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Google AI Researchers Propose ‘MODEL SWARMS’: A Collaborative Search Algorithm to Flexibly Adapt Diverse LLM Experts to Wide-Ranging Purposes

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Upcoming Live Webinar- Oct 29, 2024] The Best Platform for Serving Fine-Tuned Models: Predibase Inference Engine (Promoted) The post Google AI Researchers Propose ‘MODEL SWARMS’: A Collaborative Search Algorithm to Flexibly Adapt Diverse LLM Experts to Wide-Ranging Purposes appeared first on MarkTechPost.

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Google AI Research Examines Random Circuit Sampling (RCS) for Evaluating Quantum Computer Performance in the Presence of Noise

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Evaluating the performance of quantum computers has been a challenging task due to their sensitivity to noise, the complexity of quantum algorithms, and the limited availability of powerful quantum hardware. Researchers have made several attempts to analyze how noise affects the ability of quantum computers to perform useful computations.

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Meta AI Researchers Introduce Token-Level Detective Reward Model (TLDR) to Provide Fine-Grained Annotations for Large Vision Language Models

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These approaches typically involve training reward models on human preference data and using algorithms like Proximal Policy Optimization (PPO) or Direct Policy Optimization (DPO) for policy learning. If you like our work, you will love our newsletter. Don’t Forget to join our 55k+ ML SubReddit.

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DIFFUSEARCH: Revolutionizing Chess AI with Implicit Search and Discrete Diffusion Modeling

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Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. Existing methods to address the challenges in AI-powered chess and decision-making systems include neural networks for chess, diffusion models, and world models. If you like our work, you will love our newsletter.

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This AI Paper from Meta AI Unveils Dualformer: Controllable Fast and Slow Thinking with Randomized Reasoning Traces, Revolutionizing AI Decision-Making

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A major challenge in AI research is how to develop models that can balance fast, intuitive reasoning with slower, more detailed reasoning in an efficient way. In AI models, this dichotomy between the two systems mostly presents itself as a trade-off between computational efficiency and accuracy.

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Agent-as-a-Judge: An Advanced AI Framework for Scalable and Accurate Evaluation of AI Systems Through Continuous Feedback and Human-level Judgments

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The lack of effective evaluation methods poses a serious problem for AI research and development. Current evaluation frameworks, such as LLM-as-a-Judge, which uses large language models to judge outputs from other AI systems, must account for the entire task-solving process. If you like our work, you will love our newsletter.

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CMU Researchers Introduce ReLM: An AI System For Validating And Querying LLMs Using Standard Regular Expressions

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The regular expression, LLM decision rules, and the traversal algorithm are all stored in the Query Object. A regular expression inference engine that effectively converts regular expressions to finite automata has been designed and implemented. They are the first group to use automata to accommodate these variant encodings.