Optimizing Byte-level Representation for End-to-End ASR

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

In this paper, we propose an algorithm to optimize a byte-level representation for end-to-end (E2E) automatic speech recognition (ASR). Byte-level representation is often used by large scale multilingual ASR systems when the character set of the supported languages is large. The compactness and universality of byte-level representation allow the ASR models to use smaller output …

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Best practices for prompt engineering with Meta Llama 3 for Text-to-SQL use cases

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. This includes Meta Llama 3, Meta’s publicly available large language model (LLM). The partnership between Meta and Amazon signifies collective generative AI innovation, and Meta and Amazon are working together …

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GenOps: learning from the world of microservices and traditional DevOps

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Who is supposed to manage generative AI applications? While AI-related ownership often lands with data teams, we’re seeing requirements specific to generative AI applications that have distinct differences from those of a data and AI team, and at times more similarities with a DevOps team. This blog post explores these similarities and differences, and considers …

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Transparency is often lacking in datasets used to train large language models, study finds

by AI Generated Robotic ContentAI/ML NewsPosted on Comments are Disabled

In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources. But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost or confounded in the shuffle.

5 Influential Machine Learning Papers You Should Read

by AI Generated Robotic ContentAI/ML ResearchPosted on Comments are Disabled

In recent years, machine learning has experienced a profound transformation with the emergence of LLMs and new techniques that improved the domain’s state of the art. Most of these advancements have mainly been initially revealed in research papers, which have introduced new techniques while reshaping our understanding and approach to the domain. The number of …

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Apple Workshop on Privacy-Preserving Machine Learning 2024

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At Apple, we believe privacy is a fundamental human right. It’s also one of our core values, influencing both our research and the design of Apple’s products and services. Understanding how people use their devices often helps in improving the user experience. However, accessing the data that provides such insights — for example, what users …

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Accelerate Generative AI Inference with NVIDIA NIM Microservices on Amazon SageMaker

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This post is co-written with Eliuth Triana, Abhishek Sawarkar, Jiahong Liu, Kshitiz Gupta, JR Morgan and Deepika Padmanabhan from NVIDIA.  At the 2024 NVIDIA GTC conference, we announced support for NVIDIA NIM Inference Microservices in Amazon SageMaker Inference. This integration allows you to deploy industry-leading large language models (LLMs) on SageMaker and optimize their performance and …

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A developer’s guide to getting started with Imagen 3 on Vertex AI

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Over the past few months, early users put Imagen 3 on Vertex AI through its paces and shared valuable insights with us. It’s clear that users want an AI model that generates stunning visuals and empowers your practical creative applications. We’ve used their feedback to identify three common themes: Demand for unparalleled quality across diverse …

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