Robotics & Machine Learning Daily News2024,Issue(Jul.2) :162-165.

Researchers Submit Patent Application, 'Systems And Methods For Generating Impro ved Process Management Using A Bifurcated Model To Generate Synthetic Sets Of Pr ocessing Steps', for Approval (USPTO 20240193465)

研究人员提交专利申请“使用分叉模型生成合成加工步骤集的改进过程管理的系统和方法”以供批准(USPTO 20240193465)

Robotics & Machine Learning Daily News2024,Issue(Jul.2) :162-165.

Researchers Submit Patent Application, 'Systems And Methods For Generating Impro ved Process Management Using A Bifurcated Model To Generate Synthetic Sets Of Pr ocessing Steps', for Approval (USPTO 20240193465)

研究人员提交专利申请“使用分叉模型生成合成加工步骤集的改进过程管理的系统和方法”以供批准(USPTO 20240193465)

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摘要

新闻编辑从发明者提供的背景信息中获得了以下引文:“机器学习模型和人工智能已经适应了许多需要人类智能的日常过程。利用人工智能(例如机器学习、深度学习等)的最大优势是。”是快速处理数据和迅速作出决定的能力。然而,尽管机器l盈利模型的速度和准确性有所提高,一些技术问题限制了它们应用于公共应用的能力。一个主要的技术问题是机器学习模型s由于缺乏高质量的训练数据或模型参数拟合不好而在它们所做的决定中表现出偏差。这个问题在数据稀疏的环境中进一步加剧。这个技术问题在将人工智能应用于实际应用时产生了一个问题,例如:这些与产生改进的处理途径有关。

Abstract

News editors obtained the following quote from the background information suppli ed by the inventors: “Machine learning models and artificial intelligence have b een adapted to improve many everyday processes that require human intelligence. The greatest advantage to utilizing artificial intelligence (e.g., machine learn ing, deep learning, etc.) is the ability to quickly process data and swiftly mak e determinations. However, despite the increased speed and accuracy of machine l earning models, some technical problems limit their ability to be applied for pr actical applications. One major technical problem is that machine learning model s may exhibit bias in the determinations that they make due to a lack of high-qu ality training data or poorly fitted model parameters. This problem is further e xacerbated in data-sparse environments. This technical problem creates an issue when using artificial intelligence for practical applications such as those rela ted to generating improved processing pathways.”

Key words

Algorithms/Artificial Intelligence/Bus iness/Cyborgs/Devguild LLC/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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