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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. Transformers and Advanced NLP Models : The introduction of transformer architectures revolutionized the NLP landscape.

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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? For an overview of some tasks, see NLP Progress or our XTREME benchmark. In the next post, I will outline interesting research directions and opportunities in multilingual NLP.”

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

Unite.AI

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

In zero-shot learning, no examples of task completion are provided in the model. Chain-of-thought Prompting Chain-of-thought prompting leverages the inherent auto-regressive properties of large language models (LLMs), which excel at predicting the next word in a given sequence.

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Best Large Language Models & Frameworks of 2023

AssemblyAI

Original natural language processing (NLP) models were limited in their understanding of language. It offers a simple API for applying LLMs to up to 100 hours of audio data, even exposing endpoints for common use tasks It's smart enough to auto-generate subtitles, identify speakers, and transcribe audio in real time.

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TimesFM — Google’s Foundational Model for Time Series Forecasting

Towards AI

Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently. Generating Longer Forecast Output Patches In Large Language Models (LLMs), output is generally produced in an auto-regressive manner, generating one token at a time. However, there is a trade-off.

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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

Sentiment analysis and other natural language programming (NLP) tasks often start out with pre-trained NLP models and implement fine-tuning of the hyperparameters to adjust the model to changes in the environment. script will create the VPC, subnets, auto scaling groups, the EKS cluster, its nodes, and any other necessary resources.

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