With the rapid rise of generative AI in higher education and the unreliability of current AI detection tools, developing policies that encourage student learning and critical thinking has become increasingly important. This study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools, their perceptions of generative AI's usefulness and limitations, and what implications these perceptions hold for teaching proof-based mathematics. We conclude by discussing future considerations for integrating generative AI into proof-based mathematics instruction.

The proliferation of Large Language Models (LLMs) in late 2022 has impacted academic writing, threatening credibility, and causing institutional uncertainty. We seek to determine the degree to which LLMs are used to generate critical text as opposed to being used for editing, such as checking for grammar errors or inappropriate phrasing. In our study, we analyze arXiv papers for stylistic segmentation, which we measure by varying a PELT threshold against a Bayesian classifier trained on GPT-regenerated text. We find that LLM-attributed language is not predictive of stylistic segmentation, suggesting that when authors use LLMs, they do so uniformly, reducing the risk of hallucinations being introduced into academic preprints.

Each fall, I begin my course on the intersection of music and artificial intelligence by asking my students if they're concerned about AI's role in composing or producing music. So far, the question has always elicited a resounding "yes." Their fears can be summed up in a sentence: AI will create a world where music is plentiful, but musicians get cast aside. In the upcoming semester, I'm anticipating a discussion about Paul McCartney, who in June 2023 announced that he and a team of audio engineers had used machine learning to uncover a "lost" vocal track of John Lennon by separating the instruments from a demo recording. But resurrecting the voices of long-dead artists is just the tip of the iceberg in terms of what's possible – and what's already being done. In an interview, McCartney admitted that AI represents a "scary" but "exciting" future for music.

One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets. We focus on antisemitic speech on Twitter and create a labeled dataset of 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021 by drawing from representative samples with relevant keywords. Our annotation process aims to strictly apply a commonly used definition of antisemitism by forcing annotators to specify which part of the definition applies, and by giving them the option to personally disagree with the definition on a case-by-case basis. Labeling tweets that call out antisemitism, report antisemitism, or are otherwise related to antisemitism (such as the Holocaust) but are not actually antisemitic can help reduce false positives in automated detection. The dataset includes 1,250 tweets (18%) that are antisemitic according to the International Holocaust Remembrance Alliance (IHRA) definition of antisemitism. It is important to note, however, that the dataset is not comprehensive. Many topics are still not covered, and it only includes tweets collected from Twitter between January 2019 and December 2021. Additionally, the dataset only includes tweets that were written in English. Despite these limitations, we hope that this is a meaningful contribution to improving the automated detection of antisemitic speech.

Vivek Astvansh explains how ChatGPT works and believes ChatGPT has the potential to replace human beings whose job is to refer to volumes of information contained on the internet, in textbooks, or in memory, and produce information based on that available content. Astvansh is an Assistant Professor in the Department of Marketing at the Kelley School of Business at Indiana University and an Adjunct Professor of Data Science at the Luddy School of Informatics, Computing, and Engineering at Indiana University. Don't Miss: Valley of Hype: The culture that built Elizabeth Holmes WATCH HERE: About Yahoo Finance: At Yahoo Finance, you get free stock quotes, up-to-date news, portfolio management resources, international market data, social interaction and mortgage rates that help you manage your financial life. Yahoo Finance Plus: With a subscription to Yahoo Finance Plus get the tools you need to invest with confidence. Discover new opportunities with expert research and investment ideas backed by technical and fundamental analysis.

Christopher Bouzy is trying to stay ahead of the bots. As the person behind Bot Sentinel, a popular bot-detection system, he and his team continuously update their machine learning models out of fear that they will get "stale." The task? Sorting 3.2 million tweets from suspended accounts into two folders: "Bot" or "Not." To detect bots, Bot Sentinel's models must first learn what problematic behavior is through exposure to data. And by providing the model with tweets in two distinct categories--bot or not a bot--Bouzy's model can calibrate itself and allegedly find the very essence of what, he thinks, makes a tweet problematic.

Researchers have been using artificial intelligence to study how the microbiome interacts with the human system to improve vaccine response. A team of researchers at Iowa State University, US, are employing innovative artificial intelligence (AI) to investigate how the microbiome interacts with the immune system. The team, led by Dr Gregory Phillips, said that they are focusing on gut bacteria that have adapted to live in the human digestive system to improve vaccine response. We want to go beyond associations to get causes, something in the microbiota that influences the host whereby vaccines can be improved" The team are leading trials in mice monitoring changes in microbiota spurred by vaccine delivery and immune response. As the interactions they will be observing are so complex, the team have collaborated with Indiana University, US, to apply machine learning to find patterns in vast amounts of data.

Now, AI at IU has a home. IU President Michael A. McRobbie dedicated the $35 million Luddy Center for Artificial Intelligence, a 58,000-square-foot facility that will serve as the hub for multidisciplinary research in advanced AI and machine-learning applications, during a ceremony June 23 at Luddy Hall. "As we dedicate the Luddy Center for Artificial Intelligence, it is fair to say that we are also celebrating what will be a game-changing development for Indiana University," McRobbie said. "Indiana University has been a center of research in a number of areas of AI for many years. Artificial intelligence has long been an area of strength of the Department of Computer Science and, more broadly, IU faculty in the cognitive sciences, psychological sciences and neurosciences have also long been engaged in areas of research relevant to AI. The explosion worldwide of the uses and applications of AI, building on decades of steady research progress, made this the perfect time for IU to establish a major holistic initiative in artificial intelligence."

From smart appliances and chat bots to public safety and intuitive logistics, artificial intelligence impacts our daily lives. AI reaches across platforms at enterprise scale, offering convenience, increasing productivity, and propelling innovation. The IU Online BA in Artificial Intelligence can put you at the forefront of this emerging AI landscape. As a student in this program, you develop the AI skills that employers are seeking in machine learning, bot development, robotic process automation, and cognitive computing. Problem solving lies at the core of the program, as you learn how to apply artificial intelligence, bot platforms, and frameworks to automate robotic and cognitive processes from start to finish.

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.