Documents to download

The next technological generation of television (High-Definition Television – HDTV) is being pursued in Japan, USA and Europe. The European collaborative effort envisages the use of a pan- European technical standard whose viability is under question due to the merger of SkyTV and BSB, and other factors.

This POSTnote examines HDTV development worldwide and issues related to the future of this technology in Europe.


Documents to download

Related posts

  • Devices with screens include game consoles, laptops and televisions. Screen use refers to activities undertaken on such devices and the time spent on them. Children’s screen use has increased over the past decade. Policy-makers and parents have expressed concerns about possible effects of screen use on children/young people’s development and health. This POSTnote provides an overview of how children/young people use screens, the opportunities and risks of this use, evidence on the possible effects on health and development, and evidence on ways to support healthy screen use.

  • 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.

  • 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.