The National Book Foundation has dropped Drew Barrymore as the host of its upcoming awards ceremony after the actress resumed production on her talk show, crossing the picket lines of the ongoing writers' strike. On Monday, she confirmed that the Drew Barrymore Show will begin filming its newest season without writers on staff--prompting criticism from several novelists about Barrymore's involvement in the awards show. "The National Book Awards is an evening dedicated to celebrating the power of literature, and the incomparable contributions of writers to our culture," the National Book Foundation said in a statement Tuesday, according to NPR. "In light of the announcement that'The Drew Barrymore Show' will resume production, the National Book Foundation has rescinded Ms. Barrymore's invitation to host the 74th National Book Awards Ceremony." For more than four months, the Writers' Guild of America, in a historic double strike with unionized actors, has been fighting for better working conditions, including increased pay and contractual protections related to the use of artificial intelligence. While initially Barrymore was supportive of the writers' strike, even declining to host an MTV award show in solidarity, the actress has seemingly pulled a full 180, saying in a statement that she owns "this choice" and is in "compliance with not discussing or promoting film and television that is struck of any kind."

Tnirte, Morocco – Abdel Abed is watching the other villagers digging. When one of them gets tired, he scrambles down and takes over. It has been five days since the magnitude 6.8 earthquake ripped through the mountainous regions around Marrakesh, Morocco, and Abed's daughter, nine-year-old Shaima, is still buried under the rocks. Abed still hopes she may be alive, a family member explains, and he works with almost robotic energy as excavation efforts continue in Tnirte in the High Atlas Mountains. His wife was pulled dead from the rocks yesterday.

While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research. The recent proliferation of Large Language Models (LLMs) compels one to ask: how capable are these systems in generating code-mixed data? In this paper, we explore prompting multilingual LLMs in a zero-shot manner to generate code-mixed data for seven languages in South East Asia (SEA), namely Indonesian, Malay, Chinese, Tagalog, Vietnamese, Tamil, and Singlish. We find that publicly available multilingual instruction-tuned models such as BLOOMZ and Flan-T5-XXL are incapable of producing texts with phrases or clauses from different languages. ChatGPT exhibits inconsistent capabilities in generating code-mixed texts, wherein its performance varies depending on the prompt template and language pairing. For instance, ChatGPT generates fluent and natural Singlish texts (an English-based creole spoken in Singapore), but for English-Tamil language pair, the system mostly produces grammatically incorrect or semantically meaningless utterances. Furthermore, it may erroneously introduce languages not specified in the prompt. Based on our investigation, existing multilingual LLMs exhibit a wide range of proficiency in code-mixed data generation for SEA languages. As such, we advise against using LLMs in this context without extensive human checks.

Elon Musk is "wired for war." At least, that's what Musk has told Walter Isaacson, whose thick biography of the mercurial mega-billionaire, Elon Musk, is out this week. When Musk says this, he's not talking about Ukraine, where his Starlink internet service has played a central role. Civilization, Warcraft: Orcs & Humans, The Battle of Polytopia, Elden Ring--Musk has spent much of his life in fantasy worlds. Isaacson's biography includes many astonishing details and relatively few pages focused on Musk's gaming obsession. But the video-game detail is telling. Musk doesn't seem to inhabit our reality, exactly, even as he profoundly shapes it.

In 2021, Elon Musk became the world's richest man (no woman came close), and Time named him Person of the Year: "This is the man who aspires to save our planet and get us a new one to inhabit: clown, genius, edgelord, visionary, industrialist, showman, cad; a madcap hybrid of Thomas Edison, P. T. Barnum, Andrew Carnegie and Watchmen's Doctor Manhattan, the brooding, blue-skinned man-god who invents electric cars and moves to Mars." Right about when Time was preparing that giddy announcement, three women whose ovaries and uteruses were involved in passing down the madcap man-god's genes were in the maternity ward of a hospital in Austin. Musk believes a declining birth rate is a threat to civilization and, with his trademark tirelessness, is doing his visionary edgelord best to ward off that threat. Shivon Zilis, a thirty-five-year-old venture capitalist and executive at Musk's company Neuralink, was pregnant with twins, conceived with Musk by in-vitro fertilization, and was experiencing complications. "He really wants smart people to have kids, so he encouraged me to," Zilis said.

Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains, such as recruitment. Within recruitment, AI has become commonplace, mainly in the form of job recommender systems (JRSs), which try to match candidates to vacancies, and vice versa. However, common XAI techniques often fall short in this domain due to the different levels and types of expertise of the individuals involved, making explanations difficult to generalize. To determine the explanation preferences of the different stakeholder types - candidates, recruiters, and companies - we created and validated a semi-structured interview guide. Using grounded theory, we structurally analyzed the results of these interviews and found that different stakeholder types indeed have strongly differing explanation preferences. Candidates indicated a preference for brief, textual explanations that allow them to quickly judge potential matches. On the other hand, hiring managers preferred visual graph-based explanations that provide a more technical and comprehensive overview at a glance. Recruiters found more exhaustive textual explanations preferable, as those provided them with more talking points to convince both parties of the match. Based on these findings, we describe guidelines on how to design an explanation interface that fulfills the requirements of all three stakeholder types. Furthermore, we provide the validated interview guide, which can assist future research in determining the explanation preferences of different stakeholder types.

The paper considers the possibility of fine-tuning Llama 2 large language model (LLM) for the disinformation analysis and fake news detection. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fine-tuned for the following tasks: analysing a text on revealing disinformation and propaganda narratives, fact checking, fake news detection, manipulation analytics, extracting named entities with their sentiments. The obtained results show that the fine-tuned Llama 2 model can perform a deep analysis of texts and reveal complex styles and narratives. Extracted sentiments for named entities can be considered as predictive features in supervised machine learning models.

This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact. Through literature reviews, case studies, surveys, and text analysis, it examines art and technology applications in rural contexts and identifies key challenges. The study finds artworks often fail to resonate locally, while reliance on external artists limits sustainability. Hence, nurturing grassroots "artist villagers" through AI is proposed. Our approach involves training machine learning on subjective aesthetics to generate culturally relevant content. Interactive AI media can also boost tourism while preserving heritage. This pioneering research puts forth original perspectives on the intersection of AI and aesthetics to invigorate rural culture. It advocates holistic integration of technology and emphasizes AI's potential as a creative enabler versus replacement. Ultimately, it lays the groundwork for further exploration of leveraging AI innovations to empower rural communities. This timely study contributes to growing interest in emerging technologies to address critical issues facing rural China.

We present the InterviewBot that dynamically integrates conversation history and customized topics into a coherent embedding space to conduct 10 mins hybrid-domain (open and closed) conversations with foreign students applying to U.S. colleges for assessing their academic and cultural readiness. To build a neural-based end-to-end dialogue model, 7,361 audio recordings of human-to-human interviews are automatically transcribed, where 440 are manually corrected for finetuning and evaluation. To overcome the input/output size limit of a transformer-based encoder-decoder model, two new methods are proposed, context attention and topic storing, allowing the model to make relevant and consistent interactions. Our final model is tested both statistically by comparing its responses to the interview data and dynamically by inviting professional interviewers and various students to interact with it in real-time, finding it highly satisfactory in fluency and context awareness.

Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 49 sub-themes on professionals' perception, adoption and use of TTIG systems in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG to benefit people and games as cultural artefacts.