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A report from Diponegoro University discusses the use of artificial intelligence (AI) and deep learning in medical imaging to identify kidney stones. The research highlights that deep learning is more commonly used than traditional machine learning in this field. The aim of the study was to review existing literature on deep learning in medical imaging for detecting kidney stones. The research concludes that using the appropriate AI model with high accuracy can assist radiologists in accurately detecting kidney stones. This information may be useful for those interested in AI applications in medical diagnostics. [Extracted from the article]

HEALWELL AI announced its subsidiary Pentavere Research Group has achieved a significant milestone with the publication of a paper validating the use of generative AI to identify rare lung cancer [...]

This article provides an overview of the scientific paper "Attention Is All You Need" written by Google researchers in 2017. The paper introduces a new AI architecture called transformers, which has had a significant impact on the field of artificial intelligence. The authors challenged the traditional ranking of contributors by listing themselves as equal contributors. The paper gained widespread recognition, and the authors have become well-known figures in the field. The article also discusses the development of transformer models, the challenges faced by the team, and the subsequent founding of new companies by some of the authors. It emphasizes the importance of a supportive and innovative environment for groundbreaking research. [Extracted from the article]

IN A meeting room at the Royal Society in London, several dozen graduate students were recently tasked with outwitting a large language model (LLM), a type of AI designed to [...]

Precision agriculture tools like decision support systems increasingly use machine‐learning algorithms and other types of artificial intelligence (AI) to analyze large quantities of agricultural data and provide recommendations to producers and crop advisers. However, several barriers threaten adoption of these tools. Three papers in the recent Agronomy Journal special section, "Machine Learning in Agriculture," explore this phenomenon and offer solutions and opportunities for building trust in these technologies. [ABSTRACT FROM AUTHOR]

2024 FEB 22 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on artificial intelligence is now available. According to news reporting out […]

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

A new paper in Nature Biotechnology offers the first in-depth look at how Insilico Medicine used AI to discover, develop and test its lead drug. [ABSTRACT FROM AUTHOR]