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​[[{“value”:” Employee productivity is a critical factor in maintaining a competitive advantage. Amazon Q Business offers a unique opportunity to enhance workforce efficiency by providing AI-powered assistance that can significantly reduce the time spent searching for information, generating content, and completing routine tasks. Amazon Q… Read More »Boost team productivity with Amazon Q Business Insights Guillermo Mansilla AWS Machine Learning Blog

​[[{“value”:” Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. The multi-LLM approach enables organizations to… Read More »Multi-LLM routing strategies for generative AI applications on AWS Nima Seifi AWS Machine Learning Blog

​Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance. Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding… Read More »How to Perform Scikit-learn Hyperparameter Optimization with Optuna Iván Palomares Carrascosa MachineLearningMastery.com

​[[{“value”:” Headquartered in São Paulo, Brazil, iFood is a national private company and the leader in food-tech in Latin America, processing millions of orders monthly. iFood has stood out for its strategy of incorporating cutting-edge technology into its operations. With the support of AWS, iFood… Read More »How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference Daniel Vieira AWS Machine Learning Blog

​Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning. Nowadays, everyone across AI and related communities talks about generative AI… Read More »5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs Iván Palomares Carrascosa MachineLearningMastery.com

​[[{“value”:” The AI landscape is rapidly evolving, and more organizations are recognizing the power of synthetic data to drive innovation. However, enterprises looking to use AI face a major roadblock: how to safely use sensitive data. Stringent privacy regulations make it risky to use such… Read More »Build an enterprise synthetic data strategy using Amazon Bedrock Devi Nair AWS Machine Learning Blog

​[[{“value”:” Robots are increasingly being developed for home environments, specifically to enable them to perform daily activities like cooking. These tasks involve a combination of visual interpretation, manipulation, and decision-making across a series of actions. Cooking, in particular, is complex for robots due to the… Read More »This AI Paper Introduces an LLM+FOON Framework: A Graph-Validated Approach for Robotic Cooking Task Planning from Video Instructions Nikhil Artificial Intelligence Category – MarkTechPost

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PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models.

The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding?

In PLAID, we develop a method that learns to sample from the latent space of protein folding models to generate new proteins. It can accept compositional function and organism prompts, and can be trained on sequence databases, which are 2-4 orders of magnitude larger than structure databases. Unlike many previous protein structure generative models, PLAID addresses the multimodal co-generation problem setting: simultaneously generating both discrete sequence and continuous all-atom structural coordinates.

Read More »Repurposing Protein Folding Models for Generation with Latent Diffusion The Berkeley Artificial Intelligence Research Blog