Jefferson County Sheriff Jeff Bullard said after one year in effect, the SAFE-T Act is having the "intended result" and damaging the policing profession in Illinois. With the calendar-page turn to 2024 on Monday comes 320 new state laws that Illinois residents will need to navigate. Some will have a widespread effect, including a law banning semi-automatic rifles and another requiring paid time off. But others won't have an immediate or noticeable impact, including a law that lets county governments consider a potential contractor's participation in an approved apprenticeship program in determining the winning low bid for a project. One law that took effect in 2019 but is still impacting tens of thousands of workers is an increase in the minimum wage.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The vice chair of Congress' artificial intelligence caucus says privacy regulations need to be a top short-term priority for Congress as Washington looks to get to grips with the rapidly emerging technology – which he says poses risks, but could be a catalyst for the next expansion of the U.S. economy. Rep. Jay Obernolte, R-Calif., told Fox News Digital in an interview that he is an optimist when it comes to the potential for artificial intelligence, but Congress needs to make sure it is protecting Americans from the potential negatives and disruption that AI brings. "I think in the short term, the ability of AI to pierce through digital data privacy and to re-aggregate data that has supposedly been disaggregated and use it to create behavioral models that could be used to influence behavior, that's very concerning, and that's something that the government definitely needs to play a role in mitigating," Obernolte said. Rep. Jay Obernolte has a graduate degree in artificial intelligence.

As wildfire activity reaches record levels, the tech integration company SAIC is developing artificial intelligence technology that can help predict when they'll happen, how to stop them, and how to keep folks safe. Artificial intelligence made a big splash with consumers and regulators alike in 2023, with experts believing the continued development of the technology will reach even greater heights in 2024. "I think that in 2024, AI will move a little closer to what is in the public imagination, but we remain years from AI being autonomous in the way people are imagining it," Christopher Alexander, chief analytics officer of Pioneer Development Group, told Fox News Digital. Alexander's comments come after 2023 saw a noticeable leap in the development and availability of AI tools, with popular language learning model (LLM) platforms such as OpenAI's ChatGPT gaining huge popularity and energizing other tech giants to come along for the ride. Artificial intelligence will gain new capabilities in 2024.

We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts. Using our annotated data, we have trained and evaluated case outcome extraction with GPT-3, GPT-4 and RoBERTa models to provide benchmarks. We include an extensive legal and ethical discussion to address the potentially sensitive nature of this material. As a consequence, the corpus will only be released for research purposes under certain restrictions.

Local neighborhoods play a crucial role in embedding generation in graph-based learning. It is commonly believed that nodes ought to have embeddings that resemble those of their neighbors. In this research, we try to carefully expand the concept of similarity from nearby neighborhoods to the entire graph. We provide an extension of similarity that is based on transitivity relations, which enables Graph Neural Networks (GNNs) to capture both global similarities and local similarities over the whole graph. We introduce Transitivity Graph Neural Network (TransGNN), which more than local node similarities, takes into account global similarities by distinguishing strong transitivity relations from weak ones and exploiting them. We evaluate our model over several real-world datasets and showed that it considerably improves the performance of several well-known GNN models, for tasks such as node classification. This popularity can be attributed to GNNs' adaptability and efficiency in learning from data structured as graphs, proving essential in domains where data can be naturally organized into nodes, and predictions rely on the complex relationships (edges) inter-linking these nodes. Their versatility finds applications in diverse fields such as molecular chemistry [7], social networks [8], and recommendation systems [9]. Graph Convolutional Networks (GCNs) [10], introduced by Kipf and Welling in 2017, present an efficient adaptation of Convolutional Neural Networks (CNNs) [11] for graph data. This model involves stacking layers of first-order spectral filters, succeeded by a non-linear activation function, facilitating the acquisition of graph representations [10]. Within the GNN framework, the core concept revolves around iteratively updating node states through interactions with their neighbors.

Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbreaking. These advanced models, despite their impressive capabilities, are often trained on massive Internet datasets, making them susceptible to generating content that perpetuates social stereotypes and biases, which can lead to severe consequences. Prior research on assessing bias within image generation models suffers from several shortcomings, including limited accuracy, reliance on extensive human labor, and lack of comprehensive analysis. In this paper, we propose BiasPainter, a novel metamorphic testing framework that can accurately, automatically and comprehensively trigger social bias in image generation models. BiasPainter uses a diverse range of seed images of individuals and prompts the image generation models to edit these images using gender, race, and age-neutral queries. These queries span 62 professions, 39 activities, 57 types of objects, and 70 personality traits. The framework then compares the edited images to the original seed images, focusing on any changes related to gender, race, and age. BiasPainter adopts a testing oracle that these characteristics should not be modified when subjected to neutral prompts. Built upon this design, BiasPainter can trigger the social bias and evaluate the fairness of image generation models. To evaluate the effectiveness of BiasPainter, we use BiasPainter to test five widely-used commercial image generation software and models, such as stable diffusion and Midjourney. Experimental results show that 100\% of the generated test cases can successfully trigger social bias in image generation models.

Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education to mislead users. Current methods for evaluating LLMs' veracity are limited by test data leakage or the need for extensive human labor, hindering efficient and accurate error detection. To tackle this problem, we introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs. This framework involves three main steps: First, it constructs a factual knowledge graph by retrieving fact triplets from a large-scale knowledge database. Then, leveraging the knowledge graph, FactChecker employs a rule-based approach to generates three types of questions (Yes-No, Multiple-Choice, and WH questions) that involve single-hop and multi-hop relations, along with correct answers. Lastly, it assesses the LLMs' responses for accuracy using tailored matching strategies for each question type. Our extensive tests on six prominent LLMs, including text-davinci-002, text-davinci-003, ChatGPT~(gpt-3.5-turbo, gpt-4), Vicuna, and LLaMA-2, reveal that FactChecker can trigger factual errors in up to 45\% of questions in these models. Moreover, we demonstrate that FactChecker's test cases can improve LLMs' factual accuracy through in-context learning and fine-tuning (e.g., llama-2-13b-chat's accuracy increase from 35.3\% to 68.5\%). We are making all code, data, and results available for future research endeavors.

The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specifically, comparative religion. Sentiment analysis provides a mechanism to study the emotions expressed in text. Recently, sentiment analysis has been used to study and compare translations of the Bhagavad Gita, which is a fundamental and sacred Hindu text. In this study, we use sentiment analysis for studying selected chapters of the Bible. These chapters are known as the Sermon on the Mount. We utilize a pre-trained language model for sentiment analysis by reviewing five translations of the Sermon on the Mount, which include the King James version, the New International Version, the New Revised Standard Version, the Lamsa Version, and the Basic English Version. We provide a chapter-by-chapter and verse-by-verse comparison using sentiment and semantic analysis and review the major sentiments expressed. Our results highlight the varying sentiments across the chapters and verses. We found that the vocabulary of the respective translations is significantly different. We detected different levels of humour, optimism, and empathy in the respective chapters that were used by Jesus to deliver his message.

It's been hard recently to think about anything other than the wars and humanitarian crises raging around the world. Climate change has left its mark in what was almost certainly the hottest year in human history--there were unprecedented heat waves, intensified forest fires, torrential rain, and floods like those in Libya that caused devastation after two dams burst. But this has not stopped scientists, innovators, and decisionmakers from working on solutions to our biggest societal challenges--with success. Here is a collection of uplifting news to come out of 2023. In an instant, millions of volts can damage buildings, spark fires, and harm people--unless the lightning can be redirected.

The United States and China explore the lunar presence of critical minerals. Mexico will launch its first lunar mission next month, a historic step for the country and Latin America as a whole, according to officials. "This project will make history and is the first of its kind in Latin America, which elevates the name of our country, confirming once again that Mexican engineering is at the level of the best in the world," Salvador Landeros, director of the Mexican Space Agency (AEM), said in a press release. A team of scientists and nearly 250 university students developed five microrobots that the AEM will launch from Cape Canaveral, Florida, between Jan. 8 and Jan. 11 as part of project Colmena. Each robot weighs 60 grams -- a little over one-tenth of a pound -- and measures just under 5 inches in diameter.