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Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI…

ODSC - Open Data Science

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Various prompting techniques, such as Zero/Few Shot, Chain-of-Thought (CoT)/Self-Consistency, ReAct, etc.

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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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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

GPT-4: Prompt Engineering ChatGPT has transformed the chatbot landscape, offering human-like responses to user inputs and expanding its applications across domains – from software development and testing to business communication, and even the creation of poetry. Imagine you're trying to translate English to French.

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Empowering Model Sharing, Enhanced Annotation, and Azure Blob Backups in NLP Lab

John Snow Labs

We are thrilled to release NLP Lab 5.4 which brings a host of exciting enhancements to further empower your NLP journey. Publish Models Directly into Models HUB We’re excited to introduce a streamlined way to publish NLP models to the NLP Models HUB directly from NLP Lab.

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MetaGPT: Complete Guide to the Best AI Agent Available Right Now

Unite.AI

Agile Development SOPs act as a meta-function here, coordinating agents to auto-generate code based on defined inputs. In simple terms, it's as if you've turned a highly coordinated team of software engineers into an adaptable, intelligent software system.

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. For example, metadata can be used for filtering and searching. append(e["Text"].upper())

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Use Amazon Titan models for image generation, editing, and searching

AWS Machine Learning Blog

Mask prompt – A mask prompt is a natural language text description of the elements you want to affect, that uses an in-house text-to-segmentation model. For more information, refer to Prompt Engineering Guidelines. To remove an element, omit the text parameter completely. Parse and decode the response.