Artificial intelligence
Research Topic
Language: English
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Research problems linked to this topic
- What new and emerging technologies (including cloud, Artificial Intelligence, Machine Learning, and Augmented Reality/Virtual Reality) will impact geospatial skills and innovation, and access to geospatial data in the future, and how could the UK leverage these technologies?
- What are the opportunities of emerging technologies (quantum and AI) to revolutionise our ability to map underground assets?
- Changing world: How have evolutions in our statistical system (such as the greater focus on administrative sources for statistics) influenced how statistics are produced, used, and valued? How may advances in wider society (such as the increasing sophistication of large language models) influence how statistics are produced, used, and valued?
- What resources are required to ensure the safe and efficient handling of data in education settings?
- What risk is there that generative AI evolves such that the content it generates can avoid detection faster than tools can be developed to detect it? How can international and industry collaboration limit this risk?
- How will the use of generative AI to create ‘deepfakes’ that manipulate people’s likeness (face, body, voice) evolve? What is the psychological impact of being deepfaked, and what harmful uses (e.g. intimate image abuse, fraud, reputational damage) will develop and increase?
- Which harmful online uses of AI are likely to increase? What could be the impact of AI-generated content on attitudes, beliefs, behaviours or psychological wellbeing?
- One expected impact of AI will be on trust in information. How might AI reduce public trust in information available online? Do UK citizens trust AI-generated online content?
- In what ways will AI exacerbate the spread of mis/disinformation and is mis/disinformation spread by AI likely to be more effective in influencing UK audiences?
- AI will democratise access to capabilities that used to be expensive or hard to access, and create new capabilities that didn’t previously exist. As barriers (e.g. technical skills, access to specialist equipment) are reduced, AI use will increase. What is the prevalence of AI generated content online?
- What is the expected growth of the UK's maritime autonomy and remote operations sector and what impact will this technical change have on the workforce?
- How do we account for AI-first assumptions/errors (that humans would not make)?
- How can we better understand novel uses or applications of AI in the geospatial ecosystem, such as in the analysis of Earth Observation and Population Movement data, 3D visualisation, and climate modelling?
- How can front-line public services adapt to the challenges and opportunities presented by automation? What are the benefits and risks of adopting new technologies to deliver services?
- How can AI be used to identify harmful content?
- In which channels is harmful Gen-AI content most prevalent, how does it spread, and how can friction be introduced to these channels?
- How will AI affect existing kinds of harmful online content (e.g. online abuse, scams) and what new kinds of online harmful content might it give rise to?
- Which areas of research on the uses of next generation networks may need policy interventions (e.g. Internet of Things and Artificial Intelligence)? This may include driving strong take-up of fibre and 5G/6G, encouraging the adoption of the products and services, and increasing Willingness to Pay and supporting industry to make the necessary investments?
- Evaluate the technologies that will drive smart networks: evidencing the utilisation of the UK’s lead in AI and Edge technology to develop self-organising, secure and highly optimised network software.
- Evaluate the technologies that will drive clean networks/power efficiency: harnessing semiconductors and AI to drive more efficient telecom radios and network optimisation.
- How might AI contribute to future spectrum regulation/management?
- Does the cyber security of AI models need to follow any novel principles that aren’t set out under existing policy and technology security principles? If so, what are these measures and how do the differ from what exists? How do the vulnerabilities/risk of AI model security differ from existing cyber threats?
- How might automation, machine learning/AI change the way in which cybersecurity services are currently delivered? Do these changes lead to a reduction or even an increase in demand for cyber security skills, products and services
- How might Artificial Intelligence tools such as language models be useful in:i) processing delivery data ii) increasing understanding of geospatial areas and associated risks of delivery?
- Detailed data on companies that specialise in the provision of AI services in life sciences.
- How can we apply the defence in depth approach to preparing for risks from AI?
- What human systems are resilient to impacts from AI and which are less so?
- Which data sources or key indicators should we be watching that may indicate major changes in the risk assessment, or new risks hitherto unidentified arising?
- How can we reduce bias when using AI?
- Detailed company data on both current expenditure and investment in AI.
- What risk assessment methods are best suited to risks from AI?
- How can we ensure use of AI is ethical?
- How can we ensure AI increases public sector productivity?
- Which risks from AI are the most urgent to mitigate?
- How can we attribute the role that AI had in causing a particular harm, rather than something else?
- What are the possible scenarios for various AI risks 1, 3, 5, 10 and more years from now?
- What is needed to enable the public sector to adopt AI?
- Will adoption of AI in key delivery departments contribute to more efficient and effective public services?
- Will investment in AI lead to reduced public sector costs in the long run?
- Agile and responsive skills system: What changes in the skills system are required to meet government ambitions to support and grow critical technologies set out in the Science and Technology Framework? How can we future proof the workforce by giving them the right skills to fully embrace AI and its potential?
- Compute: What are the latent needs of the UK’s AI ecosystem for compute resource?
- Skills: How do we make sure the right skills are available to maintain a world-leading position in AI? And how do we ensure the labour force has the right skills to support individual opportunities?
- "R&D : In which areas of AI R&D is the UK strongest? What are the most significant AI R&D opportunities for the UK? Which government interventions are most effective for boosting UK AI R&D (relative to such goals as economic growth, productivity and security)?"
- To what extent are the potential risks posed by highly capable AI systems a barrier to economy-wide adoption, and how could progress in AI safety overcome these barriers? How can we ensure that the UK population has the right AI skills for life and work?
- How can we ensure public attitudes to AI are positive, and maximise trust in safe AI?
- How can we better understand the barriers to AI adoption?
- How should the UK position itself in terms of the global AI market? What sort of AI businesses should we particularly be looking to attract?
- Micro productivity: To what extent does AI impact firm level productivity?
- Productivity: What are the possible direct and indirect productivity impacts of AI?
- Sectors: Which sectors are more likely to benefit from AI and which are more at risk from the downsides?
- Occupations: What are the characteristics of occupations that put them more at risk of replacement/change or give them a comparative advantage? Over what time frame will they be impacted?
- How can AI and other emerging technologies be implemented in education settings so that they do not widen existing inequalities or create new inequalities?
- What are the most effective approaches to upskilling the education workforce to use AI well? What impact could this have on productivity?
- How will AI impact societal outcomes, especially regarding inequality, health and the environment?
- How will AI impact competition and innovation?
- What will the future of AI look like within the UK, and how can we monitor our progress towards the many possible scenarios?
- Quality or quantity: Does AI enable the delivery of better outputs and increased quality from firms and businesses?
- Macro productivity: To what extent does AI impact national productivity?
- Wages: What would be the impact of AI deployment at work on wages and costs for employees and employers?
- Indicators: What are the rapid indicators of AI impact on the labour market?
- What models of management and professional development of teaching and nonteaching roles support efficient and safe use of data and technology including AI?
- What approaches or innovation are needed to support the efficient handling of data within education settings?
- How can we adapt research methodologies to robustly measure the impact of technology in education, given its fast-moving nature?
- How can the impact of digital technology be robustly measured, and implemented in a way that supports teachers and learners?
- How has the increased accessibility of generative AI influenced HE and FE providers and students?
- What are the best ways to ensure that AI is used safely, ethically, and in ways that protect the data and interests of children, young people, teachers, and schools and colleges? What forms of regulation and enforcement may be appropriate?
- How do AI and other digital technologies support existing ways of working in schools and colleges? What are the main opportunities for the future?
- What are the potential impacts of AI, and how can new technologies be used safely and effectively within education?
- What are the potential long-term opportunities and challenges of AI use in education at all stages?
- What is the stock of skills in the economy, where are there mismatches between need and availability? Where will the greatest skills needs be across the medium (5-10 years) and long-term (10 to 20+ years)? What impacts might we expect AI to have on future skills needs?
- What scope does data analytics and AI have to tailor services to claimants’ needs? What are the benefits and risks of digital services? What is the effectiveness of digital transformation in driving efficiency and improving satisfaction?
- What is required to identify implications for worker health and safety from AI, ensuring it is used safely throughout its lifecycle, including consideration of the interaction between digital and physical systems?
- How will future changes in technology, and the way in which workers interact with these new technologies (e.g. Artificial Intelligence, Net Zero technologies), affect the health of workers and what can be done to mitigate any work-related ill health?
- In security applications, how can we rely on AI to show us all the possible threats (not seeing what we are not shown)?
- How can new approaches and technologies be applied to deter, detect, and disrupt the misuse of drones?
- How do we design public-serving autonomous systems to be fair and inclusive?
- What training needs to be delivered to interact and challenge meaningfully AI algorithms? How can we prevent skills deterioration?
- What are the public needs for explanability in AI?
- What frameworks can we use to ensure proportional, safe, and trusted applications of AI in the transport sector?
- What are the key factors regarding public trust on autonomous systems?
- What are the human-machine interface (HMI) requirements for AI applications such as machine vision? How can we limit overreliance?
- How can we ensure Situational Awareness for different human in the loop actors in autonomous aviation?
- Data science, machine learning, and artificial intelligence: algorithmic decision-support and decision-making, to inform the real-time personalisation of services and interventions; natural language processing, feature extraction and analysis of complex textual data; artificial intelligence transparency, accountability, fairness, and ‘explainability’.
- Data Science and Decisions - How can MOD harness the benefits of data science? How do we build trust in automated systems? How do we integrate multiple sources of information with differing levels of uncertainty and represent this effectively and efficiently to busy decision makers?
- Reducing Cognitive Load - Greater access to data, information and services will challenge the cognitive loading on personnel. How can new technology help reduce this burden through, for example, autonomous software agents? Which functions could be carried out by machine and, conversely, what decisions will still need to be taken by human operators to ensure compliance with legal and ethical standards? How do we integrate these advances into our command and control systems?
- S&T Skills – The complex challenges faced by Defence requires a multidisciplinary approach. However of particular interest is access to those specialist skills relating to nuclear, energetics and explosives, autonomy, aerodynamics, big data and cyber. There will a continuing demand for systems engineering skills to harness and integrate technology for military advantage. Within Defence there are opportunities for engineering and scientific apprenticeships, industrial placements and summer student placements which all provide the opportunity to work on challenging but exciting problems.
- Allocating resources optimally, including using data science effectively (for example to improve targeting).
- Using biometrics, digital and behavioural aspects to assure identity and to understand and mitigate the possible deception of systems.
- Recovering fingerprints from various materials; automatically processing fingerprints; getting additional information (not just the image itself) from fingerprints.
- Risks and effectiveness of counter-UAV measures in civilian airspace.
- How can artificial intelligence, machine learning, simulation, agent based modelling and other leading data science techniques contribute to better understanding of trade and investment patterns?
- How can we develop and exploit new methodologies to ensure cost-effective monitoring (for example remote sensing and environmental DNA)?
- Data science: Application of techniques, including AI and block chain, to unlock opportunities for improved and more efficient environmental monitoring, regulatory compliance, and land management
- Development of models to support decision making on complex and wicked problems (for example on land use, environmental trade-offs, food systems)
- How should government maintain trust and accountability in using AI and machine learning? What is the public appetite for government making use of these in decision making?
- What innovative approaches to data in education could increase staff capacity and reduce workload?
- What are the most robust methodologies for assessing the effectiveness of technology used for education?
- In what ways can AI and other digital technology reduce teacher workload and improve student outcomes? How can AI and digital technology impact on productivity?
- How should autonomous aviation systems communicate with ground control and each other?
- What is substantial change to trigger re-evaluation of AI?
- Can we have AI learn “on the job” safely? For example, in machine vision applications.
- How can we use digital twins to increase resilience, responsiveness, and integration of our network (cross modally)?
- What is the role of remote operation (assistance, decision making & control)? What are the skills and requirements for such operation for autonomous systems?
- Trust and accountability in using AI and machine learning within policing
- Autonomous systems – What are the ways Defence could exploit autonomy and autonomous systems in military operations and potentially at lower cost than traditional high-end military platforms? What is the range of potential benefits of utilising autonomous systems in Defence; greater areas covered, persistent effects, or removing personnel from immediate danger? How can we reduce the need for human involvement in difficult, mundane and dangerous tasks such as bomb disposal, force protection or decontamination?
- Developing assurances and standards for technology designed to safely and effectively respond to instances of malicious, illegal use of autonomous and unmanned systems across sectors.
- How can we combine available and emergent information technologies (such as text analytics, natural language processing, sentiment analysis and semantic markup) to improve knowledge and information sharing in a public sector context?
- What are the risks and opportunities to creative business growth posed by new technology, including automation such as the use of AI?
- Approaches to parsing images automatically.
- How can effective accountability and governance through complex AI supply chains be achieved? How can joined-up approaches with AI/digital experts in industry and academia be encouraged to develop, share knowledge and resources in ways that leverage synergies and efficiencies? e.g., sandboxes and incorporate learning from international contexts?
- A changing world of work: Are there significant implications for worker health and safety and for building safety of widespread adoption across this diverse sector of: Modern Methods of Construction (MMC); AI, Autonomy and the Internet of Things; Robotics; Advanced materials and additive manufacturing?
- How might technology shape the future requirements of, and services offered by, the legal profession and sector? How can LawTech and innovation support greater access to justice?
- Ubiquitous sensing and processing - in the future, sensors will become smaller and cheaper leading to their wide availability both in civilian applications and in defence. They will also be available to our adversaries. How they are deployed and how the information they generate is managed and used will be key. We need to understand how they will be networked and how automation could be exploited to task and manage them.