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…

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…

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…

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.…

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…

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…

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…

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…

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…

The text is about the new updates in Python SDK, AI-assisted labeling, and a growing library of generative models.