The revolutions in machine learning and artificial intelligence means much higher computational ability is required.

It combines real-time analytics, artificial intelligence, and machine learning to provide situational awareness with intelligence to accurately …

To create engagement, you have to have a story. In the metaverse, the creators will create the community, and the stories they tell will create the community, just like at the beginning of time. The king's storyteller kept people engaged, Shakespeare kept people engaged,

AI chatbot experts are all talking about -- and talking to -- a newish research project from artificial intelligence research organization OpenAI. Conceptually, ChatGPT can be used like the AI art tools in the sense that minimal text input by the user produces credible synthetic media -- paragraphs instead of images. In fact, it can write convincing, often compelling essays, stories and even poems. And, like the AI image creators, you can direct ChatGPT to write prose in specific styles. I told ChatGPT to tell me about Twitter in three separate queries: one in the style of Ernest Hemingway, another in the style of Mark Twain and the third in the form of a limerick.

Here's how it works: The ball, which has a sensor at the top, uses machine learning to track shooting.

… artificial general intelligence — machine intelligence that can solve problems … These specific learning technologies are self catalyzing; …

Amid swirling predictions of how an artificial intelligence chatbot could … ChatGPT is nothing more than a sophisticated machine-learning system …

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy

Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language. ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence deep neural network models. This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system. Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level. These data was used to train and compare pipeline and end-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.

This literature review identifies indicators that associate with higher impact or higher quality research from article text (e.g., titles, abstracts, lengths, cited references and readability) or metadata (e.g., the number of authors, international or domestic collaborations, journal impact factors and authors' h-index). This includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers. The literature review also includes evidence about the strength of association between bibliometric indicators and quality score rankings from previous UK Research Assessment Exercises (RAEs) and REFs in different subjects and years and similar evidence from other countries (e.g., Australia and Italy). In support of this, the document also surveys studies that used public datasets of citations, social media indictors or open review texts (e.g., Dimensions, OpenCitations, Altmetric.com and Publons) to help predict the scholarly impact of articles. The results of this part of the literature review were used to inform the experiments using machine learning to predict REF journal article quality scores, as reported in the AI experiments report for this project. The literature review also covers technology to automate editorial processes, to provide quality control for papers and reviewers' suggestions, to match reviewers with articles, and to automatically categorise journal articles into fields. Bias and transparency in technology assisted assessment are also discussed.