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ChatGPT, the rise of generative AI

CIO

Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. ChatGPT was trained with 175 billion parameters; for comparison, GPT-2 was 1.5B (2019), Google’s LaMBDA was 137B (2021), and Google’s BERT was 0.3B (2018). Learn more about Protiviti’s Artificial Intelligence Services.

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. The following diagram illustrates the solution architecture. The following are some example prompts: Create a new claim.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Build knowledge-powered conversational applications using LlamaIndex and Llama 2-Chat

AWS Machine Learning - AI

RAG allows models to tap into vast knowledge bases and deliver human-like dialogue for applications like chatbots and enterprise search assistants. Solution overview In this post, we demonstrate how to create a RAG-based application using LlamaIndex and an LLM. Download press releases to use as our external knowledge base.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

Generative artificial intelligence (AI) with Amazon Bedrock directly addresses these challenges. With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts. Amazon Bedrock generates Terraform code from architectural descriptions.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning - AI

Solution architecture The following diagram illustrates the solution architecture. Diagram 1: Solution Architecture Overview The agent’s response workflow includes the following steps: Users perform natural language dialog with the agent through their choice of web, SMS, or voice channels.