An in‑depth interview chronicles Xu Haiyang’s rise from a modest rural upbringing to becoming Baidu’s chief search architect, detailing his early education, rapid career progression, the humble beginnings of Baidu’s search infrastructure, and his later work in neural networks and AI research.
The article presents Baidu's end‑to‑end third‑party sentiment feedback solution, detailing its evolution from waterfall to agile and lean development, and describing the three‑stage pipeline of data acquisition, cleansing, and warehousing that enables real‑time product quality loops.
The article outlines Baidu Mobile Ad Network's challenges in media integration and presents a standardized, automated three‑system architecture—business, joint‑debug, and logic layers—along with a detailed three‑stage integration workflow to improve efficiency, quality, and scalability of ad traffic onboarding.
This article provides a comprehensive guide to Baidu's mobile SDKs—including Statistics, Map (location), and Voice—detailing their features, platform-specific integration steps for Android and iOS, and sample code snippets for developers to implement analytics, precise positioning, and speech recognition in their apps.
This article explains why migrating to HTTPS is critical for modern web applications, outlines the technical challenges such as compatibility, latency, and attacks, and details Baidu's practical solutions and free options for small sites to adopt full‑site HTTPS securely and efficiently.
This article explains Baidu's continuous integration (CI) journey, detailing the CI capability model, its three core factors—test coverage, build system, and team habits—along with metrics, scoring, and how the CI team supports product lines to improve development efficiency.
This article chronicles Baidu’s eight‑year journey from early Hadoop adoption to advanced MPI, DAG engines, and real‑time streaming platforms, detailing architectural milestones, performance optimizations, and practical lessons for large‑scale offline and online data processing.
The article examines Baidu’s massive 2014 investment in artificial intelligence—covering Baidu Brain, breakthroughs in vision, speech and NLP, big‑data capabilities, open platforms, IoT hardware, and talent strategy—to explain how these moves may reshape both Baidu and the broader technology landscape.
This article reviews Spark 1.2’s major enhancements—including the External Data Source API, column pruning, predicate pushdown, and in‑memory columnar storage—while also detailing Baidu’s large‑scale Spark deployments, its custom high‑performance Shuffle service, and the integration of Spark with the Tachyon memory file system.
The Baidu Tech Salon introduced the Blend framework, an open‑source solution that merges native device capabilities and cloud services into WebApps, enabling faster iteration, richer APIs, and near‑native user experiences across iOS, Android, and browsers.
At Baidu’s November 25 technical salon, senior leaders Robin Li, Wang Jin and Andrew Ng highlighted the company’s AI‑driven strategy—emphasizing sub‑second search, massive deep‑learning and data infrastructure, a 14% revenue R&D spend, breakthroughs in vision, speech, NLP and autonomous‑driving platforms, and a new AI talent program—positioning Baidu as a leading innovator in big‑data and intelligent connectivity.
This article examines Baidu’s home‑grown real‑time big‑data platforms Dstream and TM, detailing their architectures, performance metrics, key features, and practical use cases such as log ETL and real‑time bidding, while highlighting how they meet millisecond‑level processing demands.
At QCon2014, Baidu Research’s Shen Zhiyong showcased the company’s massive big‑data engine—20,000 PB storage and daily processing of up to 100 PB—highlighting open platforms like Baidu Brain and real‑world prediction projects for tourism, the World Cup, disease outbreaks, and UN collaborations, while urging industry‑wide data‑driven transformation.
After decades of being deemed a 'century‑long' AI challenge, Baidu’s Bingo system achieved amateur‑to‑professional level Go play by introducing optimized Monte‑Carlo tree search, a weakened Alpha‑Beta hybrid, and massive supervised learning, demonstrating how breakthroughs in game AI can ripple into broader Baidu products.
At Baidu’s 53rd Technology Salon, researcher Shen Zhiyong outlined the lab’s vision of an online intelligent system that unifies monitoring, anomaly detection, diagnosis and big‑data‑driven prediction—using time‑series, causal and simulation analyses—to forecast tourism crowds, predict Gaokao essay topics, and illustrate both the opportunities and challenges of processing massive, heterogeneous data for real‑time decision support.
At Baidu’s Technology Salon #53 on August 16, a near‑full audience heard data scientist Shen Zhiyong explain the origins of Baidu’s tourist‑flow prediction tool and Liu Ying discuss the evolution of social‑economic forecasting from classic “beer‑and‑diapers” models to modern big‑data analytics, followed by an interactive Q&A and prize‑giving session.
Baidu’s speech synthesis system, developed since 2013 to give machines natural Chinese voices, tackles millions of tonal variations through phonetic compression and optimized acoustic models, balances trade‑offs in data and scalability, and offers a free open platform that lets developers integrate high‑quality text‑to‑speech into apps, advancing equal access to information.
This article analyzes Baidu's lightweight app platform, presents research on web‑vs‑native performance gaps, introduces the BlendUI concept that treats every element as an independent webview, and details its design principles, core implementation, and suitable use cases for achieving native‑level transition smoothness.
In a June 28 presentation at Baidu’s 51st Technology Salon, senior NLP researcher Zhao Shiqi showcased Baidu Kaoba—a conversational search tool for college admissions—illustrating how inference‑based rewriting, ellipsis completion, concept clarification and interactive generation enable deep, domain‑specific semantic understanding that he predicts will power future internet services.
The article examines how Baidu’s newly launched AI‑powered “KaoBa” lightweight app is helping Chinese high‑school graduates choose universities, illustrates the broader surge of AI services in China, compares Baidu’s approach with rivals such as Google, IBM, Facebook and Microsoft, and discusses the potential impact on education, healthcare and other industries.
Using search query volumes, Baidu’s big‑data engine forecasts tourist numbers for Chinese attractions, achieving over 90% accuracy and partnering with CCTV to broadcast real‑time crowd predictions during the May Day holiday, while also outlining future forecasting plans for flu, sports, finance and real‑estate trends.
At Baidu’s 49th Technical Salon, Cheng Yishi explained how the company revamped its MooseFS and Redis systems by adding a Shadow Master to split reads from writes, introducing Slave nodes for failover, and deploying a Redis proxy middleware, thereby dramatically improving performance, scalability, and high‑availability for critical services.
Baidu's Big Data Machine Learning team, led by Xia Fen, unveiled a suite of five novel algorithms that together allow trillion‑scale feature processing, minute‑level model updates, and up to thousand‑fold efficiency gains in training and inference, dramatically surpassing existing solutions such as Google's billion‑feature systems.
