Data-Centric AI

DataFunTalk

Jun 19, 2023 · Artificial Intelligence

Rensselaer Polytechnic Institute (RPI) Computer Science Faculty, Resources, and PhD/Intern Recruitment Overview

The announcement introduces RPI's prestigious computer science department, its extensive GPU resources, collaborations with IBM Research, and detailed profiles of three incoming faculty members—highlighting their research areas in graph neural networks, trustworthy AI, data‑centric AI, drug‑design generative models, and neural‑symbolic reasoning—while inviting PhD and intern applicants to apply with full scholarships and funding support.

Data-Centric AIDrug DesignGraph Neural Networks

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Rensselaer Polytechnic Institute (RPI) Computer Science Faculty, Resources, and PhD/Intern Recruitment Overview

Top Architect

Apr 12, 2023 · Artificial Intelligence

Data‑Centric AI Perspective on GPT Models: Training, Inference, and Maintenance

This article examines how large language models such as GPT‑1 through GPT‑4 succeed largely due to high‑quality, large‑scale training data, and explains the Data‑centric AI framework—training data development, inference data development, and data maintenance—while discussing prompt engineering, data‑driven improvements, and future trends in AI.

Data-Centric AIGPTInference Data

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Data‑Centric AI Perspective on GPT Models: Training, Inference, and Maintenance

DataFunSummit

Dec 23, 2022 · Artificial Intelligence

Data‑Centric AI Practices for Content Moderation at NetEase Yidun

The article presents NetEase Yidun’s data‑centric AI approach to content moderation, covering the background of Data‑Centric AI, the specific business and data challenges of content safety, comprehensive data pipelines—including collection, labeling, augmentation, selection, cleaning, iteration and testing—and the role of self‑, semi‑ and weak‑supervised learning in enhancing algorithm performance.

Algorithm InnovationData-Centric AIMachine Learning

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Data‑Centric AI Practices for Content Moderation at NetEase Yidun

DataFunTalk

Oct 27, 2022 · Artificial Intelligence

Data‑Centric AI and MLOps: A Case Study of Smart‑Cabin Applications in the Automotive Industry

The talk by Magic Data’s founder Zhang Qingqing outlines the shift from model‑centric to data‑centric AI, introduces Data‑Centric MLOps methodology, and demonstrates its automotive smart‑cabin application, highlighting data quality requirements, collaborative workflow, and performance gains across speech, live‑social and navigation scenarios.

AI applicationsAutomotive AIData-Centric AI

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Data‑Centric AI and MLOps: A Case Study of Smart‑Cabin Applications in the Automotive Industry