We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.

M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.

Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. As existing ECR benchmark datasets rarely provide images for all event mentions, we augment the popular ECB+ dataset with event-centric images scraped from the internet and generated using image diffusion models. We establish three methods that incorporate images and text for coreference: 1) a standard fused model with finetuning, 2) a novel linear mapping method without finetuning and 3) an ensembling approach based on splitting mention pairs by semantic and discourse-level difficulty. We evaluate on 2 datasets: the augmented ECB+, and AIDA Phase 1. Our ensemble systems using cross-modal linear mapping establish an upper limit (91.9 CoNLL F1) on ECB+ ECR performance given the preprocessing assumptions used, and establish a novel baseline on AIDA Phase 1. Our results demonstrate the utility of multimodal information in ECR for certain challenging coreference problems, and highlight a need for more multimodal resources in the coreference resolution space.

While deep neural networks (DNNs) have led to remarkable results in standard supervised learning tasks in computer vision and natural language processing, they are widely recognized to be susceptible to minor adversarially-designed perturbations to their input data commonly regarded as adversarial attacks [1, 2]. Adversarial examples are typically designed by finding the worst-case norm-constrained perturbation that leads to the maximum impact on the classification loss at an input data point. To combat norm-bounded adversarial attacks, adversarial training (AT) methods [3] which learn a DNN classifier using adversarially-perturbed training examples have been shown to significantly improve the robustness of a DNN against norm-bounded adversarial attacks. Several variants of AT methods have been developed in the machine learning community to accelerate and facilitate the application of AT algorithms to large-scale machine learning problems [4, 5]. While AT algorithms have achieved state-of-the-art robustness scores against standard norm-bounded adversarial attacks, the generalization gap between their performance on training and test data has been frequently observed to be significantly greater than the generalization error of DNNs learned by standard empirical risk minimization (ERM) [6, 7]. To understand the significant generalization gap in adversarial training, several theoretical and empirical studies have focused on the generalization properties of adversariallytrained models [8,9]. These studies have attempted to analyze the generalization error in learning adversariallyrobust models and reduce the generalization gap by applying explicit and implicit regularization techniques such as early stopping and Lipschitz regularization methods. Specifically, several recent works [10-12] have focused on the connections between the optimization and generalization behavior of adversarially-trained models.

We live in frightening times. Wars in Ukraine and Gaza could widen, the polar ice caps are melting, and even some scientists developing artificial intelligence systems are worried about unleashing a monster. But those fears all pale in comparison to what really gives Americans the jitters. The calamity that worries them above all else is a total economic collapse in the US. The Pentagon's four legged robot dogs may offer a glimpse of what killer machines will look like An economic meltdown is the top fear for a third of respondents.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Fake Botox is on the CDC's radar. The U.S. Centers for Disease Control and Prevention (CDC) announced on Friday that it is investigating reports of "a few botulism-like illnesses in several states resulting from botulinum toxin injections (commonly called'Botox') administered in non-medical settings," the agency said in a statement. "We are coordinating a multi-state outbreak investigation," the agency added.

Since the war began in Gaza, more than six months ago, the Israeli magazine 972 has published some of the most penetrating reporting on the Israel Defense Forces' conduct. In November, 972, along with the Hebrew publication Local Call, found that the I.D.F. had expanded the number of "legitimate" military targets, leading to a huge increase in civilian casualties. Then earlier this month, 972 and Local Call released a long feature called "Lavender: The AI Machine Directing Israel's Bombing Spree in Gaza." The story revealed how the Israeli military had used the program to identify suspected militants, which in practice meant that tens of thousands of Palestinians had their homes marked as legitimate targets for bombing, with minimal human oversight. The I.D.F. also said that, according to its rules, "analysts must conduct independent examinations" to verify the identification of targets.

Any dreams of a sweeping AI bill out of Congress are basically a hallucination.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. A massive missile and drone attack destroyed one of Ukraine's largest power plants and damaged others, officials said Thursday, part of a renewed Russian campaign targeting energy infrastructure. The Trypilska plant, which was the biggest energy supplier for the Kyiv, Cherkasy and Zhytomyr regions, was struck numerous times, destroying the transformer, turbines and generators and leaving the plant ablaze. As the first drone approached, workers hid in a shelter, saving their lives, said Andrii Gota, chairman of the supervisory board of the state company that runs the plant, Centrenergo.

As spacecraft journey further from Earth with more complex missions, systems of greater autonomy and onboard intelligence are called for. Reducing reliance on human-based mission control becomes increasingly critical if we are to increase our rate of solar-system-wide exploration. Recent work has explored AI-based goal-oriented systems to increase the level of autonomy in mission execution. These systems make use of symbolic reasoning managers to make inferences from the state of a spacecraft and a handcrafted knowledge base, enabling autonomous generation of tasks and re-planning. Such systems have proven to be successful in controlled cases, but they are difficult to implement as they require human-crafted ontological models to allow the spacecraft to understand the world. Reinforcement learning has been applied to train robotic agents to pursue a goal. A new architecture for autonomy is called for. This work explores the application of Large Language Models (LLMs) as the high-level control system of a spacecraft. Using a systems engineering approach, this work presents the design and development of an agentic spacecraft controller by leveraging an LLM as a reasoning engine, to evaluate the utility of such an architecture in achieving higher levels of spacecraft autonomy. A series of deep space mission scenarios simulated within the popular game engine Kerbal Space Program (KSP) are used as case studies to evaluate the implementation against the requirements. It is shown the reasoning and planning abilities of present-day LLMs do not scale well as the complexity of a mission increases, but this can be alleviated with adequate prompting frameworks and strategic selection of the agent's level of authority over the host spacecraft. This research evaluates the potential of LLMs in augmenting autonomous decision-making systems for future robotic space applications.