An opportunity agenda for AI
Favorite Today we’re sharing an AI Opportunity Agenda to provide concrete policy recommendations to help AI benefit as many people as possible. View Original Source (blog.google/technology/ai/) Here.
Improve LLM responses in RAG use cases by interacting with the user
Favorite One of the most common applications of generative AI and large language models (LLMs) is answering questions based on a specific external knowledge corpus. Retrieval-Augmented Generation (RAG) is a popular technique for building question answering systems that use an external knowledge base. To learn more, refer to Build a
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Shared by AWS Machine Learning November 14, 2023
Implement real-time personalized recommendations using Amazon Personalize
Favorite At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses to use data to derive actionable insights and help grow their revenue and brand loyalty. Amazon Personalize
Read More Shared by AWS Machine Learning November 14, 2023
Taking legal action to protect users of AI and small businesses
Favorite Today we’re taking legal action against two groups of scammers. View Original Source (blog.google/technology/ai/) Here.
Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning
Favorite Creating high-performance machine learning (ML) solutions relies on exploring and optimizing training parameters, also known as hyperparameters. Hyperparameters are the knobs and levers that we use to adjust the training process, such as learning rate, batch size, regularization strength, and others, depending on the specific model and task at
Read More Shared by AWS Machine Learning November 11, 2023
Use machine learning without writing a single line of code with Amazon SageMaker Canvas
Favorite In the recent past, using machine learning (ML) to make predictions, especially for data in the form of text and images, required extensive ML knowledge for creating and tuning of deep learning models. Today, ML has become more accessible to any user who wants to use ML models to
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Build trust and safety for generative AI applications with Amazon Comprehend and LangChain
Favorite We are witnessing a rapid increase in the adoption of large language models (LLM) that power generative AI applications across industries. LLMs are capable of a variety of tasks, such as generating creative content, answering inquiries via chatbots, generating code, and more. Organizations looking to use LLMs to power
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Enabling large-scale health studies for the research community
Favorite Posted by Chintan Ghate, Software Engineer, and Diana Mincu, Research Engineer, Google Research As consumer technologies like fitness trackers and mobile phones become more widely used for health-related data collection, so does the opportunity to leverage these data pathways to study and advance our understanding of medical conditions. We
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Shared by Google AI Technology November 10, 2023
How we taught Google Translate to recognize homonyms
Favorite How Google Translate’s neural model taught it to understand bass from bass. View Original Source (blog.google/technology/ai/) Here.
UNDP KM strategy
Favorite Linked below is an excellent video on the 2022 KM strategy from the UN Development program. Good to see the focus on culture and networks. View Original Source (nickmilton.com) Here.