thought-provoking course introduces students to the AI technologies, and more broadly, the algorithms currently informing criminal justice policy and practice in contemporary justice systems. Theory and research from the fields of criminology and sociology are used to explore the complex role of the technologies. Students are also introduced to digital research methods for researching criminological and sociological topics to influence criminal justice policy. Additionally, the module considers the methodological and ethical issues that arise when researching topics such as online radicalisation and victimisation, for criminal justice policy impact. While the module focuses on England and Wales, in order to deepen students’ appreciation of the merits and demerits of AI technologies and algorithms more broadly, and perceive common problems inherent in the technologies, international comparisons are embedded throughout the module. Participation in this module does not require prior knowledge of how algorithms are operationalised or their technical dimensions. The module focuses on criminological and sociological perspectives on their use and their broader social, political, and cultural implications.
In this module you will explore the application of Artificial Intelligence (AI) in analysing social problems and formulating public policy responses. You will examine how AI technologies are reshaping our understanding of societal issues and influencing policy-making processes. The module is designed for postgraduate students from various disciplines, particularly those interested in public policy, social sciences, and the societal implications of AI.
Artificial intelligence requires access to data and computation. Both data and computational are material: they are produced by people, made possible by resource extraction, need power to survive, and both inhabit and resculpt the landscape. The use of AI, then, contributes to the climate crisis, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of AI, data, computation, and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented environmentalist practice in relation to AI.
This module is the lab programme for all first-year students enrolled on an AICE degree programme. A range of lab activities which tie into each of the first year modules are provided. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career. The module must be passed as a whole to progress onto year 2. Structurally, the AICE Part One Laboratory Programme is organized to cover all practical and laboratory based work in the first-year in a single timetable organized into central laboratory locations. There are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.
The module has been designed to impart the scientific knowledge required to tackle the many problems associated with air and other types of environmental pollution, including how to identify and assess the nature, sources and effects of pollutants, how to measure and monitor pollution, and how to remediate existing problems using technology and/or management strategies. For example, we have learned over the last 60 years that poor air quality can have damaging effects on both the living and non-living environment. Air pollution can degrade forests, lakes, crops, wildlife, buildings and other materials as well as having a detrimental effect upon human health. We have had to devise methods to accurately measure and monitor the air that we breathe, develop technology to clean up polluted air from the industries we have created, and institute strategies to ensure that the air remains fit for now and for the future. All this has to be done within a limited budget and using best available practice. This module focuses upon all of these issues. It aims to equip students with the scientific knowledge and skills to make a professional contribution to current and future debates about air and other environmental pollution, and to the practical steps which need to be taken – whatever they may be – to maintain and improve environmental quality. Students will use their knowledge and skills to complete assignments that will test the learning outcomes for the module. This module does not have any pre-requisites, but some background in chemistry and biology is preferred. Students will be required to perform mathematical activities.
The module has been designed to impart the scientific knowledge required to tackle the many problems associated with air and other types of environmental pollution, including how to identify and assess the nature, sources and effects of pollutants, how to measure and monitor pollution, and how to remediate existing problems using technology and/or management strategies. For example, we have learned over the last 60 years that poor air quality can have damaging effects on both the living and non-living environment. Air pollution can degrade forests, lakes, crops, wildlife, buildings and other materials as well as having a detrimental effect upon human health. We have had to devise methods to accurately measure and monitor the air that we breathe, develop technology to clean up polluted air from the industries we have created, and institute strategies to ensure that the air remains fit for now and for the future. All this has to be done within a limited budget and using best available practice. This module focuses upon all of these issues. It aims to equip students with the scientific knowledge and skills to make a professional contribution to current and future debates about air and other environmental pollution, and to the practical steps which need to be taken – whatever they may be – to maintain and improve environmental quality. Students will use their knowledge and skills to complete assignments that will test the learning outcomes for the module. This module does not have any pre-requisites, but some background in chemistry and biology is preferred Students will be required to perform mathematical activities
This module develops aerodynamic and thermodynamic methods for design of gas turbine engines. Starting from considerations of aircraft requirements and basic thermodynamics and fluid mechanics, students learn how the overall engine design can be tailored to achieve the required performance and develop a detailed understanding of turbomachinery design.
The module not only introduces the fundamental concepts of aircraft structural design but also provides the analytical and numerical tools to analyse complex aerospace systems within a multidisciplinary environment. Understanding and predicting the mutual interactions between different fields (aerodynamics, structural dynamics, etc.) is instrumental to successfully design any modern future air vehicles. With the subject matters covered in Part I and Part II as background knowledge, students will be taught how to closely interconnect previously separated disciplines.
Topology is concerned with the way in which geometric objects can be continuously deformed to one another. It can be thought of as a variation of geometry where there is a notion of points being "close together" but without there being a precise measure of their distance apart. Examples of topological objects are surfaces which we might imagine to be made of some infinitely malleable material. However much we try, we can never deform in a continuous way a torus (the surface of a bagel) into the surface of the sphere. Other kinds of topological objects are knots, i.e. closed loops in 3-dimensional space. Thus, a trefoil or "half hitch" knot can never be deformed into an unknotted piece of string. It's the business of topology to describe more precisely such phenomena. In topology, especially in algebraic topology, we tend to translate a geometrical, or better said a topological problem to an algebraic problem (more precisely, for example, to a group theoretical problem). Then we solve that algebraic problem and try to see what that solution tells us of our initial topological problem. So to do topology you need to work equally well with both geometric and algebraic objects.
This module: - Introduces the students to the key issues of interaction of multiple self-interested parties (a.k.a. agents) and gives a broad survey of topics at the interface of theoretical computer science and game theory dealing with such interactions. - Provides the theoretical background and practical tools to solve problems arising in settings with self-interested participants, to predict possible behaviour and outcomes, and finally, to design multi-agent systems that would incentivise desirable behaviour. - Introduces the students to the specifics of computational game-theoretic techniques in different application areas, ranging from multi-agent systems, electronic marketplaces and networked computer systems to computational biology and social networks. - Extends and advances the knowledge obtained in other AI modules (in particular, COMP6203 Intelligent Agents).
