Tag Archives: Machine Learning

GPTs are GPTs: An early look at the labor market impact potential of large language models

https://openaicom.imgix.net/b5b6da55-f60d-43b3-a40f-902b9baf57e0/gpts-are-gpts-an-early-look-at-the-labor-market-impact-potential-of-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=%2C%2C%2C Original Source Here AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

GPTs are GPTs: An early look at the labor market impact potential of large language models

https://openaicom.imgix.net/b5b6da55-f60d-43b3-a40f-902b9baf57e0/gpts-are-gpts-an-early-look-at-the-labor-market-impact-potential-of-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=%2C%2C%2C Original Source Here AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

Original Source Here This is a guest post co-written with Antony Vance from Intel. Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. Running ML workloads on Amazon SageMaker runningContinue reading “Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances”

Large Language Models as Zero-shot Labelers

https://miro.medium.com/v2/resize:fit:1200/0*SPaExWlaJOerB1st Original Source Here Large Language Models as Zero-shot Labelers Using LLMs to obtain labels for supervised models Photo by h heyerlein on Unsplash Introduction Labeling data is a critical step in building supervised machine learning models, as the quantity and quality of labels is often the main factor that determines model performance. However, labelingContinue reading “Large Language Models as Zero-shot Labelers”