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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

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NLP News Cypher | 08.09.20

Towards AI

Photo by Kunal Shinde on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 08.09.20 What is the state of NLP? Deep learning and semantic parsing, do we still care about information extraction? For an overview of some tasks, see NLP Progress or our XTREME benchmark.

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction.

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AI code-generation software: What it is and how it works

IBM Journey to AI blog

Auto-generated code suggestions can increase developers’ productivity and optimize their workflow by providing straightforward answers, handling routine coding tasks, reducing the need to context switch and conserving mental energy. It can also modernize legacy code and translate code from one programming language to another.

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Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

AWS Machine Learning Blog

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. Hugging Face is a popular open source hub for machine learning (ML) models. Prerequisites Complete the following prerequisites: Create a SageMaker domain.

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Improve performance of Falcon models with Amazon SageMaker

AWS Machine Learning Blog

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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Improve throughput performance of Llama 2 models using Amazon SageMaker

AWS Machine Learning Blog

This is because a large portion of the available memory bandwidth is consumed by loading the model’s parameters and by the auto-regressive decoding process.As Then we highlight how Amazon SageMaker large model inference (LMI) deep learning containers (DLCs) can help with these techniques. Use MPI to enable continuous batching.