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OpenAI Releases Multilingual Massive Multitask Language Understanding (MMMLU) Dataset on Hugging Face to Easily Evaluate Multilingual LLMs Asif Razzaq Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” OpenAI released the Multilingual Massive Multitask Language Understanding (MMMLU) dataset on Hugging Face. As language models grow increasingly powerful, the necessity of evaluating their capabilities across diverse linguistic, cognitive, and cultural contexts has become a pressing concern. OpenAI’s decision to introduce the MMMLU dataset… Read More »OpenAI Releases Multilingual Massive Multitask Language Understanding (MMMLU) Dataset on Hugging Face to Easily Evaluate Multilingual LLMs Asif Razzaq Artificial Intelligence Category – MarkTechPost

CALM: Credit Assignment with Language Models for Automated Reward Shaping in Reinforcement Learning Asif Razzaq Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Reinforcement Learning (RL) is a critical area of ML that allows agents to learn from their interactions within an environment by receiving feedback as rewards. A significant challenge in RL is solving the temporal credit assignment problem, which refers to determining which actions in… Read More »CALM: Credit Assignment with Language Models for Automated Reward Shaping in Reinforcement Learning Asif Razzaq Artificial Intelligence Category – MarkTechPost

Speculative Streaming: Fast LLM Inference Without Auxiliary Models Apple Machine Learning Research

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​Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As… Read More »Speculative Streaming: Fast LLM Inference Without Auxiliary Models Apple Machine Learning Research

Trust-Align: An AI Framework for Improving the Trustworthiness of Retrieval-Augmented Generation in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

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​[[{“value”:” Large language models (LLMs) have gained significant attention due to their potential to enhance various artificial intelligence applications, particularly in natural language processing. When integrated into frameworks like Retrieval-Augmented Generation (RAG), these models aim to refine AI systems’ output by drawing information from external… Read More »Trust-Align: An AI Framework for Improving the Trustworthiness of Retrieval-Augmented Generation in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

Be Part of the AI Revolution at the Chatbot Conference Tomorrow! Cassandra C. Becoming Human: Artificial Intelligence Magazine – Medium

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​ Tomorrow, September 24, 2024, San Francisco will host one of the biggest global AI events of the year: the Chatbot Conference! Whether you’re passionate about artificial intelligence, curious about chatbots, or simply eager to connect with industry leaders, this conference is for you. Why You… Read More »Be Part of the AI Revolution at the Chatbot Conference Tomorrow! Cassandra C. Becoming Human: Artificial Intelligence Magazine – Medium

Generate synthetic data for evaluating RAG systems using Amazon Bedrock Lukas Wenzel AWS Machine Learning Blog

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​[[{“value”:” Evaluating your Retrieval Augmented Generation (RAG) system to make sure it fulfils your business requirements is paramount before deploying it to production environments. However, this requires acquiring a high-quality dataset of real-world question-answer pairs, which can be a daunting task, especially in the early… Read More »Generate synthetic data for evaluating RAG systems using Amazon Bedrock Lukas Wenzel AWS Machine Learning Blog

Making traffic lights more efficient with Amazon Rekognition Colin Chu AWS Machine Learning Blog

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​[[{“value”:” State and local agencies spend approximately $1.23 billion annually to operate and maintain signalized traffic intersections. On the other end, traffic congestion at intersections costs drivers about $22 billion annually. Implementing an artificial intelligence (AI)-powered detection-based solution can significantly mitigate congestion at intersections and… Read More »Making traffic lights more efficient with Amazon Rekognition Colin Chu AWS Machine Learning Blog

Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio James Wu AWS Machine Learning Blog

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​[[{“value”:” Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and… Read More »Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio James Wu AWS Machine Learning Blog