A rare albino deer was spotted enjoying a midnight snack from a bird feeder in a suburb of St Louis, Missouri. Albinism is observed in one in 30,000 deer, with albino deer referred to as "ghost deer," according to the Missouri Dept. of Conservation. As photos of so-called "Frankenstein" rabbits and squirrels with strange growths on their heads and bodies have started to pop up on social media, users are now sharing pictures taken of deer with bulbous warts. While the warts, or "fibromas" as they're called, may look scary, they generally don't affect the deer's health unless the growths are around the eyes and mouth, hindering their ability to see and eat and making it harder to move, according to experts. Deer fibromas are caused by an infection and are common in the U.S., the Maine Department of Inland Fisheries and Wildlife says on its website, adding that similar diseases affect squirrels and rabbits.

The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.

AI – or artificial intelligence – is often used as a way to summarize data and improve writing. But AI tools also represent a powerful and efficient way to analyze large amounts of text to search for patterns. In addition, AI tools can assist with developing research products that can be shared widely. It's with that in mind that we, as researchers in social science, developed a new database of AI tools for the field. In the database, we compiled information about each tool and documented whether it was useful for literature reviews, data collection and analyses, or research dissemination.

This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective of BARKPLUG V.2 is to provide information to users about various campus resources, including academic departments, programs, campus facilities, and student resources at a university setting in an interactive fashion. Our system leverages university data as an external data corpus and ingests it into our RAG pipelines for domain-specific question-answering tasks. We evaluate the effectiveness of our system in generating accurate and pertinent responses for Mississippi State University, as a case study, using quantitative measures, employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS). Furthermore, we evaluate the usability of this system via subjective satisfaction surveys using the System Usability Scale (SUS). Our system demonstrates impressive quantitative performance, with a mean RAGAS score of 0.96, and experience, as validated by usability assessments.

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.

From crop dusting to package delivery, commercial drones are about to become a part of everyday life. "Just in the last 18 months, we've registered twice as many unmanned aircraft (as) we registered all aircraft from the previous 100 years," said Earl Lawrence, director of the Federal Aviation Administration's Unmanned Aircraft Systems Integration Office. To safely integrate the vast numbers of new unmanned aircraft systems (UAS) into the nation's airspace, the FAA is relying on a group of 23 research institutions led by Mississippi State University. The Alliance for System Safety of UAS through Research Excellence (ASSURE) is conducting in-depth studies on virtually every aspect of drone operations, including air traffic control, pilot certification and crash avoidance. "What happens when a drone hits a wing or a windshield or any other part of the aircraft is (one) of our key questions," Lawrence said.

A team at Mississippi State University is suggesting using robots to question children in investigations of child abuse. But not everyone is convinced. Children's accounts are often vital evidence in cases of abuse. But even specially trained police interviewers can find it tough to stay neutral when talking to children. This can result in leading questions and bad evidence, because children can be very suggestible to saying what they think someone wants to hear.