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Microsoft’s research team has crafted SheetCompressor: A cutting-edge AI framework designed for encoding that efficiently compresses spreadsheets for LLMs.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 17, 202436Views0Likes0Comments
Spreadsheet analysis is crucial for managing and interpreting data in the extensive two-dimensional grids used in tools like MS Excel and Google Sheets. However, the large, complex grids often exceed the token limits of large language models (LLMs), making it difficult to process and extract meaningful information. Traditional methods struggle with the size and complexity…

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COCOM: A Potent Context Compression Technique Transforming Context Embeddings for Optimized Response Generation in RAG.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 17, 202437Views0Likes0Comments
For AI research, efficiently managing long contextual inputs in Retrieval-Augmented Generation (RAG) models is a central challenge. Current techniques such as context compression have certain limitations, particularly in how they handle multiple context documents, which is a pressing issue for many real-world scenarios. Addressing this challenge effectively, researchers from the University of Amsterdam, The University of…

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Transforming Cell Analysis: Advanced Phenotyping Made Possible by Integrating Artificial Intelligence and Mass Spectrometry with Deep Visual Proteomics.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 17, 202436Views0Likes0Comments
Deep Visual Proteomics (DVP) is a groundbreaking approach for analyzing cellular phenotypes, developed using Biology Image Analysis Software (BIAS). It combines advanced microscopy, artificial intelligence, and ultra-sensitive mass spectrometry, considerably expanding the ability to conduct comprehensive proteomic analyses within the native spatial context of cells. The DVP method involves high-resolution imaging for single-cell phenotyping, artificial…

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Transforming Cell Study: Advanced Phenotyping through the Integration of Artificial Intelligence and Mass Spectrometry in Deep Visual Proteomics

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 17, 202436Views0Likes0Comments
Deep Visual Proteomics (DVP) is a groundbreaking method that combines high-end microscopy, AI, and ultra-sensitive mass spectrometry for comprehensive proteomic analysis within the native spatial context of cells. By utilizing AI to identify different cell types, this technology allows an in-depth study of individual cells, increasing the precision and effectiveness of cellular phenotyping. The DVP workflow…

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Planetarium: A Novel Benchmark for Assessing LLMs in Converting Natural Language Descriptions of Planning Issues into Planning Domain Definition Language PDDL

AI Governance, AI Paper Summary, AI Shorts, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 16, 202434Views0Likes0Comments
Large language models (LLMs) have shown promise in solving planning problems, but their success has been limited, particularly in the process of translating natural language planning descriptions into structured planning languages such as the Planning Domain Definition Language (PDDL). Current models, including GPT-4, have achieved only 35% accuracy on simple planning tasks, emphasizing the need…

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Investigating Resilience: A Comparative Study of Larger Kernel ConvNets, Convolutional Neural Networks (CNNs), and Vision Transformers (ViTs)

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Computer vision, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 16, 202430Views0Likes0Comments
Robustness plays a significant role in implementing deep learning models in real-world use cases. Vision Transformers (ViTs), launched in the 2020s, have proven themselves to be robust and offer high-performance levels in various visual tasks, surpassing traditional Convolutional Neural Networks (CNNs). It’s been recently seen that large kernel convolutions can potentially match or overtake ViTs…

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H2O.ai has just launched their most recent Open-Weight Compact Language Model, H2O-Danube3, under the Apache v2.0 license.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Small Language Model, Staff, Tech News, Technology, UncategorizedJuly 16, 202440Views0Likes0Comments
Natural Language Processing (NLP) is rapidly evolving, with small efficient language models gaining relevance. These models, ideal for efficient inference on consumer hardware and edge devices, allow for offline applications and have shown significant utility when fine-tuned for tasks like sequence classification or question answering. They can often outperform larger models in specialized areas. One…

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A Genuine Insight into Language Model Optimizers: Functionality and Utility

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 16, 202435Views0Likes0Comments
A team from Harvard University and the Kempner Institute at Harvard University have conducted an extensive comparative study on optimization algorithms used in training large-scale language models. The investigation targeted popular algorithms like Adam - an optimizer lauded for its adaptive learning capacity, Stochastic Gradient Descent (SGD) that trades adaptive capabilities for simplicity, Adafactor with…

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Introducing Gauge: An Emerging AI Company Developing Open Source Instruments to Tackle the Issue of Microservices VS Monolith

AI Startups, Editors Pick, Staff, Tech News, UncategorizedJuly 16, 202434Views0Likes0Comments
Startups often encounter challenges when prioritizing business growth over code quality, resulting in code sprawl and tightly coupled services. Managing even minor features or changes turns into a substantial burden and as a solution, several startups have turned to microservices. However, this introduces another set of issues such as orchestration, lifecycle management, and versioning dependencies.…

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Researchers from MIT suggest IF-COMP: A Far-Reaching Resolution for Enhanced Calibration and Uncertainty Estimation in Deep Learning Amid Distribution Alterations.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Deep Learning, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 16, 202440Views0Likes0Comments
Researchers from the Massachusetts Institute of Technology, University of Toronto, and Vector Institute for Artificial Intelligence have developed a new method called IF-COMP for improving the estimation of uncertainty in machine learning, particularly in deep learning neural networks. These fields place importance on not only accurately predicting outcomes but quantifying the uncertainty involved in these…

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IBM scientists recommend ExSL+granite-20b-code: A model based on granite code that simplifies data analysis by allowing generative AI to translate natural language inquiries into SQL queries.

AI Shorts, AI Tool, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 16, 202437Views0Likes0Comments
IBM researchers are working on addressing the challenge of digging out beneficial insights from large databases, a problem often encountered in businesses. The volume and variety of data are overwhelming and can pose a significant challenge for employees to find the necessary information. Writing SQL codes, needed to retrieve data across multiple programs and tables,…

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