This paper focuses on the piloting of the CGScholar AI Helper, a Generative AI (GenAI) assistant tool that aims to provide feedback on writing in high school contexts. The aim was to use GenAI to provide formative and summative feedback on students' texts in English Language Arts (ELA) and History. The trials discussed in this paper relate to Grade 11, a crucial learning phase when students are working towards college readiness. These trials took place in two very different schools in the Midwest of the United States, one in a low socio-economic background with low-performance outcomes and the other in a high socio-economic background with high-performance outcomes. The assistant tool used two main mechanisms "prompt engineering" based on participant teachers' assessment rubric and "fine-tuning" a Large Language Model (LLM) from a customized corpus of teaching materials using Retrieval Augmented Generation (RAG). This paper focuses on the CGScholar AI Helper's potential to enhance students' writing abilities and support teachers in ELA and other subject areas requiring written assignments.

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.

For a prolific art collector, Nicolai Tangen is remarkably relaxed about the prospect of masterpieces created by robots. The threat of AI-made paintings, impossible to distinguish from human brushstrokes, has sparked soul-searching and paranoia in the art world, but not with Tangen. "Hey, if it creates better art that's fantastic," says the Norwegian philanthropist, art historian and boss of the world's biggest sovereign wealth fund. "If you create something which is even more aesthetically pleasing, what's wrong about that?" Tangen's own gallery, a converted grain silo in the Norwegian seaside resort of Kristiansand, will open later this year to display one of the world's biggest collections of Nordic modernist art. Tangen has amassed more than 5,000 works by 300 artists.

How old is your brain, really? Just like people who look older than they really are, scientists say a person's brain can age faster than the rest of their body. With that in mind, researchers at USC have created an artificial intelligence program which can accurately tell how old someone's brain is -- while also pointing out warning signs for Alzheimer's disease. The AI program analyzes MRI brain scans, looking for signs of cognitive decline which have a link to neurodegenerative diseases, like Alzheimer's. Brain aging is one of the most reliable markers for neurodegenerative disease risk.

A new peer-reviewed study published in PLOS Digital Health demonstrates how OpenAI's GPT-3 program predicts early stages of dementia from spontaneous speech with a high degree of accuracy. "To our knowledge, this is the first application of GPT-3 to predicting dementia from speech," wrote professor Hualou Liang, Ph.D., and co-author Felix Agbavor at Drexel's School of Biomedical Engineering, Science and Health Systems. The most common type of dementia is Alzheimer's disease, a neurodegenerative disease that affects an estimated 47 million people worldwide, according to the Alzheimer's Association. By 2030 this figure is expected to grow to 76 million globally, according to the same source. There are 5.8 million Americans with Alzheimer's disease, of which two-thirds are women, according to a report by AARP and the Women's Alzheimer's Movement (WAM).

Healthcare's comfort level with artificial intelligence and machine learning models – and skill at deploying them across myriad clinical, financial and operational use cases – continued to increase in 2023. More and more evidence shows that training AI algorithms on a variety of datasets can improve decision support, boost population health management, streamline administrative tasks, enable cost efficiencies and even improve outcomes. But there's still a lot work to be done to ensure accurate, reliable, understandable and evidence-based results that ensure patient safety and account for health equity. There's no doubt that AI's application in healthcare has gone beyond "real" in 2019 to significant investment by providers and payers last year. This year, we've reported on deeper industry discussions focused on trust and best practices.

Rising to meet the formidable challenge of the timely diagnosis of dementia, research scientists from Regenstrief Institute, IUPUI and the medical schools of Indiana University and University of Miami are conducting the Digital Detection of Dementia study, a real-world evaluation of the use of an artificial intelligence (AI) tool they developed for early identification of Alzheimer's disease and related dementias in primary care, the setting where most adults receive healthcare. Individuals identified as cognitively impaired will be referred for diagnostic services. The AI tool, called a passive digital marker, is a machine learning algorithm the researchers developed, trained and tested. The tool uses natural language processing to cull unstructured information in combination with structured data from a patient's electronic health record. These could include mention of memory issues, a notation of vascular concerns, comorbid conditions or other factors potentially linked to dementia. "Between 50 to 80 percent of dementia cases are unrecognized by the healthcare system in the U.S. And, if you include patients living with mild cognitive impairment, that number might actually climb to higher than 80 percent of cases that are not recognized," said Regenstrief Institute and Indiana University School of Medicine faculty member Malaz Boustani, M.D., MPH, senior author of the Digital Detection of Dementia study protocol paper, published in the peer reviewed journal Trials.

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.