Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correlations in SCM data tend to increase along the causal ordering. Several popular algorithms exploit these artifacts, possibly leading to conclusions that do not generalize to real-world settings. Existing metrics like $\operatorname{Var}$-sortability and $\operatorname{R^2}$-sortability quantify these patterns, but they do not provide tools to remedy them. To address this, we propose internally-standardized structural causal models (iSCMs), a modification of SCMs that introduces a standardization operation at each variable during the generative process. By construction, iSCMs are not $\operatorname{Var}$-sortable, and as we show experimentally, not $\operatorname{R^2}$-sortable either for commonly-used graph families. Moreover, contrary to the post-hoc standardization of data generated by standard SCMs, we prove that linear iSCMs are less identifiable from prior knowledge on the weights and do not collapse to deterministic relationships in large systems, which may make iSCMs a useful model in causal inference beyond the benchmarking problem studied here.

With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19) pandemic, there are still uncertainties that are yet to be discovered about its spread, future impact, and resurgence. In this paper, we present a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms and their intrinsic demographic variables. As a second stage, the output from the Bayesian network structure learning, serves as a useful guide to train an unsupervised machine learning (ML) algorithm that uncovers the similarities in patients' symptoms through clustering. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification (DSID) model which predicts a patient's symptom class and the corresponding demographic probability distribution. We applied our method on the COVID-19 dataset obtained from the Centers for Disease Control and Prevention (CDC) in the United States. Results from the experiments show a testing accuracy of 99.99%, as against the 41.15% accuracy of a heuristic ML method. This strongly reveals the viability of our Bayesian network and ML approach in understanding the relationship between the virus symptoms, and providing insights on patients' stratification towards reducing the severity of the virus.

The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts. With the advent of OpenAI's ChatGPT, LLM-based chatbots have set new standards in the AI community. This paper presents a complete survey of the evolution and deployment of LLM-based chatbots in various sectors. We first summarize the development of foundational chatbots, followed by the evolution of LLMs, and then provide an overview of LLM-based chatbots currently in use and those in the development phase. Recognizing AI chatbots as tools for generating new knowledge, we explore their diverse applications across various industries. We then discuss the open challenges, considering how the data used to train the LLMs and the misuse of the generated knowledge can cause several issues. Finally, we explore the future outlook to augment their efficiency and reliability in numerous applications. By addressing key milestones and the present-day context of LLM-based chatbots, our survey invites readers to delve deeper into this realm, reflecting on how their next generation will reshape conversational AI.

The errors and omissions come as tech companies increasingly invest in technology that pushes users to a single definitive answer -- rather than providing a list of websites -- raising the stakes of each response. They also come as Donald Trump and his allies continue to press the false claim that the 2020 election was stolen. Multiple investigations have revealed no evidence of fraud, and Trump faces federal criminal charges related to his efforts to overturn the election of Joe Biden, who swamped Trump in the electoral college and earned over 51 percent of the popular vote.

The Defense Ministry is planning to test the use of lasers to counter drones, sources familiar with the matter have said. Under the plan, a laser system will be installed on vehicles of the Ground Self-Defense Force, including high mobility vehicles, and tests on its response capabilities will be conducted, the sources said. In March, the ministry signed a contract to buy lasers to be mounted on GSDF high mobility vehicles from Kawasaki Heavy Industries for about 1.5 billion and a 1.9 billion deal to purchase lasers for trucks from Mitsubishi Heavy Industries.

The Chinese military recently unveiled a new kind of battle buddy for its soldiers: a "robot dog" with a machine gun strapped to its back. In video distributed by the state-run news agency CCTV, People's Liberation Army personnel are shown operating on a testing range alongside a four-legged robot with what appears to be a variant of the standard-issue 5.8 x 42-mm QBZ-95 assault rifle mounted on it as part of China's recent Golden Dragon 24 joint military exercises with Cambodia in the Gulf of Thailand. In one scenario, Chinese soldiers stand on either side of a doorway while the robot dog enters the building ahead of them; in another, the robot fires off a burst of bullets as it advances on a target. "It can serve as a new member in our urban combat operations, replacing our members to conduct reconnaissance and identify enemy [sic] and strike the target during our training," one Chinese soldier shown operating the robot told CCTV. This isn't the first time the Chinese military-industrial complex has shown off an armed robot dog. In October 2022, Chinese defense company Kestrel Defense published a video showing an unmanned aerial vehicle air-dropping a quadrupedal ground vehicle affixed with a 5.8 x 42-mm QBB-97 light machine gun on a roof during an urban warfare experiment.

A boarding system using a facial recognition technology was introduced at the Yamaman Yukarigaoka Line, a train service in a residential area in the city of Sakura, Chiba Prefecture, on Saturday. According to Yamaman, the operator of the line, it is the first time that a facial recognition-based boarding management system for general passengers has been put into practical use at a public transportation system in the country. After registering their face photo data and credit card information on a dedicated website in advance, passengers can board trains, without buying tickets, through cameras installed at the ticket gates at stations that recognize their faces. Passengers with commuter passes can also use the system. Yamaman introduced the system also to its bus service the same day.

Beneath an old Queenslander on the south side of the Brisbane River, beside a garage with a hand-painted sign that reads "recording" and above a computer in a cluttered spare room, is a Post-it note. The home – "not unlike Bluey's" – belongs to Bryce Corbett and doubles as an unofficial headquarters of the children's news podcast he founded and co-presents, Squiz Kids. Daily episodes tackle a headline story – like South Australia's proposal to ban children from social media – covered to inform, but not frighten, kids. The coating: a bit of fun science, pop culture and, of course, animal stories – the alligator that came to school, the world's funniest crab joke. Corbett's chat, too, is professional yet upbeat.

Large language models (LLMs) inevitably memorize sensitive, copyrighted, and harmful knowledge from the training corpus; therefore, it is crucial to erase this knowledge from the models. Machine unlearning is a promising solution for efficiently removing specific knowledge by post hoc modifying models. In this paper, we propose a Real-World Knowledge Unlearning benchmark (RWKU) for LLM unlearning. RWKU is designed based on the following three key factors: (1) For the task setting, we consider a more practical and challenging unlearning setting, where neither the forget corpus nor the retain corpus is accessible. (2) For the knowledge source, we choose 200 real-world famous people as the unlearning targets and show that such popular knowledge is widely present in various LLMs. (3) For the evaluation framework, we design the forget set and the retain set to evaluate the model's capabilities across various real-world applications. Regarding the forget set, we provide four four membership inference attack (MIA) methods and nine kinds of adversarial attack probes to rigorously test unlearning efficacy. Regarding the retain set, we assess locality and utility in terms of neighbor perturbation, general ability, reasoning ability, truthfulness, factuality, and fluency. We conduct extensive experiments across two unlearning scenarios, two models and six baseline methods and obtain some meaningful findings. We release our benchmark and code publicly at http://rwku-bench.github.io for future work.

Privacy issues arise prominently during the inappropriate transmission of information between entities. Existing research primarily studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns, while neglecting that privacy is not an isolated, context-free concept limited to traditionally sensitive data (e.g., social security numbers), but intertwined with intricate social contexts that complicate the identification and analysis of potential privacy violations. The advent of Large Language Models (LLMs) offers unprecedented opportunities for incorporating the nuanced scenarios outlined in privacy laws to tackle these complex privacy issues. However, the scarcity of open-source relevant case studies restricts the efficiency of LLMs in aligning with specific legal statutes. To address this challenge, we introduce a novel framework, GoldCoin, designed to efficiently ground LLMs in privacy laws for judicial assessing privacy violations. Our framework leverages the theory of contextual integrity as a bridge, creating numerous synthetic scenarios grounded in relevant privacy statutes (e.g., HIPAA), to assist LLMs in comprehending the complex contexts for identifying privacy risks in the real world. Extensive experimental results demonstrate that GoldCoin markedly enhances LLMs' capabilities in recognizing privacy risks across real court cases, surpassing the baselines on different judicial tasks.