The Noah’s Ark Lab is the AI research center for Huawei Technologies, founded in 2012. Research areas of the lab mainly include computer vision, natural language processing, search & recommendation, decision and reasoning ,AI theory.
The mission of the lab is to make significant contributions to both the company and society by innovating in artificial intelligence, data mining and related fields. The lab has now grown to be a research organization with many significant achievements in both academia and industry.
Focus on the application of AI technology in telecommunication networks. Using key technologies such as reinforcement learning, deep learning, robust AI, spatial-temporal prediction, to implement network space-time prediction and optimization control, and finally to achieve an automatic, self-healing, and self-optimized automatic driving network.
Focusing on data analysis and management, we leverage AI algorithms, such as demand prediction, inventory control and multi-object optimization, to support and optimize daily operation at enterprise level. Solutions include but not limit to smart supply chain, smart finance system and smart HR system.
MACHING LEARNING FOR SEATCH
Focus on machine learning driven recommendation and search technologies, the research directions includes deep learning for recommendation system, reinforcement learning for search, online learning technology, Exploration & Exploitation, search query recommendation, new word detection, the general low rank model, such as one class matrix factorization, field-aware factorization machine
COGNITIVE LOV(INTERNET OF VEHICLES)
Focus on building a learning-based self-driving system with high level of safety, smartness, speed and smoothness. Using key technologies such as deep learning, reinforcement learning, SLAM, multi-sensor fusion to enable vehicles to realize accurate localization, sense and understand the surrounding world and make intelligent decisions and actions.
Focus on artificial intelligence algorithms and systems. Includes an industrial AR assistant system constructed based on machine vision such as target detection, SLAM, and target tracking, to improve network operation efficiency. Based on the modeling and analysis of all telecom fields and fine-grained data, accurate prediction of network spatiotemporal traffic is implemented, and intelligent network management, planning, and upgrade are implemented. Based on sequence data prediction, anomaly detection, and root cause analysis, intelligent fault locating and quick recovery of network problems are implemented.