​[[{“value”:” AWS offers powerful generative AI services, including Amazon Bedrock, which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. Many businesses want to integrate these cutting-edge AI… Read More »Create a generative AI–powered custom Google Chat application using Amazon Bedrock Nizar Kheir AWS Machine Learning Blog

​[[{“value”:” A number of organizations use Gmail for their business email needs. Gmail for business is part of Google Workspace, which provides a set of productivity and collaboration tools like Google Drive, Gmail, and Google Calendar. Google Drive supports storing documents such as Emails contain… Read More »Discover insights from Gmail using the Gmail connector for Amazon Q Business Divyajeet Singh AWS Machine Learning Blog

​[[{“value”:” Amazon SageMaker Ground Truth enables the creation of high-quality, large-scale training datasets, essential for fine-tuning across a wide range of applications, including large language models (LLMs) and generative AI. By integrating human annotators with machine learning, SageMaker Ground Truth significantly reduces the cost and… Read More »Accelerate custom labeling workflows in Amazon SageMaker Ground Truth without using AWS Lambda Sundar Raghavan AWS Machine Learning Blog

​Sponsored Content From November 1st to November 21st, 2024 (8:00 am UTC), 365 Data Science offers free access to its comprehensive learning platform. This is… Sponsored Content From November 1st to November 21st, 2024 (8:00 am UTC), 365 Data Science offers free access to its comprehensive… Read More »Free Data and AI Courses with 365 Data Science—Unlimited Access until Nov 21 MLM Team MachineLearningMastery.com

​[[{“value”:” The widespread adoption of large language models (LLMs) has ushered in significant advancements across fields such as conversational AI, content generation, and on-device applications. However, the heavy reliance on extensive cloud resources to deploy these models raises concerns about latency, cost, and environmental sustainability.… Read More »Meta AI Releases MobileLLM 125M, 350M, 600M and 1B Model Checkpoints Asif Razzaq Artificial Intelligence Category – MarkTechPost

​Python is the most popular data science programming language, as it’s versatile and has a lot of support from the community. Python is the most popular data science programming language, as it’s versatile and has a lot of support from the community.  Read More  

​[[{“value”:” Large language models (LLMs) rely on deep learning architectures that capture complex linguistic relationships within layered structures. Primarily based on Transformer architectures, these models are increasingly deployed across industries for tasks that require nuanced language understanding and generation. However, the demands of large Transformer… Read More »Relaxed Recursive Transformers with Layer-wise Low-Rank Adaptation: Achieving High Performance and Reduced Computational Cost in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have revolutionized software development by enabling code completion, functional code generation from instructions, and complex code modifications for bug fixes and feature implementations. While these models excel at generating code from natural language instructions, significant challenges persist in evaluating the… Read More »CodeFavor: A Machine Learning Framework that Trains Pairwise Preference Models with Synthetic Code Preferences Generated from Code Evolution like Code Commits and Code Critiques Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Task planning in language agents is gaining attention in LLM research, focusing on breaking complex tasks into manageable sub-tasks arranged in a graph format, with nodes as tasks and edges as dependencies. The study explores task planning challenges in LLMs, such as HuggingGPT, which… Read More »Enhancing Task Planning in Language Agents: Leveraging Graph Neural Networks for Improved Task Decomposition and Decision-Making in Large Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Deep learning has made advances in various fields, and it has made its way into material sciences as well. From tasks like predicting material properties to optimizing compositions, deep learning has accelerated material design and facilitated exploration in expansive materials spaces. However, explainability is… Read More »XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science Afeerah Naseem Artificial Intelligence Category – MarkTechPost