May 14, 2024, 4:41 a.m. | Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06784v1 Announce Type: new
Abstract: This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for …

abstract artificial artificial intelligence arxiv biomedical challenges chatgpt clip cs.lg datasets federated learning foundation healthcare impact integration intelligence llama opportunities pretraining research survey through type unsupervised vast

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Intern - Robotics Industrial Engineer Summer 2024

@ Vitesco Technologies | Seguin, US