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Discovering Hallucinations in Text Generated by Advanced AI: A New Innovation from KnowHalu: Evaluating Large Language Models (LLMs)

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedMay 13, 202440Views0Likes0Comments
Artificial intelligence models, in particular large language models (LLMs), have made significant strides in generating coherent and contextually appropriate language. However, they sometimes create content that seems correct but is actually inaccurate or irrelevant, a problem often referred to as "hallucination". This can pose a considerable issue in areas where high factual accuracy is critical,…

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Researchers from the University of California, Berkeley have unveiled a new AI strategy named Learnable Latent Codes as Bridges (LCB). This innovative approach merges the abstract thinking abilities of large language models with low-level action strategies.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedMay 12, 202433Views0Likes0Comments
Robotics traditionally operates within two dominant architectures: modular hierarchical policies and end-to-end policies. The former uses rigid layers like symbolic planning, trajectory generation, and tracking, whereas the latter uses high-capacity neural networks to directly connect sensory input to actions. Large language models (LLMs) have rejuvenated the interest in hierarchical control architectures, with researchers using LLMs…

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Reducing Computational Overload in Dependable Implementation: A Hybrid CNN Approach to Repetition in AI

Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 11, 202439Views0Likes0Comments
Researchers from Zurich's Institute of Embedded Systems at the University of Applied Sciences Winterthur have addressed the issue of reliability and safety in AI models. This is especially relevant for systems with essential safety integrated functions (SIF), such as edge-AI devices. The team noted that while existing redundancy techniques are effective, they are often computationally…

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Improving Graph Neural Network Training with DiskGNN: A Significant Advancement towards Effective Large-Scale Learning

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 11, 202446Views0Likes0Comments
Graph Neural Networks (GNNs) are essential for processing complex data structures in domains such as e-commerce and social networks. However, as graph data volume increases, existing systems struggle to efficiently handle data that exceed memory capacity. This warrants out-of-core solutions where data resides on disk. Yet, such systems have faced challenges balancing speed of data…

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COLLAGE: An Innovative Machine Learning Method to Handle Floating-Point Mistakes in Low-Precision for Accurate and Streamlined LLM Training

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 11, 202438Views0Likes0Comments
Large language models (LLMs) have introduced ground-breaking advancements to the field of natural language processing, such as improved machine translation, question-answering, and text generation. Yet, training these complex models poses significant challenges, including high resource requirements and lengthy training times. Former methods addressing these concerns involved loss-scaling and mixed-precision strategies, which aimed to further training efficiency…

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AnchorGT: An Innovative Attention Mechanism for Graph Transformers Providing a Versatile Component to Enhance Scalability Across Various Graph Transformer Models

AI Paper Summary, AI Shorts, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 10, 202440Views0Likes0Comments
The standard Transformer models in machine learning have encountered significant challenges when applied to graph data due to their quadratic computational complexity, which scales with the number of nodes in the graph. Past efforts to navigate these obstacles have tended to diminish the key advantage of self-attention, which is a global receptive field, or have…

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An improved method for regulating the transformation of flexible robots.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Research, Robotics, Robots, School of Engineering, UncategorizedMay 10, 202444Views0Likes0Comments
Scientists at MIT have been working on the design and control of a reconfigurable, squishy, soft robot, similar in nature to 'slime', that has potential applications in healthcare, wearable devices and industrial systems due to its ability to shape-shift to complete varying tasks. These soft robots currently only exist in labs and do not possess…

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Examining the Influence of Intense Focus on Numerical Variation and Training Consistency in Extensive Machine Learning Systems.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 10, 202433Views0Likes0Comments
Training large-scale Generative AI models can be challenging due to the immense computational resources and time they require. This complexity gives rise to frequent instabilities, manifested as disruptive loss spikes during prolonged training periods. These instabilities can result in costly interruptions, requiring the training process to be paused and restarted. For example, the LLaMA2's 70-billion…

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