America is set to return to the moon on Thursday, marking the first time a US-made craft touched down on the lunar surface since the last Apollo mission in 1972. Odysseus, or Odie, is soaring through space, but unlike previous trips, this one is owned by Houston-based Intuitive Machines. The six-legged robot lander is scheduled to touch down at 6:24pm ET at a crater called Malapert A near the moon's south pole. The landing attempt will be livestreamed on NASA TV beginning at 5pm ET. While the mission is operated by a private company, NASA has sponsored the journey to take its scientific instruments and technology to the moon.

U.S. Central Command announced Thursday that American aircraft and a coalition warship have shot down six Houthi one-way attack drones in the Red Sea. The unmanned aerial vehicles were identified as "likely targeting U.S. and coalition warships and were an imminent threat," it said, noting that the drones were taken out around 4:30 a.m. "Later, between 8:30 a.m. and 9:45 a.m., the Houthis fired two anti-ship ballistic missiles from southern Yemen into the Gulf of Aden," Central Command also wrote in a post on X. "The missiles impacted MV Islander, a Palau-flagged, U.K.-owned, cargo carrier causing one minor injury and damage. The ship is continuing its voyage." The attack comes after the Pentagon earlier this week confirmed that the Houthis shot down a U.S. MQ-9 Reaper drone off the coast of Yemen on Monday.

Second gentleman Doug Emhoff recently said he and his wife Vice President Kamala Harris are "living" out the HBO series "Veep" during their time at the White House. Emhoff made the claim while appearing on Bravo's "Watch What Happens Live," telling host Andy Cohen that his and Harris' lives resemble the comedy centered on the antics of fictional Vice President Selina Meyer from the popular HBO comedy. During the segment, Cohen asked Emhoff, "Do you watch'Veep?' Have you ever seen'Veep?'" to which he responded, "We're living it." Second Gentleman Doug Emhoff recently claimed he and his wife, Vice President Harris, are "living" the HBO series "Veep." In the show, many of the comedic moments come from Meyer's gaffes, awkward social interactions and frustrations with the limits of her job and incompetence of her staff.

The Justice Department named its first-ever official focused on artificial intelligence (AI) on Thursday in anticipation of the rapidly evolving technology's impact on the criminal justice system. Jonathan Mayer, a professor at Princeton University who focuses on the "intersection of technology and law, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech," according to his online biography, was selected to serve as the DOJ's chief science and technology adviser and chief AI officer, Reuters reported. "The Justice Department must keep pace with rapidly evolving scientific and technological developments in order to fulfill our mission to uphold the rule of law, keep our country safe and protect civil rights," U.S. Attorney General Merrick Garland said in a statement. Mayer previously served as the technology adviser to Vice President Kamala Harris during her time as a U.S. senator, and as the Chief Technologist of the Federal Communications Commission Enforcement Bureau. In his new role, he is expected to advise Garland and DOJ leadership on matters related to emerging technologies, including how to responsibly integrate AI into the department's investigations and criminal prosecutions, according to Reuters.

Analysing a mouse's brain activity tells scientists where the animal is located and the exact direction it is looking. With further research, the findings could one day help robots navigate autonomously. Mammalian brains use two main types of neurons for navigation: "head direction cells" show where an animal is facing and "grid cells" help provide a two-dimensional brain map of where it is located. To learn more about the firing of these neurons, Vasileios Maroulas at the University of Tennessee, Knoxville, and his colleagues – together with the US Army Research Laboratory – analysed data from a previous study. Revealed: What your thoughts look like and how they compare to others' In this experiment, probes were inserted into several mice's brains.

Google has put a temporary block on its new artificial intelligence model producing images of people after it portrayed German second world war soldiers and Vikings as people of colour. The tech company said it would stop its Gemini model generating images of people after social media users posted examples of images generated by the tool that depicted some historical figures – including popes and the founding fathers of the US – in a variety of ethnicities and genders. "We're already working to address recent issues with Gemini's image generation feature. While we do this, we're going to pause the image generation of people and will rerelease an improved version soon," Google said in a statement. Google did not refer to specific images in its statement, but examples of Gemini image results were widely available on X, accompanied by commentary on AI's issues with accuracy and bias, with one former Google employee saying it was "hard to get Google Gemini to acknowledge that white people exist". Jack Krawczyk, a senior director on Google's Gemini team, had admitted on Wednesday that the model's image generator – which is not available in the UK and Europe – needed adjustment.

The U.S. military conducted "self-defense strikes" against Houthi missiles and a launcher prepared to fire from Yemen toward the Red Sea on Wednesday, U.S. Central Command announced. Between 12 a.m. and 6:45 p.m. local time on Wednesday, four self-defense strikes were launched in response to seven mobile Houthi anti-ship cruise missiles and one mobile anti-ship ballistic missile launcher aimed at the Red Sea, the agency said. Also, in an act of self-defense, CENTCOM said its forces shot down a one-way attack unmanned aircraft system. U.S. Central Command announced more "self-defense strikes" against Houthi terrorists in Yemen after American forces located missiles and a launcher prepared to fire toward the Red Sea. The missiles, launchers and the unmanned aircraft system were all determined to have originated from Houthi-controlled areas of Yemen.

Some of the major limitations identified in the areas of argument mining, argument generation, and natural language argument analysis are related to the complexity of annotating argumentatively rich data, the limited size of these corpora, and the constraints that represent the different languages and domains in which these data is annotated. To address these limitations, in this paper we present the following contributions: (i) an effective methodology for the automatic generation of natural language arguments in different topics and languages, (ii) the largest publicly available corpus of natural language argumentation schemes, and (iii) a set of solid baselines and fine-tuned models for the automatic identification of argumentation schemes.

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks.

The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context. Previous research has predominantly focused on examining hallucinations stemming from static input, such as in the domains of summarization or machine translation. However, our investigation delves into the faithfulness of generative question answering in the presence of dynamic knowledge. Our objective is to explore the existence of hallucinations arising from parametric memory when contextual knowledge undergoes changes, while also analyzing the underlying causes for their occurrence. In order to efficiently address this issue, we propose a straightforward yet effective measure for detecting such hallucinations. Intriguingly, our investigation uncovers that all models exhibit a tendency to generate previous answers as hallucinations. To gain deeper insights into the underlying causes of this phenomenon, we conduct a series of experiments that verify the critical role played by context in hallucination, both during training and testing, from various perspectives.