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In embedding-matching acoustic-to-word (A2W) ASR, every word in the vocabulary is represented by a fixed-dimension embedding vector that can be added or removed independently of the rest of the system. The approach is potentially an elegant solution for the dynamic out-of-vocabulary (OOV) words problem, where speaker- and context-dependent named entities like contact names must be …
Posted by Yu Zhang, Research Scientist, and James Qin, Software Engineer, Google Research Last November, we announced the 1,000 Languages Initiative, an ambitious commitment to build a machine learning (ML) model that would support the world’s one thousand most-spoken languages, bringing greater inclusion to billions of people around the globe. However, some of these languages …
Read more “Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages”
Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions (BERT) to over a trillion parameters (MiCS), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and …
Read more “Training large language models on Amazon SageMaker: Best practices”
As technology advances, it’s essential for developers, students and educators to stay ahead of the curve through continuous learning. This is especially true for those interested in AI, high performance computing and the metaverse, as these technologies evolve fast. Beginners, experts and everyone in between can advance their technical skills in these fields by attending …
Read more “From Basics to Mastery: How to Advance AI, HPC and Metaverse Technical Skills”
(Scroll down for English translation below) Wie können wir Polizeibehörden ermöglichen, den Herausforderungen der digitalen Zeit besser gerecht zu werden? Wie können wir gleichzeitig Grundrechte wahren? Dies sind Fragen, mit denen sich die Innenministerien des Bundes und der Länder bereits seit 2016 im Rahmen der sogenannten Saarbrücker Agenda beschäftigen. Konkret geht es darum, eine “moderne, …
Read more “Wie Palantir die Digitalisierung deutscher Polizeien unterstützt”
Posted by Krzysztof Choromanski, Staff Research Scientist, Robotics at Google, and Xuesu Xiao, Visiting Researcher, George Mason University Despite decades of research, we don’t see many mobile robots roaming our homes, offices, and streets. Real-world robot navigation in human-centric environments remains an unsolved problem. These challenging situations require safe and efficient navigation through tight spaces, …
Read more “Performer-MPC: Navigation via real-time, on-robot transformers”
Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should …
Read more “Index your Microsoft Exchange content using the Exchange connector for Amazon Kendra”
Editors Note: Written by Courtney Bowman, Palantir’s Global Director of Privacy and Civil Liberties Engineering, this blog highlights our belief in the need for an integrated and operationally-orientated approach to artificial intelligence, which acknowledges its limitations, while placing ethics and efficacy at the heart of its use. You can read more about our principled approach …
Read more “The Efficacy and Ethics of AI Must Move Beyond the Performative to the Operational”
Posted by Florian Hartmann, Software Engineer, and Peter Kairouz, Research Scientist, Google Research Federated learning is a distributed way of training machine learning (ML) models where data is locally processed and only focused model updates and metrics that are intended for immediate aggregation are shared with a server that orchestrates training. This allows the training …
Read more “Distributed differential privacy for federated learning”
Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to help separate out the relevant and meaningful facts and figures. However, the same piece of news can have a positive or negative impact on stock prices, which presents a challenge for …