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Image recognition

Dataconomy

This technology employs machine vision and artificial intelligence (AI) to decipher visual information, making it indispensable across numerous fields. Artificial Intelligence (AI): The simulation of human intelligence processes by machines. Machine vision: A technology that enables computers to interpret and understand visual data.

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How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

AWS Machine Learning Blog

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. An FM-driven solution can also provide rationale for outputs, whereas a traditional classifier lacks this capability.

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Why high quality data annotation is the backbone of AI training?

Dataconomy

AI doesn’t learn in a bubble. Accuracy, consistency, and context determine how useful they are for training AI models. Supervised learning means training an AI model using examples with labels. If labels are wrong or messy, the model learns the wrong thing. Labeling isn’t just a step in the pipeline.

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Techniques for automatic summarization of documents using language models

Flipboard

Large language models A large language model refers to any model that undergoes training on extensive and diverse datasets, typically through self-supervised learning at a large scale, and is capable of being fine-tuned to suit a wide array of specific downstream tasks.

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How to tackle lack of data: an overview on transfer learning

Data Science Blog

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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The evolution of LLM embeddings: An overview of NLP

Data Science Dojo

Their impact on ML tasks has made them a cornerstone of AI advancements. Hence, while it is helpful to develop a basic understanding of a document, it is limited in forming a connection between words to grasp a deeper meaning. The two main approaches of interest for embeddings include unsupervised and supervised learning.

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How have LLM embeddings evolved to make machines smarter?

Data Science Dojo

Their impact on ML tasks has made them a cornerstone of AI advancements. Read on to understand the role of embeddings in generative AI Let’s take a step back and travel through the journey of LLM embeddings from the start to the present day, understanding their evolution every step of the way.