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Streamlined Ongoing Education for Pulse-Based Neural Networks Utilizing Time-Domain Compaction

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 10, 202432Views0Likes0Comments
AI integration into low-powered Internet of Things (IoT) devices such as microcontrollers has been enabled by advances in hardware and software. Holding back deployment of complex Artificial Neural Networks (ANNs) to these devices are constraints such as the need for techniques such as quantization and pruning. Shifts in data distribution between training and operational environments…

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Google DeepMind Presents JEST: An Enhanced AI Training Technique that is 13 Times Quicker and 10 Times More Energy Efficient

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 10, 202439Views0Likes0Comments
Data curation, particularly high-quality and efficient data curation, is crucial for large-scale pretraining models in vision, language, and multimodal learning performances. Current approaches often depend on manual curation, making it challenging to scale and expensive. An improvement to such scalability issues lies in model-based data curation that selects high-quality data based on training model features.…

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Using Deep Learning in the Engineering of Proteins: Creating Functional Soluble Proteins

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Deep Learning, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 10, 202429Views0Likes0Comments
Traditional protein design, which relies on physics-based methods like Rosetta can encounter difficulties in creating functional proteins due to parametric and symmetric constraints. Deep learning tools such as AlphaFold2 have revolutionized protein design by providing more accurate prediction abilities and the capacity to analyze large sequence spaces. With these advancements, more complex protein structures can…

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DoRM: A Method Inspired by the Brain for Generative Adaptation in Various Domains

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 10, 202433Views0Likes0Comments
Generative Domain Adaptation (GDA) is a machine learning technique used to adapt a model trained in one domain (source) using a few examples from another domain (target). This is beneficial in situations where it is expensive or impractical to obtain substantial labeled data from the target domain. While existing GDA solutions focus on enhancing a…

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An Overview of Manageable Learning: Techniques, Uses, and Difficulties in Data Gathering

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 10, 202427Views0Likes0Comments
Controllable Learning (CL) is being recognized as a vital element of reliable machine learning, one that ensures learning models meet set targets and can adapt to changing requirements without the need for retraining. This article examines the methods and applications of CL, focusing on its implementation within Information Retrieval (IR) systems, as demonstrated by researchers…

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NVIDIA presents RankRAG: An innovative RAG structure that uses a single LLM to tune-instructions for dual uses, namely top-k context ranking, and answer generation in RAG.

AI Paper Summary, AI Shorts, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 10, 202432Views0Likes0Comments
Retrieval-augmented generation (RAG) is a technique that enhances large language models’ capacity to handle specific expertise, offer recent data, and tune to specific domains without changing the model’s weight. RAG, however, has its difficulties. It struggles with handling different chunked contexts efficiently, often doing better with a lesser number of highly relevant contexts. Similarly, ensuring…

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D-Rax: Improving Radiological Accuracy with Expert-Coupled Vision-Language Models

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 9, 202436Views0Likes0Comments
Advancements in Vision-and-Language Models (VLMs) like LLaVA-Med propose exciting opportunities in biomedical imaging and data analysis. Still, they also face challenges such as hallucinations and imprecision risks, potentially leading to misdiagnosis. With the escalating workload in radiology departments and professionals at risk of burnout, the need for tools to mitigate these problems is pressing. In response…

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D-Rax: Improving Radiological Accuracy with Expert-Combined Visual-Language Models

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 9, 202435Views0Likes0Comments
Radiology departments often deal with massive workloads leading to burnout among radiologists. Therefore, tools to help mitigate these issues are essential. VLMs such as LLaVA-Med have advanced significantly in recent years, providing multimodal capabilities for biomedical image and data analysis. However, the generalization and user-friendliness issues of these models have hindered their clinical adoption. To…

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This AI investigation by Tenyx delves into the cognitive abilities of Large Language Models (LLMs) by observing their understanding of geometric principles.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202434Views0Likes0Comments
Large language models (LLMs) have demonstrated impressive performances across various tasks, with their reasoning capabilities playing a significant role in their development. However, the specific elements driving their improvement are not yet fully understood. Current strategies to enhance reasoning focus on enlarging model size and expanding the context length via methods such as chain of…

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This AI study by Tenyx investigates the logical capabilities of Large Language Models (LLMs) based on their understanding of geometric concepts.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202433Views0Likes0Comments
Large language models (LLMs) have made remarkable strides in many tasks, with their capacity to reason forming a vital aspect of their development. However, the main drivers behind these advancements remain unclear. Current measures to boost reasoning primarily involve increasing the model's size and extending the context length with methods such as the chain of…

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An Extensive Comparison by Innodata: Evaluating Llama2, Mistral, Gemma, and GPT in terms of Accuracy, Offensive Language, Prejudice, and Tendency to Imagine

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 9, 202434Views0Likes0Comments
A recent study by Innodata assessed various large language models (LLMs), including Llama2, Mistral, Gemma, and GPT for their factuality, toxicity, bias, and hallucination tendencies. The research used fourteen original datasets to evaluate the safety of these models based on their ability to generate factual, unbiased, and appropriate content. Ultimately, the study sought to help…

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Innodata’s Extensive Comparisons of Llama2, Mistral, Gemma and GPT in terms of Accuracy, Harmful Language, Prejudice, and Inclination towards Illusions

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 9, 202432Views0Likes0Comments
An in-depth study by Innodata evaluated the performance of various large language models (LLMs) including Llama2, Mistral, Gemma, and GPT. The study assessed the models based on factuality, toxicity, bias, and propensity for hallucinations and used fourteen unique datasets designed to evaluate each model's safety. One of the main criteria was factuality, the ability of the…

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