Rep. Michael McCaul, R-Texas, criticizes America's stance on the world stage at the hands of President Joe Biden in an exclusive interview on'Sunday Morning Futures.' White House national security adviser Jake Sullivan was pressed in an interview Sunday over an alleged civilian casualty – a father of 10 who was tending sheep – by U.S. forces in Syria. CNN's Jake Tapper asked Sullivan about the reportedly botched missile strike, which the Pentagon initially claimed was a successful assassination of a "senior Al Qaeda leader," but later backtracked and launched an investigation. Sullivan said he could not comment on the matter until the Pentagon's "full and thorough investigation" was complete – and instead touted President Biden's record on military accountability. "It was President Biden who stood up with Secretary Austin's guidelines for this administration to ensure there would be accountability and oversight of any potential civilian casualties from counterterrorism strikes," Sullivan said. "So far we do not have evidence to validate the claims being made in Syria. But I am going to withhold any judgment on what actually happened here until the Pentagon's investigation is complete."

D.C. residents said they don't trust Vice President Kamala Harris to lead the White House's response to artificial intelligence. WASHINGTON, D.C. – Vice President Kamala Harris wouldn't be able to effectively run the White House's response to artificial intelligence if she's charged with leading it, some residents of the nation's capital told Fox News. "I don't know if Kamala Harris has the background and the tech knowledge to really get a grasp on what AI can do and what its capabilities are, to be able to wrangle it in a space that is safe for everyone and not just beneficial for large corporations," Eric told Fox News. Vice President Kamala has been involved with the White House's AI efforts. But another D.C. local, Marlena, said: "I definitely trust her on the task force. She's a brilliant woman, extraordinarily accomplished."

We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: we build an anti-LM based on previously generated text and use this anti-LM to penalize future generation of what has been generated. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD introduces no extra model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite the simplicity, FSD is surprisingly effective; Experiments show that FSD can outperform the canonical methods to date (i.e., nucleus sampling) as well as several strong baselines that were proposed recently.

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

Recent progress in large language models has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that ``it's all been solved.'' Not surprisingly, this has in turn made many NLP researchers -- especially those at the beginning of their career -- wonder about what NLP research area they should focus on. This document is a compilation of NLP research directions that are rich for exploration, reflecting the views of a diverse group of PhD students in an academic research lab. While we identify many research areas, many others exist; we do not cover those areas that are currently addressed by LLMs but where LLMs lag behind in performance, or those focused on LLM development. We welcome suggestions for other research directions to include: https://bit.ly/nlp-era-llm

Kai Zhang, PhD, Xiaoqian Jiang, PhD The University of Texas Health Science Center, McWilliams School of Biomedical Informatics, Houston, TX, USA Abstract: In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based sensitive variable identification systems that work on one database fail on another. To address this issue, our paper explores the use of machine learning algorithms to identify sensitive variables in structured data, thus facilitating the de-identification process. We made a key observation that the distributions of metadata of PHI fields and non-PHI fields are very different. Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data. We trained the model on a variety of large EHR databases from different data sources and found that our algorithm achieves 99% accuracy when detecting PHI-related fields for unseen datasets.

A. Missing Artifacts We manually evaluate 142 papers that reproduce previous studies and are published at the open-access peer-reviewed journal ReScience C. We tagged each paper based on the issues the authors faced when reproducing the original work with tags denoting implementation issues, hyperparameter issues, and the responsiveness of the original author. The end result is that only a subset of the original trials are valid, which can lead to biased analysis and unreproducible results. Abstract--Machine learning is facing a'reproducibility crisis' B. Problematic Tooling [1] identifies missing and convoluted artifacts as one of the We quantitatively evaluate the inter-project dependency issue main causes of non-reproducible research. Artifacts include for reproducibility [3]. We mine 132 repositories from the configuration details, details on the methodology, and code.

Large Language Models (LLMs) can generate texts that carry the risk of various misuses, including plagiarism, planting fake reviews on e-commerce platforms, or creating fake social media postings that can sway election results. Detecting whether a text is machine-generated has thus become increasingly important. While machine-learning-based detection strategies exhibit superior performance, they often lack generalizability, limiting their practicality. In this work, we introduce GPT Paternity Test (GPT-Pat), which reliably detects machine-generated text across varied datasets. Given a text under scrutiny, we leverage ChatGPT to generate a corresponding question and provide a re-answer to the question. By comparing the similarity between the original text and the generated re-answered text, it can be determined whether the text is machine-generated. GPT-Pat consists of a Siamese network to compute the similarity between the original text and the generated re-answered text and a binary classifier. Our method achieved an average accuracy of 94.57% on four generalization test sets, surpassing the state-of-the-art RoBERTa-based method by 12.34%. The accuracy drop of our method is only about half of that of the RoBERTa-based method when it is attacked by re-translation and polishing.

Hiroshima, Japan – Ukrainian President Volodymyr Zelenskyy has embarked on a flurry of diplomacy with international leaders in Japan, turning the focus of the Group of Seven summit firmly towards efforts to end Russia's 15-month war in Ukraine. Zelenskyy's surprise visit to Hiroshima on Saturday evening, following earlier reports that he would only attend online, immediately thrust the G7's efforts to choke Russia's economy to the top of a busy agenda that has touched on issues ranging from China's rise to the rapid advancement of artificial intelligence. Shortly after touching down in Japan in a French aircraft, fresh on the heels of whistlestop visits to Europe and Saudi Arabia, Zelenskyy said the summit would be an opportunity for "enhanced cooperation for our victory". "Peace will become closer today," he said on his official Twitter account. French President Emmanuel Macron late on Saturday hailed Zelenskyy's attendance at the summit as a potential "game changer".