Data-analysis and modelling positions are already becoming obsolete, but hands-on experimentalists can breathe easy for now.
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Rapidata emerges to shorten AI model development cycles from months to days with near real-time RLHF
Rapidata treats RLHF as high-speed infrastructure rather than a manual labor problem. Today, the company exclusively announced to us at VentureBeat its emergence with an $8.5 million seed ...
There are variations of these roles, but the deluge on job boards means one thing: training AI models is a real business. One ...
Automating knowledge production and teaching weakens the ecosystem of students and scholars that sustains universities, ...
Abstract: Papillary (PTC) and follicular (FTC) thyroid carcinomas require different treatment strategies, but their accurate differentiation remains a challenge in conventional histopathology.
A team of UCSF researchers successfully tested several mainstream AI agents for the ability to analyze big data on women's ...
Interpretation is the discipline through which molecular datasets reveal their significance. As the life sciences enter a new era defined by data richness and technological capacity, interpretive ...
This project investigates token quality from a noisy-label perspective and propose a generic token cleaning pipeline for SFT tasks. Our method filters out uninformative tokens while preserving those ...
Abstract: Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work generally ...
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