In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages. Existing speech technologies may be inefficient in extracting information from such conversations, where the speech data is rich in diversity with multiple languages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage in Conversational Environments) challenge constitutes an open-call for evaluating and bench-marking the speaker and language diarization technologies on this challenging condition. The challenge entailed two tracks: Track-1 focused on speaker diarization (SD) in multilingual situations while, Track-2 addressed the language diarization (LD) in a multi-speaker scenario. Both the tracks were evaluated using the same underlying audio data. To facilitate this evaluation, a real-world dataset featuring multilingual, multi-speaker conversational far-field speech was recorded and distributed. Furthermore, a baseline system was made available for both SD and LD task which mimicked the state-of-art in these tasks. The challenge garnered a total of $42$ world-wide registrations and received a total of $19$ combined submissions for Track-1 and Track-2. This paper describes the challenge, details of the datasets, tasks, and the baseline system. Additionally, the paper provides a concise overview of the submitted systems in both tracks, with an emphasis given to the top performing systems. The paper also presents insights and future perspectives for SD and LD tasks, focusing on the key challenges that the systems need to overcome before wide-spread commercial deployment on such conversations.

On January 1, Mike Neville gave Midjourney the following prompt: "Steamboat Willie drawn in a vintage Disney style, black and white. He is dripping all over with white gel." There's no polite way to describe what this prompt conjured from the AI image generator. It looks, very much, like Mickey Mouse is drenched in ejaculate. At the start of every year, a crop of cultural works enters the public domain in the United States.

Artificial intelligence had its break out year in 2023. We chose those two language models because they use live information from the internet to make their predictions, unlike ChatGPT and Microsoft's Bing which rely on older data. AGI is a theoretical intelligent agent able to complete any intellectual task a human can - and the arrival of AGI is forecast to cause huge changes to human society. Backed by Google and Amazon, Claude's parent company Anthropic was founded by former members of OpenAI, makers of ChatGPT. 'In recent years we've seen AI algorithms match or exceed human performance in specialized tasks like object recognition, game playing, and language processing.

Then the poetry people respond--often a little late, in need of haircuts--with earnest arguments about the value of art. I am an English major to death. But I learned years ago that there's no benefit in joining this debate. The scientist-novelist C. P. Snow went after the subject in 1959 in a lecture called "The Two Cultures," in which he criticized British society for favoring Shakespeare over Newton. Snow gets cited a lot.

Large language models like ChatGPT write impressively well--so well, in fact, that they've become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual errors, so wary readers may want to know if generative AI tools have been used to ghostwrite news articles or other sources before trusting them. What can teachers and consumers do? Existing tools to detect AI-generated text sometimes do poorly on data that differs from what they were trained on.

If you've worried that candidate-screening algorithms could be standing between you and your dream job, reading Hilke Schellmann's The Algorithm won't ease your mind. The investigative reporter and NYU journalism professor's new book demystifies how HR departments use automation software that not only propagate bias, but fail at the thing they claim to do: find the best candidate for the job. Schellmann posed as a prospective job hunter to test some of this software, which ranges from résumé screeners and video-game-based tests to personality assessments that analyze facial expressions, vocal intonations, and social media behavior. One tool rated her as a high match for a job even though she spoke nonsense to it in German. A personality assessment algorithm gave her high marks for "steadiness" based on her Twitter use and a low rating based on her LinkedIn profile. It's enough to make you want to delete your LinkedIn account and embrace homesteading, but Schellmann has uplifting insights too.

Partway through 2023, I caught up with a respected, high-ranking tech writer at another publication. We gossiped and nattered, and, a bit exasperated, empathised with each other: we were run ragged. The last two years have raised the stakes for what tech journalists do from serving a small niche community to covering stories that have an impact on the wider world. It's also down to the characters involved and what's at stake. Tech journalists have lived on fast-forward since Elon Musk first lodged his bid to take over Twitter – now X – in April 2022.

The game of Othello is one of the world's most complex and popular games that has yet to be computationally solved. Othello has roughly ten octodecillion (10 to the 58th power) possible game records and ten octillion (10 to the 28th power) possible game positions. The challenge of solving Othello, determining the outcome of a game with no mistake made by either player, has long been a grand challenge in computer science. This paper announces a significant milestone: Othello is now solved. It is computationally proved that perfect play by both players lead to a draw. Strong Othello software has long been built using heuristically designed search techniques. Solving a game provides a solution that enables the software to play the game perfectly.

Despite increasing interest in the automatic detection of media frames in NLP, the problem is typically simplified as single-label classification and adopts a topic-like view on frames, evading modelling the broader document-level narrative. In this work, we revisit a widely used conceptualization of framing from the communication sciences which explicitly captures elements of narratives, including conflict and its resolution, and integrate it with the narrative framing of key entities in the story as heroes, victims or villains. We adapt an effective annotation paradigm that breaks a complex annotation task into a series of simpler binary questions, and present an annotated data set of English news articles, and a case study on the framing of climate change in articles from news outlets across the political spectrum. Finally, we explore automatic multi-label prediction of our frames with supervised and semi-supervised approaches, and present a novel retrieval-based method which is both effective and transparent in its predictions. We conclude with a discussion of opportunities and challenges for future work on document-level models of narrative framing.

Misinformation poses a variety of risks, such as undermining public trust and distorting factual discourse. Large Language Models (LLMs) like GPT-4 have been shown effective in mitigating misinformation, particularly in handling statements where enough context is provided. However, they struggle to assess ambiguous or context-deficient statements accurately. This work introduces a new method to resolve uncertainty in such statements. We propose a framework to categorize missing information and publish category labels for the LIAR-New dataset, which is adaptable to cross-domain content with missing information. We then leverage this framework to generate effective user queries for missing context. Compared to baselines, our method improves the rate at which generated questions are answerable by the user by 38 percentage points and classification performance by over 10 percentage points macro F1. Thus, this approach may provide a valuable component for future misinformation mitigation pipelines.