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This manuscript explores the use of Spanish language translators in an outpatient clinic in New Jersey. The authors surveyed patients before and after in person, video, and telephone appointments to determine which modality was most acceptable to the patients. The authors found that the three modalities did not differ in patient satisfaction, but that patients were grateful for translations services and that patient trust may be expanded by the use of these services and by focuses of translator soft skills.

Diagnosing of Autism Spectrum Disorder (ASD) using tools developed in the West is challenging in the Indian setting due to a huge diversity in sociocultural and economic backgrounds. Here, the authors developed a home-based, audiovisual game app (Autest) suitable for ASD risk assessment in Indian children under 10 years of age. Ratings suggested that the tool is effective and can reduce social inhibition and facilitate assessment. Further usage and development of Autest can improve risk assessment and early intervention measures for children with ASD in India.

In cognitive psychology, typed responses are used to assess thinking skills and creativity, but research on factors influencing typing speed is limited. This study examined how language familiarity affects typing speed, hypothesizing that familiarity with a language would correlate with faster typing. Participants typed faster in English than Latin, with those unfamiliar with Latin showing a larger discrepancy between the two languages, though Latin education level did not significantly impact typing speed, highlighting the role of language familiarity in typing performance.

Authors examine the effectiveness of Large Language Models (LLMs) like BERT, MathBERT, and OpenAI GPT-3.5 in assisting middle school students with math word problems, particularly following the decline in math performance post-COVID-19.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.

The Aromanian language and culture is quickly declining towards extinction. In this new research article, Ganea and Lascu provide evidence that, although the use of the Aromanian language is less prevalent among younger individuals, participants overwhelming support the preservation of Aromanian language and culture.

Here, the authors investigated the integration of large language models (LLMs) with drug target affinity predictors (DTAPs) to improve drug repurposing, demonstrating a significant increase in prediction accuracy, particularly with GPT-4, for psychotropic drugs and the sigma-1 receptor. This novel approach offers to potentially accelerate and reduce the cost of drug discovery by efficiently identifying new therapeutic uses for existing drugs.

Middle school math forms the basis for advanced mathematical courses leading up to the university level. Large language models (LLMs) have the potential to power next-generation educational technologies, acting as digital tutors to students. The main objective of this study was to determine whether LLMs like ChatGPT, Bard, and Llama 2 can serve as reliable middle school math tutoring assistants on three tutoring tasks: hint generation, comprehensive solution, and exercise creation.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.