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Utilizing Machine Learning and Process-Based Models for Estimating Soil Organic Carbon: An Analytical Comparison and the Function of ChatGPT in Soil Science Studies

Machine learning (ML) algorithms have increasingly found use in ecological modelling, including the prediction of Soil Organic Carbon (SOC), a critical component for soil health. However, their application in smaller datasets characteristic of long-term soil research still needs further exploration, notably in comparison with traditional process-based models. A study conducted in Austria compared the performance…

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Assisting beginners in creating sophisticated generative AI models

Artificial intelligence (AI) models today have become increasingly complex with billions of parameters. Existing AI models are largely inaccessible to many due to a lack of widespread knowledge of how to create and control them. MosaicML, a company co-founded by Jonathan Frankle PhD '23 and MIT Associate Professor Michael Carbin, strives to overcome this issue.…

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Administer private hub access for Amazon SageMaker JumpStart base models.

Amazon SageMaker JumpStart is a machine learning (ML) platform that offers pre-built solutions and pre-trained models. The platform provides access to hundreds of foundation models (FMs) for various enterprise operations. A critical feature of SageMaker JumpStart is the private hub, which allows an organization to share their models, thereby facilitating the discovery and widespread use…

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CS-Bench: A Dual-language (Chinese-English) Standard for Assessing the Efficiency of LLMs in the Field of Computer Science.

Artificial Intelligence (AI) continues to evolve rapidly, with large language models (LLMs) demonstrating vast potential across diverse fields. However, optimizing the potential of LLMs in the field of computer science has been a challenge due to the lack of comprehensive assessment tools. Researchers have conducted studies within computer science, but they often either broadly evaluate…

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Reducing Memory Reliance in Language Models: The Goldfish Loss Method

Language learning models (LLMs) are capable of memorizing and reproducing their training data, which can create substantial privacy and copyright issues, particularly in commercial environments. These concerns are especially important for models that generate code as they may unintentionally reuse code snippets verbatim, thereby conflicting with licensing terms that restrict commercial use. Moreover, models may…

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CS-Bench: A Dual-Language (Chinese-English) Standard for Assessing the Effectiveness of Language Models in Computer Science

The realm of artificial intelligence has been widely influenced by the emergence of large language models (LLMs), with their potential being seen across multiple fields. However, the task of enabling these models to efficiently utilize knowledge of computer science and to benefit humanity remains a challenge. Although many studies have been conducted across various disciplines,…

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