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Meet POCO: A Novel Artificial Intelligence Framework for 3D Human Pose and Shape Estimation
The POCO (POse and shape estimation with COnfidence) framework is introduced as a solution to address challenges in estimating 3D human pose and shape from 2D images. POCO extends existing methods by estimating uncertainty along with body parameters, allowing for better accuracy and improved reconstruction quality. The framework incorporates a Dual Conditioning Strategy (DCS) and…
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New AI Tool Could Detect Patient Pain During Surgery
An AI-powered system presented at the ANESTHESIOLOGY 2023 annual meeting has the potential to revolutionize pain assessment in healthcare. The system uses computer vision and deep learning to interpret facial expressions and body movements, offering a more objective and unbiased method compared to current pain assessment tools. Early detection of pain can lead to shorter…
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This Artificial Intelligence Survey Research Provides A Comprehensive Overview Of Large Language Models Applied To The Healthcare Domain
This text discusses the use of Large Language Models (LLMs) in the healthcare industry. LLMs, such as GPT-4 and Med-PaLM 2, have shown improved performance in medical tasks and can revolutionize healthcare applications. However, there are challenges such as training data requirements and potential biases. The text also emphasizes the importance of ethical considerations. The…
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This AI Research Proposes FireAct: A Novel Artificial Intelligence Approach to Fine-Tuning Language Models with Trajectories from Multiple Tasks and Agent Methods
Researchers from System2 Research, the University of Cambridge, Monash University, and Princeton University have developed a fine-tuning approach called “FireAct” for language agents. Their research reveals that fine-tuning language models consistently improves agent performance. The study explores the advantages and consequences of fine-tuning, discussing topics such as scaling effects, robustness, generalization, efficiency, and cost implications.…
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Meet xVal: A Continuous Way to Encode Numbers in Language Models for Scientific Applications that Uses Just a Single Token to Represent any Number
Large Language Models (LLMs) often struggle with numerical calculations involving large numbers. The xVal encoding strategy, introduced by Polymathic AI researchers, offers a potential solution. By treating numbers differently in the language model and using a singular token labeled as [NUM], xVal achieves efficient and accurate encoding of numbers. The approach outperforms other strategies in…
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Apple and CMU Researchers Unveil the Never-ending UI Learner: Revolutionizing App Accessibility Through Continuous Machine Learning
Apple researchers, in collaboration with Carnegie Mellon University, have developed the Never-Ending UI Learner AI system. It continuously interacts with mobile applications to improve its understanding of UI design patterns and new trends. The system autonomously explores apps, performing actions and classifying UI elements. The collected data trains models to predict tappability, draggability, and screen…
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Is Multilingual AI Truly Safe? Exposing the Vulnerabilities of Large Language Models in Low-Resource Languages
Researchers from Brown University have demonstrated that translating English inputs into low-resource languages increases the likelihood of bypassing the safety filter in GPT-4 from 1% to 79%. This exposes weaknesses in the model’s security measures and highlights the need for more comprehensive safety training across languages. The study also emphasizes the importance of inclusive red-teaming…
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Google AI Introduces SANPO: A Multi-Attribute Video Dataset for Outdoor Human Egocentric Scene Understanding
Researchers at Google have developed SANPO, a large-scale video dataset for human egocentric scene understanding. The dataset contains over 600K real-world and 100K synthetic frames with dense prediction annotations. SANPO includes a combination of real and synthetic data, panoptic instance masks, depth information, and camera pose, making it unique compared to other datasets in the…
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This AI Paper Introduces DSPy: A Programming Model that Abstracts Language Model Pipelines as Text Transformation Graphs
Researchers have developed a programming model called DSPy that abstracts language model pipelines into text transformation graphs. This model allows for the optimization of natural language processing pipelines through the use of parameterized declarative modules and general optimization strategies. The DSPy compiler simulates different program versions and generates example traces for self-improvement. Case studies have…
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Clarifai 9.9: AI Assist
The text is about the new updates in Python SDK, AI-assisted labeling, and a growing library of generative models.