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Introducing Sohu: The First Global Transformer Specialized ASIC Chip.

The Sohu AI chip created by Etched holds the title as the fastest AI chip currently available, redefining AI computation and application capabilities. It enables processing of over 500,000 tokens per second on the Llama 70B model, outperforming traditional GPUs. An 8xSohu server can even replace 160 H100 GPUs, demonstrating its superior power and efficiency.…

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Researchers from New York University have released Cambrian-1: Improving Multimodal AI with Vision-Based Large Language Models for Better Performance and Adaptation in Actual World Scenarios.

Multimodal large language models (MLLMs), which integrate sensory inputs like vision and language, play a key role in AI applications, such as autonomous vehicles, healthcare and interactive AI assistants. However, efficient integration and processing of visual data with textual details remain a stumbling block. The traditionally used visual representations, that rely on benchmarks such as…

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Researchers from New York University Present Cambrian-1: Progressing Multimodal AI with a Focus on Large Language Models for Improved Real-World Functionality and Incorporation.

Multimodal large language models (MLLMs), which integrate sensory inputs like vision and language to create comprehensive systems, have become an important focus in AI research. Their applications include areas such as autonomous vehicles, healthcare, and AI assistants, which require an understanding and processing of data from various sources. However, integrating and processing visual data effectively…

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GraphReader: An AI Agent System Built on Graph-structures for Managing Extensive Texts by Organizing them into Graphs and Utilizing an Agent for Independent Exploration of these Graphs.

Large Language Models (LLMs) have played a notable role in enhancing the understanding and generation of natural language. They have, however, faced challenges in processing long contexts due to restrictions in context window size and memory usage. This has spawned research to address these limitations and come up with ways of making the LLMs work…

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GraphReader: An Artificial Intelligence system built on a graph framework intended to manage extensive texts by organizing them into a graph, which is then navigated independently by an AI agent.

Large language models (LLMs) have made significant progress in the understanding and generation of natural language, but their application over long contexts is still limited due to constraints in context window sizes and memory usage. It's a pressing concern as the demand for LLMs' ability to handle complex and lengthy tasks is on the rise. Various…

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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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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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