New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world’s smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawns.

Automatic plagiarism detection tools have evolved considerably in recent years. Owing in part to the recent technological developments, which provided more powerful processing capacities, as well as to the research interest that plagiarism detection attracted among computational linguists, results are nowadays more accurate and reliable. However, most of the plagiarism detection systems freely and commercially available are still based on similarity measures, whose algorithms search for similar or, at most, identical strings of text, within a more or less short search distance. Although these methods tend to perform well in detecting literal, verbatim plagiarism, their performance drops when other strategies are used, such as word substitution or reordering. This paper presents the results of a forensic linguistic analysis of real plagiarism cases among higher education students. Comparing the suspect plagiarised strings against the most likely originals from a legal perspective, it is demonstrated that strategies other than literal borrowing are increasingly used to plagiarise. A forensic linguistic explanation of the strategies used and why they represent instances of plagiarism is then offered, and examples are provided to illustrate why existing software fails to detect them. The paper concludes by arguing that commonly used detection software packages can be effective in identifying matching text, but are not necessarily good plagiarism detection systems. More indepth research and improvements in computational linguistics and natural language processing are required to increase the accuracy and reliability of the machinedetection procedure.