Journalists are at risk of being replaced by artificial intelligence systems like ChatGPT, the CEO of German media group Axel Springer has said. The announcement was made as the publisher sought to boost revenue at German newspapers Bild and Die Welt and transition to becoming a "purely digital media company". It said job cuts lay ahead, because automation and AI were increasingly making many of the jobs that supported the production of their journalism redundant. "Artificial intelligence has the potential to make independent journalism better than it ever was – or simply replace it," CEO Mathias Doepfner said in an internal letter to employees. AI tools like the popular ChatGPT promise a "revolution" in information, he said, and would soon be better at the "aggregation of information" than human journalists.

The development of machines that {\guillemotleft}talk like us{\guillemotright}, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI), since language is the quintessence of human intelligence. The brief but intense life of NLU research in AI and Natural Language Processing (NLP) is full of ups and downs, with periods of high hopes that the Grail is finally within reach, typically followed by phases of equally deep despair and disillusion. But never has the trust that we can build {\guillemotleft}talking machines{\guillemotright} been stronger than the one engendered by the last generation of NLU systems. But is it gold all that glitters in AI? do state-of-the-art systems possess something comparable to the human knowledge of language? Are we at the dawn of a new era, in which the Grail is finally closer to us? In fact, the latest achievements of AI systems have sparkled, or better renewed, an intense scientific debate on their true language understanding capabilities. Some defend the idea that, yes, we are on the right track, despite the limits that computational models still show. Others are instead radically skeptic and even dismissal: The present limits are not just contingent and temporary problems of NLU systems, but the sign of the intrinsic inadequacy of the epistemological and technological paradigm grounding them. This paper aims at contributing to such debate by carrying out a critical analysis of the linguistic abilities of the most recent NLU systems. I contend that they incorporate important aspects of the way language is learnt and processed by humans, but at the same time they lack key interpretive and inferential skills that it is unlikely they can attain unless they are integrated with structured knowledge and the ability to exploit it for language use.

Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing. Additionally, it is unclear if particular genres contribute to the emergence of inconsistency in recommendation performance across user groups. In this paper, we present an analysis of these three aspects of five well-known recommendation algorithms for user groups that differ in their preference for popular content. Additionally, we study how different genres affect the inconsistency of recommendation performance, and how this is aligned with the popularity of the genres. Using data from LastFm, MovieLens, and MyAnimeList, we present two key findings. First, we find that users with little interest in popular content receive the worst recommendation accuracy, and that this is aligned with miscalibration and popularity lift. Second, our experiments show that particular genres contribute to a different extent to the inconsistency of recommendation performance, especially in terms of miscalibration in the case of the MyAnimeList dataset.

Recommender System is an attractive field of research and development for many commercial applications. A typical recommender system recommends items to users using collaborative filtering [1, 2] based on a large amount of accumulated data from other users' choices. A major drawback of this approach is the so-called cold start problem [3]; when a target user has no history in order to identify his/her interests and preferences for the recommendation. Interaction with users can mitigate this problem by iteratively updating their interests and preferences. Natural language conversation is a promising way for interaction between users and recommender systems, especially for new under-experienced users. Conversational Recommender System (CRS) [4, 5] is a variant of such a recommender system. CRS recommends items to users according to their user portrait through conversation. The user portrait is a representation of user interests used for the recommendation [6]. Existing CRS studies [5, 6] represent a user portrait using a userdependent embedding vector and use it to choose appropriate items for recommen-Yuka Okuda Nara Institute of Science and Technology, Ikoma, Nara, Japan, e-mail: okuda.yuka.ou0@is.

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, we train Kosmos-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. We evaluate various settings, including zero-shot, few-shot, and multimodal chain-of-thought prompting, on a wide range of tasks without any gradient updates or finetuning. Experimental results show that Kosmos-1 achieves impressive performance on (i) language understanding, generation, and even OCR-free NLP (directly fed with document images), (ii) perception-language tasks, including multimodal dialogue, image captioning, visual question answering, and (iii) vision tasks, such as image recognition with descriptions (specifying classification via text instructions). We also show that MLLMs can benefit from cross-modal transfer, i.e., transfer knowledge from language to multimodal, and from multimodal to language. In addition, we introduce a dataset of Raven IQ test, which diagnoses the nonverbal reasoning capability of MLLMs.

This study develops FinText, a financial word embedding compiled from 15 years of business news archives. The results show that FinText produces substantially more accurate results than general word embeddings based on the gold-standard financial benchmark we introduced. In contrast to well-known econometric models, and over the sample period from 27 July 2007 to 27 January 2022 for 23 NASDAQ stocks, using stock-related news, our simple natural language processing model supported by different word embeddings improves realised volatility forecasts on high volatility days. This improvement in realised volatility forecasting performance switches to normal volatility days when general hot news is used. By utilising SHAP, an Explainable AI method, we also identify and classify key phrases in stock-related and general hot news that moved volatility.

With its uncanny ability to mimic human language and reasoning, ChatGPT seems to herald a revolution in artificial intelligence. The nimble chatbot can conjure poems and essays, share recipes, translate languages, dispense advice, and tell jokes, among the endless applications users have tested since the Silicon Valley research lab OpenAI released the natural language-processing tool in November. With the excitement comes some trepidation--that the technology could degrade authentic human writing and critical thinking, upend industries, and amplify our own prejudices and biases. Experts from across the university convene at 1 p.m. EST to discuss the latest developments in AI, including language learning programs such as ChatGPT, disinformation campaigns, and ethical concerns. To those working in artificial intelligence, ChatGPT is not merely an overnight sensation, but a mark of achievement after years of experimentation, says Johns Hopkins assistant computer science professor Daniel Khashabi, who specializes in language processing and has worked on similar tools.

Microsoft's new A.I.-enhanced Bing search engine has gotten a lot of press over the past few weeks for being combative, rude, and just creepy. The chatbot told Kevin Roose of the New York Times that it loved him and that, although he's married, Kevin doesn't actually love his spouse. It told a Washington Post reporter that it can "feel or think things." On Twitter, a lot of the folks with early access to the chatbot (it's in a test stage and is not yet available to the public) posted screenshots of chilling conversations. Faced with the potential threat of runaway A.I., for years researchers have been working on safety guardrails, out of the public eye, hoping they could keep these systems functioning the way they're supposed to.

Artificial Intelligence (AI) has taken over our imagination in recent months, with Large Language Models, in particular, being touted as a ground-breaking development. However, as we move beyond the hype, the true capabilities and limitations of the technology are beginning to emerge. It is hard to deny, the recent developments in Artificial Intelligence (AI) that have been released are beginning to highlight to the masses the potential of AI in both our personal and professional lives. Over the past several weeks, people have revelled in the capabilities of chatbots, such as ChatGPT, even declaring that it could replace certain roles, such as those associated with customer care, media (advertising, content creation and technical writing) and research. However, over time, as more questions have been thrown as these chatbots and people actively test their limits, several cracks have begun to emerge, as few of which we outline below. As a result, people are beginning to ask more questions about the deficiencies and limitations of AI, especially the Large Language Models (LLM), such as ChatGPT, which to some degree, was being marketed as "the best thing since sliced bread" and being able to transform life as we know it.

With artificial intelligence (AI) currently a hot topic in creative circles, it's easy to wonder if the threat to photography is real. If this is something that alarms or fascinates you (or both), we're sure you'd also like to know what your fellow photographers think. In his channel The Art of Photography, Ted Forbes usually talked about AI, but only in the context of image editing. This time, he shared his insights on computer-generated imaging itself, and if there's a legitimate threat to the craft. He asks, "Will this kill photography as we know it?"