An introduction to Elinor Glyn : her life and legacy

This special issue of Women: A Cultural Review re-evaluates an author who was once a household name, beloved by readers of romance, and whose films were distributed widely in Europe and the Americas. Elinor Glyn (1864–1943) was a British author of romantic fiction who went to Hollywood and became famous for her movies. She was a celebrity figure of the 1920s, and wrote constantly in Hearst's press. She wrote racy stories which were turned into films—most famously, Three Weeks (1924) and It (1927). These were viewed by the judiciary as scandalous, but by others—Hollywood and the Spanish Catholic Church—as acceptably conservative. Glyn has become a peripheral figure in histories of this period, marginalized in accounts of the youth-centred ‘flapper era’. Decades on, the idea of the ‘It Girl’ continues to have great pertinence in the post-feminist discourses of the twenty-first century. The 1910s and 1920s saw the development of intermodal networks between print, sound and screen cultures. This introduction to Glyn's life and legacy reviews the cross-disciplinary debate sparked by renewed interest in Glyn by film scholars and literary and feminist historians, and offers a range of views of Glyn's cultural and historical significance and areas for future research

Near to mid-term risks and opportunities of open-source generative AI

In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source Generative AI. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact