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Conversational AI use cases for enterprises

IBM Journey to AI blog

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. This sophisticated foundation propels conversational AI from a futuristic concept to a practical solution.

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This AI Paper Unveils the Potential of Speculative Decoding for Faster Large Language Model Inference: A Comprehensive Analysis

Marktechpost

Large Language Models (LLMs) are crucial to maximizing efficiency in natural language processing. These models, central to various applications ranging from language translation to conversational AI, face a critical challenge in the form of inference latency.

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Salesforce Research Introduces AgentOhana: A Comprehensive Agent Data Collection and Training Pipeline for Large Language Model

Marktechpost

Integrating Large Language Models (LLMs) in autonomous agents promises to revolutionize how we approach complex tasks, from conversational AI to code generation. Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. Check out the Paper.

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Researchers at Apple Propose ReDrafter: Changing Large Language Model Efficiency with Speculative Decoding and Recurrent Neural Networks

Marktechpost

The development and refinement of large language models (LLMs) mark a significant step in the progress of machine learning. These sophisticated algorithms, designed to mimic human language, are at the heart of modern technological conveniences, powering everything from digital assistants to content creation tools.

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How Does Machine Learning Scale to New Peaks? This AI Paper from ByteDance Introduces MegaScale: Revolutionizing Large Language Model Training with Over 10,000 GPUs

Marktechpost

Large language models (LLMs) stand out for their astonishing ability to mimic human language. These models, pivotal in advancements across machine translation, summarization, and conversational AI, thrive on vast datasets and equally enormous computational power. Check out the Paper.

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Evaluate conversational AI agents with Amazon Bedrock

AWS Machine Learning Blog

However, the dynamic and conversational nature of these interactions makes traditional testing and evaluation methods challenging. Conversational AI agents also encompass multiple layers, from Retrieval Augmented Generation (RAG) to function-calling mechanisms that interact with external knowledge sources and tools.

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How MLOps Work in the Era of Large Language Models

ODSC - Open Data Science

Large language models (LLMs) and generative AI have taken the world by storm, allowing AI to enter the mainstream and show that AI is real and here to stay. However, a new paradigm has entered the chat, as LLMs don’t follow the same rules and expectations of traditional machine learning models.