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MALT (Mesoscopic Almost Linearity Targeting): A New Antagonistic Targeting Technique Relying on Assumptions of Near-Linearity at an Intermediate Scale

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 10, 202461Views0Likes0Comments
Adversarial attacks, efforts to deceitfully force machine learning (ML) models to make incorrect predictions, have presented a significant challenge to the safety and dependability of crucial machine learning applications. Neural networks, a form of machine learning algorithm, are especially susceptible to adversarial attacks. These attacks are especially concerning in applications such as facial recognition systems,…

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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, 202465Views0Likes0Comments
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, 202461Views0Likes0Comments
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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The comprehensive analysis of U.S. census data highlights that the majority of employment opportunities are newly created.

Artificial Intelligence, Economics, History of science, Innovation and Entrepreneurship (I&E), Invention, Labor and jobs, Manufacturing, Research, School of Humanities Arts and Social Sciences, UncategorizedJuly 10, 202458Views0Likes0Comments
New research led by David Autor, Ford Professor of Economics at MIT, suggests that the jobs landscape in the US has been largely shaped by the emergence of occupations that did not necessarily exist pre-1940. The study, which covers employment developments between 1940 to 2018, showed that about six of every 10 jobs in existence…

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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, 202466Views0Likes0Comments
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, 202472Views0Likes0Comments
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, 202459Views0Likes0Comments
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, 202466Views0Likes0Comments
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, 202459Views0Likes0Comments
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, 202462Views0Likes0Comments
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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VCHAR: An Innovative AI Framework that Considers the Results of Simple Tasks as a Distribution Across Defined Ranges

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology, UncategorizedJuly 9, 202465Views0Likes0Comments
Complex Human Activity Recognition (CHAR) identifies the actions and behaviors of individuals in smart environments, but the process of labeling datasets with precise temporal information of atomic activities (basic human behaviors) is difficult and can lead to errors. Moreover, in real-world scenarios, accurate and detailed labeling is hard to obtain. Addressing this challenge is important…

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The study conducted on Artificial Intelligence by Ohio State University and Carnegie Mellon University delves into the concept of under-the-radar reasoning in Transformers and obtaining generalization via the process of grasping or Grokking.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJuly 9, 202462Views0Likes0Comments
Recent research by scientists at Ohio State University and Carnegie Mellon University has analyzed the limitations of large language models (LLMs), such as GPT-4, and their limitations in implicit reasoning. This refers to their ability to make accurate comparisons of internalized facts and properties, even when aware of the entities in question. The study focused…

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