POST has published 20 COVID-19 Areas of Research Interest (ARIs) for the UK Parliament. ARIs were identified using the input of over 1,000 experts. They were then ranked in order of interest to UK Parliament research and select committee staff, following internal feedback. Each ARI comes with a series of questions aiming to further break down each broad area. The ARIs focus on the impacts of the global pandemic and range from economic recovery and growth, to surveillance and data collection, long-term mental health effects, education, vaccine development, and the NHS.
Documents to download
Edge computing (228 KB, PDF)
Edge computing describes the use of computing resources (including data storage and processing) located close to the devices which generate the data, rather than relaying this data to a remote cloud computer to perform computations. It is suited to applications that require quick computation and use large volumes of data, which would require high bandwidth for transfer to the cloud. It may also offer some privacy benefits as more data can be processed locally rather than being sent to a cloud. However, the distributed nature of edge computing, and the need for many individual devices to connect and interact, poses a number of unique challenges to its widespread adoption.
- Edge computing can enable fast data processing and reduces the need to transfer large amounts of data across the network, it can also enhance privacy and enable computation to continue if the network connection is lost.
- Edge computing is considered to be an emerging technology that has not yet reached maturity.
- For some applications, edge computing offers advantages over cloud computing or vice versa. However, for other applications a flexible approach that uses both cloud and edge is the most appropriate.
- Edge computing is expected to contribute to many emerging technologies including smart manufacturing, autonomous vehicles and the increased uptake of smart internet-connected devices.
- It may be challenging to coordinate data exchange and share computational tasks between many different devices on an edge computing network.
- New security technologies are being developed to secure edge computing networks that contain many devices without using too many computational resources.
- Although edge computing can offer privacy advantages, it may become increasingly difficult to determine who has the responsibility for complying with data protection law and whether they are equipped to comply as uptake by consumers becomes more widespread.
POSTnotes are based on literature reviews and interviews with a range of stakeholders and are externally peer reviewed. POST would like to thank interviewees and peer reviewers for kindly giving up their time during the preparation of this briefing, including:
- Dr Bill Mitchell OBE, BCS
- Professor Carsten Maple, Warwick Manufacturing Group*
- Professor Dimitra Simeonidou, University of Bristol
- Glen Robinson, Microsoft*
- James Lovegrove, Red Hat*
- Joanna Hodgson, Red Hat*
- Adrian Keward, Red Hat*
- Jonathan Legh-Smith, BT*
- Bryan Stiekes, Google Cloud*
- Ksenia Duxfield-Karyakina, Google Cloud*
- Martin Percival, Red Hat*
- Professor Lilian Edwards, Newcastle University
- Neil Stansfield, NPL
- Nicky Stewart, UKCloud*
- Paul Duncan, NPL*
- Paul Martynenko, POST Board*
- Professor Paul Watson, Newcastle University*
- Professor Reza Nejabati, University of Bristol*
- Rebecca Lucas, RUSI
- Rhiannon Lawson, Government Digital Service
- Richard Ward, IBM*
- Simon Hansford, UKCloud*
- Sneha Dawda, RUSI*
- Dr Stephen Pattison, ARM*
- Tom March, Government Digital Service*
*denotes people and organisations who acted as external reviewers of the briefing.
Documents to download
Edge computing (228 KB, PDF)
Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview of some of the proposed tools for making ML systems more accountable.
Over 350 experts have shared with us what they think the implications of the COVID-19 pandemic will be in the next 2 to 5 years. This work was done to inform the House of Lords COVID-19 Committee inquiry on Life beyond COVID, and is based on 366 expert responses. Areas of concern include work and employment, health and social care, research and development, society and community, the natural environment, education, arts, culture and sport, infrastructure and crime and justice.