In the present world, businesses and individuals rely heavily on artificial intelligence, particularly large language models (LLMs), to assist with various tasks. However, these models have significant limitations. One of the main issues is their inability to remember long-term conversations, which makes it difficult to provide consistent and context-aware responses. Additionally, LLMs cannot perform actions… →
The primary goal of AI is to create interactive systems capable of solving diverse problems, including those in medical AI aimed at improving patient outcomes. Large language models (LLMs) have demonstrated significant problem-solving abilities, surpassing human scores on exams like the USMLE. While LLMs can enhance healthcare accessibility, they still face limitations in real-world clinical… →
INTRODUCTION: Pulmonary embolism (PE) is a challenge to diagnose and when missed, exposes patients to potentially fatal recurrent events. Beyond CT pulmonary angiography (CTPA) and planar ventilation/perfusion (V/Q) scan, single photon emission CT (SPECT) V/Q emerged a new diagnostic modality of scintigraphic acquisition that has been reported to improve diagnostic performances. To date, no management… →
Large language models (LLMs), including GPT-4, LLaMA, and PaLM are pushing the boundaries of artificial intelligence. The inference latency of LLMs plays an important role because of LLMs integration in various applications, ensuring a positive user experience and high service quality. However, the LLM service operates within an AR paradigm, generating one token at a… →
Natural language processing (NLP) has advanced significantly thanks to neural networks, with transformer models setting the standard. These models have performed remarkably well across a range of criteria. However, they pose serious problems because of their high memory requirements and high computational expense, particularly for applications that demand long-context work. This persistent problem motivates the… →
The evaluation of artificial intelligence models, particularly large language models (LLMs), is a rapidly evolving research field. Researchers are focused on developing more rigorous benchmarks to assess the capabilities of these models across a wide range of complex tasks. This field is essential for advancing AI technology as it provides insights into the strengths &… →
Conducting functional assessments remotely can help alleviate the burden of in-person assessment on patients with Duchenne muscular dystrophy and their caregivers. The objective of this study was to evaluate whether scores from remote functional assessment of patients with Duchenne muscular dystrophy correspond to in-person scores on the same functional assessments. Remote live stream versus in-person… →
Feedback is crucial for student success, especially in large computing classes facing increasing demand. Automated tools, incorporating analysis techniques and testing frameworks, are gaining popularity but often need more helpful suggestions. Recent advancements in large language models (LLMs) show promise in offering rapid, human-like feedback. However, concerns about the accuracy, reliability, and ethical implications of… →
Large Language Models (LLMs) like GPT 3.5 and GPT 4 have recently gained a lot of attention in the Artificial Intelligence (AI) community. These models are made to process enormous volumes of data, identify patterns, and produce language that resembles that of a human being in response to cues. One of their primary characteristics is… →
Recently, there’s been a surge in the adoption of Integrated Speech and Large Language Models (SLMs), which can understand spoken commands and generate relevant text responses. However, concerns linger regarding their safety and robustness. LLMs, with their extensive capabilities, raise the need to address potential harm and guard against misuse by malicious users. Although developers… →