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A Team of UC Berkeley and Stanford Researchers Introduce S-LoRA: An Artificial Intelligence System Designed for the Scalable Serving of Many LoRA Adapters
UC Berkeley and Stanford researchers have developed a parameter-efficient fine-tuning method called Low-Rank Adaptation (LoRA) for deploying language models. The method, S-LoRA, allows thousands of adapters to run efficiently on a single GPU or across multiple GPUs with minimal overhead. It optimizes GPU memory usage, reducing computational requirements for real-world applications. S-LoRA outperforms other libraries…
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Researchers from Cambridge have Developed a Virtual Reality Application Using Machine Learning to Give Users the ‘Superhuman’ Ability to Open and Control Tools in Virtual Reality
Researchers from the University of Cambridge have developed a VR program called “HotGestures” that allows users to access and use 3D modeling tools through hand gestures. Using machine learning, the system recognizes gestures and enables quick and efficient tool selection. The gesture-based method was well-received by participants and outperformed traditional menu-based interaction in terms of…
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Meta Researchers Introduced VR-NeRF: An Advanced End-to-End AI System for High-Fidelity Capture and Rendering of Walkable Spaces in Virtual Reality
VR-NeRF is an advanced AI system for capturing and rendering high-fidelity walkable spaces in virtual reality. It addresses the limitations of existing methods by offering realistic VR experiences with high-quality renderings and allowing users to freely explore real-world spaces. The system utilizes a high-fidelity dataset and a multi-camera rig, along with a custom GPU renderer,…
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Giskard Releases Giskard Bot on HuggingFace: A Bot that Automatically Detects Issues of the Machine Learning Models You Pushed to the HuggingFace Hub
Giskard Bot, an open-source testing framework, has been introduced as a game-changer in machine learning models. It aims to identify vulnerabilities, generate domain-specific tests, and automate test suite execution within CI/CD pipelines. The integration of Giskard bot with Hugging Face allows users to automatically publish vulnerability reports when new models are uploaded. Giskard not only…
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This AI Research from China Introduces Consistent4D: A Novel Artificial Intelligence Approach for Generating 4D Dynamic Objects from Uncalibrated Monocular Videos
A research study by CASIA, Nanjing University, and Fudan University introduces Consistent 4D, a new method for generating 4D content from 2D sources. The approach utilizes a tailored Cascade DyNeRF and a pre-trained 2D diffusion model to visualize moving objects. The study demonstrates promising results for video-to-4D creation, with potential applications in various fields.
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This AI Paper Introduces RuLES: A New Machine Learning Framework for Assessing Rule-Adherence in Large Language Models Against Adversarial Attacks
A group of researchers from UC Berkeley, Stanford, and King Abdulaziz City for Science and Technology has proposed a programmatic framework called RULES to evaluate the rule-following ability of large language models (LLMs). RULES consists of 15 text scenarios with specific rules for model behavior. The study highlights vulnerabilities in popular LLMs like GPT-4 and…
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NVIDIA Maxine Transformed Video Conferencing with AI Integration
NVIDIA has unveiled its latest Maxine developer platform, introducing GPU-accelerated AI services that enhance video and audio streams in real time. The update includes features like augmented reality, audio effects, video effects, Live Portrait animation using a standard webcam, Voice Font for creating a unique digital voice, and Eye Contact, which enhances conversation engagement. Maxine…
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Johannes Kepler University Researchers Introduce GateLoop: Advancing Sequence Modeling with Linear Recurrence and Data-Controlled State Transitions
GateLoop is a novel sequence model developed by researchers from Johannes Kepler University. It outperforms existing linear recurrent models in auto-regressive language modeling. GateLoop offers low-cost recurrent and efficient parallel modes and introduces a surrogate attention mode with implications for Transformer architectures. It emphasizes the significance of data-controlled cumulative products for more robust sequence models.…
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This AI Paper Introduces PolyID: Pioneering Machine Learning in the Discovery of High-Performance Biobased Polymers
Artificial intelligence has proven to be a valuable tool in the field of chemistry and polymer science. By predicting chemical reactions and suggesting optimal combinations, AI helps scientists discover new materials and accelerate the development process. Researchers are also exploring the use of biomass and waste materials to create more sustainable polymers with enhanced properties.…
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Duke University Researchers Propose Policy Stitching: A Novel AI Framework that Facilitates Robot Transfer Learning for Novel Combinations of Robots and Tasks
Researchers from Duke University and the Air Force Research Laboratory have introduced a new approach called Policy Stitching (PS) to tackle challenges in using reinforcement learning (RL) for teaching robots new skills. PS enables the combination of separately trained robots and task modules to create a new policy for rapid adaptation, showing exceptional zero-shot and…