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Artificial Intelligence

The AI study by Google’s DeepMind investigates the impact of communication linkage in systems involving multiple agents.

In the field of large language models (LLMs), multi-agent debates (MAD) pose a significant challenge due to their high computational costs. They involve multiple agents communicating with one another, all referencing each other's solutions. Despite attempts to improve LLM performance through Chain-of-Thought (CoT) prompting and self-consistency, these methods are still limited by the increased complexity…

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An algorithm developed at MIT assists in predicting the occurrence rate of severe weather conditions.

Scientists led by Themistoklis Sapsis at MIT's Department of Mechanical Engineering have developed a strategy to "correct" the predictions of coarse global climate models, enhancing the accuracy of risk analysis for extreme weather events. Global climate models, used by policymakers to assess a community's risk of severe weather, can predict weather patterns decades or even…

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Introducing Abstra: A Start-up Powered by AI Using Python and AI to Expand Business Operations.

As a business grows, challenges around hiring, scaling, and compliance are common, necessitating an improvement in internal processes such as onboarding, customer service, and financial systems. To meet these needs and maintain operational agility, Abstra, an AI-driven startup has emerged as a comprehensive business process solution. Unlike other process automation tools, Abstra's real edge lies in…

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Path: A Machine Learning Technique for Educating Small-Scale (Sub-100M Parameter) Neural Data Retrieval Models Utilizing a Minimum of 10 Gold Relevance Labels

The use of pretrained language models and their creative applications have contributed to significant improvements in the quality of information retrieval (IR). However, there are questions about the necessity and efficiency of training these models on large datasets, especially for languages with scant labeled IR data or niche domains. Researchers from the University of Waterloo,…

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Replete-AI presents Replete-Coder-Qwen2-1.5b: A Multipurpose AI Model for Sophisticated Programming and Common Applications with Unrivalled Performance Efficiency.

Replete AI has launched Replete-Coder-Qwen2-1.5b, an artificial intelligence (AI) model with extensive capabilities in coding and other areas. Developed using a mix of non-coding and coding data, the model is designed to perform diverse tasks, making it a versatile solution for a range of applications. Replete-Coder-Qwen2-1.5b is part of the Replete-Coder series and has been…

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EvolutionaryScale has unveiled its new innovative product, ESM3, which combines modality, generativity, and language modeling to comprehensively analyze protein structures, systems, and functions.

Natural evolution has meticulously shaped proteins over more than three billion years. Modern-day research is closely studying these proteins to understand their structures and functions. Large language models are increasingly being employed to interpret the complexities of these protein structures. Such models demonstrate a solid capacity, even without specific training on biological functions, to naturally…

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EvolutionaryScale unveils ESM3: An innovative Multimodal Generative Language Model that can analyze and interpret the sequence, structure, and function of proteins.

Scientists from Evolutionary Scale PBC, Arc Institute, and the University of California have developed an advanced generative language model for proteins known as ESM3. The protein language model is a sophisticated tool designed to understand and forecast proteins' sequence, structure, and function. It applies the masked language modeling approach to predict masked portions of protein…

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UCLA’s latest machine learning study discovers unanticipated inconsistencies and roughness within the in-context decision boundaries of LLMs.

Researchers have been focusing on an effective method to leverage in-context learning in transformer-based models like GPT-3+. Despite their success in enhancing AI performance, the method's functionality remains partially understood. In light of this, a team of researchers from the University of California, Los Angeles (UCLA) examined the factors affecting in-context learning. They found that…

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New research on machine learning from UCLA reveals surprising inconsistencies and roughness in in-context decision boundaries of LLMs.

Advanced language models such as GPT-3+ have shown significant improvements in performance by predicting the succeeding word in a sequence using more extensive datasets and larger model capacity. A key characteristic of these transformer-based models, aptly named as "in-context learning," allows the model to learn tasks through a series of examples without explicit training. However,…

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An algorithm developed by MIT aids in predicting the occurrence of severe weather conditions.

Global climate models predict future weather conditions, but these models are limited in their ability to provide detailed forecasts for specific locations. Policymakers often need to supplement these coarse-scale models with high-resolution ones to predict local extreme weather events. However, the accuracy of these predictions heavily depends on the initial coarse model’s accuracy. Themistoklis Sapsis,…

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An algorithm originating from MIT assists in predicting the occurrence rate of severe weather conditions.

To better predict the risks of extreme weather events due to climate change, scientists at MIT have developed a method that refines the predictions from large, coarse climate models. The key to this approach is leveraging machine learning and dynamical systems theory to make the climate models' large-scale simulations more realistic. By correcting the climate…

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