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Generative AI to quantify uncertainty in weather forecasting

Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of hazardous weather conditions. The importance …

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Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center

Amazon Lex provides advanced conversational artificial intelligence (AI) capabilities to enable self-service support for your organization’s contact center. With Amazon Lex, you can implement an omnichannel strategy where customers engage via phone, websites, and messaging platforms. The bots can answer FAQs, provide self-service experiences, or triage customer requests before transferring to a human agent. Amazon Lex integrates …

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AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks

Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data’s patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian …

Private cloud use cases: 6 ways private cloud brings value to enterprise business

As cloud computing continues to transform the enterprise workplace, private cloud infrastructure is evolving in lockstep, helping organizations in industries like healthcare, government and finance customize control over their data to meet compliance, privacy, security and other business needs.  According to a report from Future Market Insights (link resides outside ibm.com), the global private cloud services …

AutoBNN

AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks

Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data’s patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian …

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Advanced RAG patterns on Amazon SageMaker

Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These generative AI applications are not only …

Greater Scope: Doctors Get Inside Look at Gut Health With AI-Powered Endoscopy

From humble beginnings as a university spinoff to an acquisition by the leading global medtech company in its field, Odin Vision has been on an accelerated journey since its founding less than five years ago. An alum of the NVIDIA Inception program for cutting-edge startups, Odin Vision builds cloud-connected AI software that helps clinicians detect …