An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. A robot walks into a bar: can a Melbourne researcher get AI to do comedy? Robots can make humans laugh - mostly when they fall over - but a new research project is looking at whether robots using AI could ever be genuinely funny. If you ask ChatGPT for a funny joke, it will serve you up something that belongs in a Christmas cracker: "Why don't skeletons fight each other? Because they don't have the guts."

The discovery of fast and variable coherent signals in a handful of ultraluminous X-ray sources (ULXs) testifies to the presence of super-Eddington accreting neutron stars, and drastically changed the understanding of the ULX class. Our capability of discovering pulsations in ULXs is limited, among others, by poor statistics. However, catalogues and archives of high-energy missions contain information which can be used to identify new candidate pulsating ULXs (PULXs). The goal of this research is to single out candidate PULXs among those ULXs which have not shown pulsations due to an unfavourable combination of factors. We applied an AI approach to an updated database of ULXs detected by XMM-Newton. We first used an unsupervised clustering algorithm to sort out sources with similar characteristics into two clusters. Then, the sample of known PULX observations has been used to set the separation threshold between the two clusters and to identify the one containing the new candidate PULXs. We found that only a few criteria are needed to assign the membership of an observation to one of the two clusters. The cluster of new candidate PULXs counts 85 unique sources for 355 observations, with $\sim$85% of these new candidates having multiple observations. A preliminary timing analysis found no new pulsations for these candidates. This work presents a sample of new candidate PULXs observed by XMM-Newton, the properties of which are similar (in a multi-dimensional phase space) to those of the known PULXs, despite the absence of pulsations in their light curves. While this result is a clear example of the predictive power of AI-based methods, it also highlights the need for high-statistics observational data to reveal coherent signals from the sources in this sample and thus validate the robustness of the approach.

The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterarguments, we use them to question arguments, without requiring any external knowledge. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation. Thus, in this work we investigate two complementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton's argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task.

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs.

When Ty landed an introductory phone interview with a finance and banking company last month, they assumed it would be a quick chat with a recruiter. And when they got on the phone, Ty assumed the recruiter, who introduced herself as Jaime, was human. "The voice sounded similar to Siri," said Ty, who is 29 and lives in the DC metro area. Ty realized they weren't speaking to a living, breathing person. Their interviewer was an AI system, and one with a rather rude habit.

Before OpenAI's ChatGPT emerged and captured the world's attention for its ability to create compelling sentences, a small startup called Latitude was wowing consumers with its AI Dungeon game that let them use artificial intelligence to create fantastical tales based on their prompts. But as AI Dungeon became more popular, Latitude CEO Nick Walton recalled that the cost to maintain the text-based role-playing game began to skyrocket. AI Dungeon's text-generation software was powered by the GPT language technology offered by the Microsoft-backed AI research lab OpenAI. The more people played AI Dungeon, the bigger the bill Latitude had to pay OpenAI. Compounding the predicament was that Walton also discovered content marketers were using AI Dungeon to generate promotional copy, a use for AI Dungeon that his team never foresaw, but that ended up adding to the company's AI bill.

The recent evolution in Natural Language Processing (NLP) methods, in particular in the field of argumentation mining, has the potential to transform the way we interact with text, supporting the interpretation and analysis of complex discourse and debates. Can a graphic visualisation of complex argumentation enable a more critical interpretation of the arguments? This study focuses on analysing the impact of argumentation graphs (AGs) compared with regular texts for supporting argument interpretation. We found that AGs outperformed the extrinsic metrics throughout most UEQ scales as well as the NASA-TLX workload in all the terms but not in temporal or physical demand. The AG model was liked by a more significant number of participants, despite the fact that both the text-based and AG models yielded comparable outcomes in the critical interpretation in terms of working memory and altering participants decisions. The interpretation process involves reference to argumentation schemes (linked to critical questions (CQs)) in AGs. Interestingly, we found that the participants chose more CQs (using argument schemes in AGs) when they were less familiar with the argument topics, making AG schemes on some scales (relatively) supportive of the interpretation process. Therefore, AGs were considered to deliver a more critical approach to argument interpretation, especially with unfamiliar topics. Based on the 25 participants conducted in this study, it appears that AG has demonstrated an overall positive effect on the argument interpretation process.

Latitude, the startup behind text game AI Dungeon, is expanding into a new artificial intelligence-powered game platform called Voyage. The company announced the closed beta on Friday, opening a waitlist for current AI Dungeon users. It's the next step for a company that began with a university hackathon project, but that ultimately hopes to help other people create their own games using trained AI models. AI Dungeon, which launched as AI Dungeon 2 in 2019, is powered by OpenAI's GPT-2 and GPT-3 text generation algorithms. To start, you generate some introductory text or write your own adventure setup.

I hope my headline is an overstatement, purely for job purposes, but in this week's Vergecast artificial intelligence episode, we explore the world of large language models and how they might be used to produce AI-generated text in the future. Maybe it'll give writers ideas for the next major franchise series, or write full blog posts, or, at the very least, fill up websites with copy that's too arduous for humans to do. Among the people we speak to is Nick Walton, the cofounder and CEO of Latitude, which makes the game AI Dungeon, which creates a plot in the game around what you put into it. We also chat with Samanyou Garg, founder of Writesonic, a company that offers various writing tools powered by AI. The company can even have AI write a blog post -- I'm shaking!

An artificial intelligence technique--machine learning--is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing. Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability. Already, researchers are expanding the model to predict other important MOF properties. Supported by the Office of Science's Basic Energy Sciences program within the U.S. Department of Energy (DOE), the research was reported Nov. 9 in the journal Nature Machine Intelligence. The research was conducted in the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), a DOE Energy Frontier Research Center located at the Georgia Institute of Technology.