The article introduces the ICLR‑2024 paper C‑Poly, a multi‑task learning framework that boosts large‑model efficiency and resource utilization, aiming to make powerful AI models as accessible and convenient as everyday services like QR‑code payments.
The article analyzes the computing characteristics and system challenges of domestically trained large AI models, outlines the shortcomings of current Chinese efforts, and proposes six strategic actions—including scaling compute, improving data management, building a national R&D team, and boosting funding and policy support—to accelerate China’s transition from following to leading in AI.
Zhang Le’s IDCF talk reviews the latest design principles and real‑world implementation of one‑stop R&D efficiency platforms in large enterprises, comparing leading Chinese internet firms, outlining strategies for integrating large‑model technology, and detailing the five‑stage evolution of Tencent’s platform that boosts productivity across massive development teams.
This report examines the technical evolution of large AI models, outlines industry demand, explains how these models add value across sectors, and discusses future challenges and considerations for large‑model‑driven transformation in a wide range of businesses.
The article analyzes how Kimi’s superior long‑context capabilities have propelled it to the top of user traffic in China, reshaping the competitive dynamics among domestic and international AI large models and driving a rapid surge in compute demand across both C‑end and B‑end applications.
The article analyzes the computational characteristics and system challenges of training large AI models on domestic platforms, examines framework parallelism and future algorithms, and proposes six strategic measures—including scaling compute, improving data management, building a national R&D team, and boosting AI‑chip investment—to accelerate China’s AI leadership.
At the Create 2024 Baidu AI Developer Conference, Baidu unveiled its next‑generation intelligent computing operating system WanYuan, detailing its cluster‑scale management, GPU‑centric performance, integrated large‑model services, and a layered architecture that aims to simplify AI‑native application development and accelerate the AI era.
In this talk from the 2024 China Generative AI Conference, Li Peng outlines the escalating computational demands of large‑model training and inference, identifies power, memory and communication walls, and presents Alibaba Cloud’s DeepGPU solutions and best‑practice strategies for scaling AI workloads on cloud GPUs.
At the 3rd China International Software Development Conference, Baidu’s CTO Wang Haifeng highlighted the role of operating systems in the AI era, showcasing PaddlePaddle’s open‑source platform, the Wenxin large model’s efficiency gains, and the emergence of intelligent agents and code assistants.
The article presents a 2024 AIGC round‑table where Chinese experts discuss whether to follow a technology‑first or market‑first approach, the challenges of compute, algorithms and data, domestic versus foreign large‑model strategies, multi‑model deployment in enterprises, and criteria for evaluating successful AIGC applications.
In his Create 2024 AI Developer Conference keynote, Li Yanhong outlines Baidu’s latest large‑model series, AI‑native development platforms (AgentBuilder, AppBuilder, ModelBuilder), performance breakthroughs, real‑world case studies, and the strategic vision that makes AI development accessible to all developers and enterprises.
Baidu has launched the second Wenxin Cup AI startup competition, inviting global and university teams to develop AI‑native applications and compete for cash prizes—including a new Special Award of up to 50 million RMB—while providing technical guidance, ecosystem resources, and mentorship to accelerate the AI entrepreneurship ecosystem.
This article provides a comprehensive overview of AI large‑model development, defines what constitutes a large model, discusses current challenges such as cost, privacy and safety, and examines how these models can improve efficiency across automotive marketing, sales, service, data management, infrastructure building, and future automation stages.
The 2024 China “Hundred-Model War” report analyzes how ten major domestic large‑model vendors compete across five dimensions—team, product, infrastructure, ecosystem, and development—revealing that Baidu and Huawei dominate overall while others show niche strengths and growth opportunities.
This article reviews how the rapid rise of large language models, exemplified by ChatGPT, is transforming recommendation systems by addressing traditional ID‑centric limitations, introducing prompt‑based and ID‑free representations, discussing recent research advances, practical challenges, and future research directions.
The article reviews Baidu’s Qianfan AI platform, highlighting rapid large‑model advances, enterprise challenges, new AppBuilder features, lightweight model releases, and cost‑effective model routing that together aim to boost AI adoption across industries.
In a detailed interview, Tsinghua researcher Zhao Sicheng and JD Retail senior director Peng Changping discuss the latest progress in multimodal large models, their practical applications in advertising and e‑commerce, persistent challenges such as hallucinations and data alignment, and the skills engineers need to thrive in the emerging AI era.
On March 1, Kuaishou hosted the launch of the Future High-Tech Video Intelligence Innovation Center in Beijing, highlighting collaborations with leading universities, breakthroughs in video codecs, a new transmission protocol, 6DoF video, digital humans, and large AI models that aim to drive industry-wide digital and intelligent transformation.
In this round‑table discussion, industry experts analyze Alibaba Cloud's historic price reductions, compare self‑built IDC versus public cloud economics, share iReader's phased migration experience, explore the broader value of cloud services beyond raw resources, and examine how large‑model AI workloads reshape cost and innovation strategies.
The article examines the current state and future trajectory of AI large models, highlighting GPT's leadership, domestic Chinese developments, emerging multimodal applications, evolving revenue models, and market size forecasts that signal a transformative shift in AI-driven industry.
In a detailed round‑table discussion, industry leaders analyze Alibaba Cloud's historic price cuts, compare self‑built IDC versus public cloud economics, share iReader's migration journey, and explore how AI large‑model services and PaaS offerings amplify cost‑efficiency and innovation in the cloud era.
The article details JD Retail Advertising's three‑stage evolution of its online model system—deep‑learning era, large‑model era, and power‑collaboration era—highlighting heterogeneous computing optimizations, platform and system capabilities, distributed graph computing, online learning, and dynamic power allocation to dramatically improve algorithm iteration speed and model performance.
This article introduces Colossal‑AI, an open‑source platform that tackles the challenges of training, fine‑tuning, and deploying massive AI models by leveraging efficient memory management, N‑dimensional parallelism, and high‑performance inference to dramatically reduce cost and improve scalability across thousands of GPUs.
Ant Group’s newly open‑sourced ATorch library extends PyTorch with a layered architecture and automated resource‑aware strategies, boosting large‑model training efficiency up to 60% utilization, enhancing stability, and delivering significant throughput gains across multi‑node, multi‑GPU deployments.
This article explores the rapid evolution of AI large models, their practical applications in intelligent customer service, text generation, digital humans, and healthcare, while addressing challenges such as compute costs, data quality, security, and model hallucinations, and offering expert strategies for successful enterprise adoption.
This article reviews the evolution of multimodal large models, introduces Jiuzhang Yunji DataCanvas' multimodal model platform—including AI foundation software, model tools, serving, and prompt management—shares practical building methods, memory‑augmented models, ETL pipelines, knowledge‑base applications, and offers a forward‑looking perspective on enterprise data management and intelligent agents.
The article examines the security challenges introduced by large‑model AIGC, outlines three technical upgrade paths—richer training data, few‑shot model fine‑tuning, and multimodal fusion—and demonstrates practical implementations that dramatically improve detection efficiency, accuracy, and scalability.
This article offers a comprehensive mid‑stage analysis of large‑model technology, discussing its rapid development, emerging challenges such as cost and hallucinations, positioning, scenario applications, cost‑value trade‑offs, and strategic pathways for future research and deployment.
At the Meituan Robotics Institute’s 2023 academic annual meeting in Shenzhen, leading scholars highlighted embodied intelligence as a learning “child,” emphasized large‑scale models and big data as catalysts for advanced perception and control, identified logistics, manufacturing, elder‑care and scientific research as near‑term markets, and called for deeper industry‑academia collaboration to accelerate robot deployment and explore new applications.
Wang Weiqiang, chief scientist of Ant Group’s Security Lab, highlighted the urgent need for both rapid detection and long‑term trustworthy safeguards for large AI models, outlining Ant’s data‑detox, guard‑rail, and detection platforms as core solutions to emerging risks such as hallucinations, bias, and data leakage.
The article examines how enterprises can adopt domain‑specific large models by addressing talent and cost challenges, outlining self‑supervised pre‑training, instruction fine‑tuning, data governance for unstructured data, dataset balance, model‑type selection, and integrated product solutions to achieve efficient, high‑performance AI deployments.
This article examines how AI foundation software, especially large‑model technologies, is being designed, deployed, and applied across marketing, risk control, and operations in the financial sector, highlighting trends, architectural principles, and future deployment scenarios.
This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.
Taobao’s Double 11 showcased AI assistants that deliver personalized recommendations, virtual try‑ons, and product comparisons while empowering merchants with AI‑generated content and large‑language‑model tools, illustrating how retrieval‑augmented generation and LoRA address scaling challenges and model hallucinations to create a seamless, data‑driven shopping experience.
This article reviews the evolution from MLOps to LMOps, outlines the fundamentals, challenges, and key technologies of large‑model operations—including inference optimization, prompt engineering, and context‑length extension—and presents Baidu AI Cloud's platform solutions and future outlook.
In a recent InfoQ Geek Talk, Baidu Search chief architect Tianbao discussed the rapid evolution of AI text‑to‑image technology—highlighting Chinese‑language data preparation, prompt‑engineering challenges, evaluation methods combining human feedback and metrics, and future video‑generation prospects—while announcing openings for visual algorithm engineers.
At CNCC 2023, leading researchers and industry experts convened to examine how large language models can transform recommendation systems, outlining four core challenges—model integration, fluency versus intelligence, hallucination versus deception, and user understanding—while highlighting opportunities such as multimodal content, cold‑start solutions, zero‑shot ranking, instruction‑driven algorithms, and responsible, interactive recommendation pipelines.
The inaugural Tsinghua‑Meituan Digital Life Joint Research Institute Academic Forum, scheduled for November 1 2023, will convene Academician Zheng Weimin, Tsinghua scholars, and Meituan technical experts to discuss intelligent unmanned systems in the era of large‑scale models, sharing cutting‑edge research and addressing emerging challenges.
The 23rd CCF C³ Forum, organized by Alibaba’s Alimama and the CCF CTO Club, examined how large‑model AI is reshaping intelligent business technology, from data‑driven to knowledge‑driven approaches, enhancing e‑commerce with smarter search, personalized recommendations, content creation, and guiding merchants on future AI‑native strategies.
The 2023 Inclusion·Bund Conference examined the rapid growth of large‑model AI, the resulting GPU shortage, and presented multi‑sector strategies—including policy guidance, ESG standards, AI‑driven optimization, and collaborative standards—to achieve sustainable, energy‑efficient computing across the entire ecosystem.
This article presents a comprehensive overview of data engineering practices for large model training, reviews current model scales and pre‑training data sources, discusses automated evaluation techniques, and explores how knowledge graphs can be integrated throughout the model lifecycle to improve quality and applicability.
The talk presents an overview of the MNN inference engine, outlines the end‑to‑end workflow for deploying large language models on mobile devices, discusses technical challenges and practical solutions, and concludes with future directions for edge AI deployment.
The DataFunSummit 2023 online summit (August 26‑27) will explore eight recommendation‑system topics—including core and engineering architecture, model training/inference, large models, graphs, cold start, and multi‑task scenarios—featuring Xiaohongshu leaders who will present on graph‑based business architecture, integrated training‑inference pipelines, and user/content cold‑start strategies.
The article examines Baidu’s inaugural Commercial AI Innovation Competition, highlighting its focus on AIGC commercial applications such as conversion behavior prediction and inference performance optimization, and explores how large‑scale models like PaddleBox and AI‑native tools are poised to transform content creation, marketing, and enterprise operations.
During a livestream titled “Solving the ‘Development Difficulty’ of Large Models with MindSpore Auto Parallel”, Huawei’s MindSpore experts explained how the framework’s distributed training techniques—including data, model, and pipeline parallelism as well as memory‑saving strategies—enable efficient pre‑training of trillion‑parameter models across diverse AI domains.
This article examines the storage challenges posed by large AI models, analyzes the full workflow from data ingestion to inference, compares HDFS and object‑storage data lakes, and presents Baidu's cloud‑native storage‑acceleration solutions—including RapidFS and PFS—that dramatically improve training speed, checkpoint handling, and model deployment throughput.
The CSIG‑hosted "Enterprise Visit – Into Qihoo 360" event on June 29, 2023 gathered over a thousand participants to explore multimodal and cross‑modal learning in the large‑model era, featuring keynote speeches from leading university researchers and Qihoo 360 AI experts, a tour of the company's facilities, and discussions on future AI research directions.
The article analyzes how the rise of large‑language models, the scarcity of AI‑optimized compute chips, and aggressive strategic investments by cloud giants such as Microsoft, Amazon, Google, and Oracle are reshaping competition in the cloud computing market and determining the next wave of growth.
This article explains the design and engineering of Baidu's AIPod high‑performance network, detailing the massive bandwidth, scalability, stability, and low‑latency requirements of large‑scale AI model training and the practical tools used to monitor and troubleshoot such workloads.
The DataFun Summit 2023 brings together leading experts to discuss large models and AIGC, featuring a keynote on voice cloning technology for AI sales assistants that covers multi‑speaker, multi‑accent cloning, naturalness optimization, and deployment strategies.
In a 2023 Zhi Guan Cun Forum speech, Baidu CEO Robin Li explains how large AI models are compressing human knowledge, transforming human‑computer interaction, redefining marketing and customer service, spawning AI‑native applications, and reshaping the entire technology stack, ultimately driving a new era of industrial growth.
At the 2022 China AI Industry Conference, leading scientists and industry experts presented cutting‑edge research on AI security, trustworthy mobile devices, adversarial model defenses, data‑free trojan detection, and large‑model challenges, highlighting certifications, practical implementations, and the need for continued collaborative innovation.
SenseTime introduced its visual‑centric multimodal large‑model platform SenseNova, detailing model scaling, extensive AI infrastructure, diverse industry deployments such as autonomous driving and generative content, and the challenges of compute efficiency and data acquisition in the race for advanced AI.
This article explores the concept and evolution of large foundation models, their transformative impact on AI-generated content, the underlying technologies such as transformers, diffusion, and CLIP, and discusses the challenges, emerging abilities, and future prospects of these models across multiple modalities.
The article examines the growing bottlenecks of large‑scale AI model training caused by the separation of storage and compute, analyzes why conventional GPU architectures cannot keep pace with exponential model growth, and presents in‑memory and near‑memory computing, as well as storage‑compute integration, as promising solutions to boost performance, energy efficiency, and scalability for cloud and edge deployments.
Pre‑trained large models are emerging as the foundational infrastructure for AI across industries; this article analyzes their technical advantages, application trends in NLP, CV and multimodal domains, presents a telecom customer‑service case study with performance benchmarks, and outlines future deployment challenges and research directions.
The article describes how Tencent's Taiji machine learning platform leverages cloud‑native mixed‑resource strategies—including online idle, tidal, and compute resources—to reduce training costs, improve stability, and support large‑scale AI model training for advertising and other services.
From Deep Blue’s 1997 chess victory to today’s generative AI breakthroughs like GPT‑3, DALL‑E, and large‑scale models, this article traces the rapid rise of artificial intelligence, highlighting key milestones, the impact of massive compute and data, and the societal implications of AI’s expanding capabilities.
The article reviews the rapid rise of AI‑generated content (AIGC) in 2022, outlines its historical development stages, highlights Baidu’s breakthrough video‑generation and search technologies, and discusses the severe AI talent shortage in China along with Baidu’s extensive training initiatives.
These notes explain how GPUs address the massive data, serial dependencies, and high computational complexity of modern AI by employing three acceleration strategies—parallelism, operator fusion, and simplification—illustrated with Megatron-LM, MoE models, and practical compression techniques such as quantization, distillation, and pruning.
This article explains how GPUs address the massive data, serial dependencies, and high computational complexity of modern AI by employing three acceleration strategies—parallelism, operator fusion, and simplification—detailing methods such as model, pipeline, and tensor parallelism, Megatron framework, MoE models, and various model compression techniques.
At QCon 2022, AI expert Feng Minwei shared practical insights on overcoming AI adoption challenges in SaaS service marketing, detailing cost‑reduction, industry expansion, and overseas deployment strategies, while showcasing real‑world cases of intelligent call‑center automation, knowledge‑base enrichment, and the impact of large‑model and generative AI.
The DataFun Summit 2022 brings together leading AI researchers and industry experts to discuss deep‑learning frameworks, ultra‑large model training, AI chips, compilers, MLOps, and end‑to‑end AI infrastructure, offering live streaming of six thematic forums and dozens of technical talks.
In this interview, the chief algorithm expert at Zuoyebang discusses how AI technologies such as NLP, speech recognition, large‑model pre‑training, and knowledge‑graph construction are applied to K‑12 education, covering practical challenges, deployment strategies, and future research directions.
Baidu Intelligent Cloud’s new Cloud‑native AI 2.0 platform tackles AI engineering bottlenecks by offering hybrid‑parallel large‑model training, flexible GPU virtualization, and an AI Accelerate Kit that boosts training efficiency over 50 % and cuts inference latency up to 63 %, raising GPU utilization from ~13 % to about 50 %.
The article introduces Whale, an open‑source distributed training framework that unifies multiple parallelism strategies, uses hardware‑aware load balancing to accelerate giant models like BERT‑Large and the trillion‑parameter M6 on heterogeneous GPU clusters, and details its architecture, planning, and real‑world performance gains.
The article examines the industry trend toward ever‑larger AI models, compares their parameter scale to the human brain, outlines the computational and memory challenges of training such models, and details advanced parallelism techniques and Baidu's high‑performance cluster solutions that enable efficient, stable large‑scale model training.
Amid post‑pandemic uncertainty, Baidu Research outlines 2022 AI breakthroughs—from large‑model advancements and cross‑modal knowledge enhancement to AI‑driven scientific discovery, privacy computing, quantum hardware, autonomous driving, green AI, and inclusive technologies—highlighting how these trends will reshape industries and society.
Alibaba Cloud’s PAI team and the DAMO Academy introduced the low‑carbon M6 trillion‑parameter multimodal model, demonstrating that their self‑developed Whale framework can train such massive models on just 480 V100 GPUs, reducing energy consumption by over 80% and boosting training efficiency nearly eleven‑fold.
