An award‑winning joint project by Shanghai Jiao Tong University and Ant Group unveils a multimodal foundation model and advanced detection techniques that dramatically improve AI‑driven content risk governance across massive online services.
Training massive AI models now commonly omits dropout because its original scaling trick fails to match training and inference distributions, leading to poorer performance, higher computational cost, and instability, while alternative regularization like normalization remains useful, as illustrated by practical observations and historical tricks.
JD Retail’s engineering team tackles hard AI problems by replacing a monolithic reward model with specialized small models for ad‑image generation, deploying an LLM‑driven query‑expansion pipeline that lifts conversion rates, and pruning text‑to‑image transformers using FFT and RDP to boost throughput 40% without loss, while building comprehensive evaluation tools and a semantic smart‑assistant.
This article examines how Retrieval‑Augmented Generation (RAG), AI agents, and multimodal large‑model techniques are reshaping AI‑industry integration, discusses their technical challenges and practical implementations, and outlines future development directions across algorithms, products, and domain‑specific applications.
Meituan's AI recruitment page showcases diverse opportunities across AI infrastructure, intelligent interaction, visual intelligence, and intelligent products, featuring roles from algorithm engineers to product managers working on cutting-edge technologies including large models, intelligent agents, and multimodal systems.
The article analyzes the rapid evolution of large‑model technologies—Retrieval‑Augmented Generation, autonomous agents, and multimodal AI—detailing their technical foundations, practical challenges, industry applications such as unified multimodal tasks, open‑world detection, and video moderation, and forecasting future development directions.
The article analyzes DeepSeek’s private‑deployment solutions, detailing selection criteria, deployment forms, service models, hardware‑software cost breakdown, technical innovations that lower model and compute barriers, and their impact on government and enterprise AI adoption.
This document outlines how large language models can be combined with evolutionary algorithms such as genetic algorithms to automatically generate, evaluate, and iteratively improve operations‑optimization code for logistics, resource allocation, and staffing scenarios, reducing development cycles, enhancing adaptability, and achieving higher solution quality.
Training today's massive AI foundation models demands extensive GPU resources and sophisticated multi‑GPU communication, making technologies like NVLink and NVSwitch crucial for efficient distributed training, while data‑parallel and model‑parallel strategies together optimize performance across large‑scale hardware clusters.
PaddlePaddle 3.0, launched on April 1 2025, introduces five core innovations—including dynamic‑static unified automatic parallelism, a training‑inference integrated PIR, high‑order automatic differentiation for scientific computing, a one‑stage CINN compiler, and heterogeneous multi‑chip adaptation—that dramatically reduce distributed‑training code, boost performance up to four‑fold, and extend the framework to aerospace, automotive, meteorology and life‑science applications while remaining fully compatible with the 2.0 API.
This article examines the current state and future prospects of large AI models and inference acceleration, covering technology trends, diverse application scenarios from research to industry, and the challenges and opportunities that lie ahead for intelligent data governance, multimodal agents, and AGI.
This article explores how high cardinality in Prometheus metrics impacts AI large‑model and autonomous‑driving observability, explains the underlying concepts, outlines the performance and cost challenges, and presents practical design, collection, and query‑side solutions—including metric modeling, pre‑aggregation, and remote‑read pushdown—to keep monitoring efficient and scalable.
The Ant Research Institute and Tsinghua University's Wu Yi team released AReaL‑boba 0.2, an open‑source reinforcement‑learning training framework that dramatically speeds up large‑scale model training, achieves state‑of‑the‑art mathematical reasoning results, and provides all code, data, and scripts for reproducible research.
This guide explains why programmers should embrace AI, outlines a four‑stage learning roadmap covering model fundamentals, practical development skills, advanced project work, and continuous community engagement, and lists mainstream large models, frameworks, and API platforms to get started.
This article traces the decade‑long evolution of AI training stability—from early small‑model manual operations to large‑scale, multi‑thousand‑GPU clusters—detailing metrics like invalid training time, fault‑tolerance architectures, eBPF‑based hidden‑fault detection, BCCL enhancements, multi‑level restart strategies, and trigger‑based checkpointing that together shrink downtime from minutes to seconds.
This article introduces major international and domestic AI large models, recommends desktop aggregation tools and APIs such as POE, Monica, and OpenRouter, and provides complete Python code examples for synchronous and streaming text and multimodal conversations, along with additional API and compute‑rental resources.
The article analyzes how the commercialization of large AI models is redefining cloud providers' competitive dynamics, highlighting Amazon Bedrock's DeepSeek‑R1 launch, IDC forecasts on model usage, major vendors' capital expenditures, and the shift toward flexible, cost‑effective multi‑model ecosystems for enterprise AI.
The article recounts a JD retail engineer’s rapid growth through tackling low‑resource large‑model training, developing a margin‑based dynamic data selection method (DynaMS) that earned an ICLR paper, and sharing practical insights on aligning business needs with cutting‑edge AI research.
The project develops a multimodal automatic layout generation system for e‑commerce by fine‑tuning the qwen‑vl‑7b vision‑language model with LoRA on poster and Taobao image‑layout data, employing diffusion‑based image generation and coordinate‑prediction methods to produce structured layouts that power poster, marketing image, and video‑cover creation with over 90% adoption, while exploring multi‑image, style‑aware, and iterative refinement extensions.
The article analyzes how DeepSeek’s unexpected success has triggered a strategic rethink across Chinese AI firms, prompting shifts from product‑centric growth to foundational model research, reshaping talent structures at Tencent and ByteDance, and questioning where the true barriers to AGI lie.
This article outlines a systematic AGI learning framework across five capability levels, recommends key papers and books, and provides practical steps for engineers to combine study with hands‑on large‑model projects, identify suitable use‑cases, and stay competitive in the evolving AI landscape.
This article examines the challenges of AI storage for massive models, describes Ant Group's multi‑cloud, high‑availability PCache architecture, and details its GPU‑mixed deployment, metadata services, data‑link optimizations, and performance results that enable petabyte‑scale training with low cost and high stability.
The DeepSeek open‑source week introduced seven breakthrough technologies—FlashMLA, DeepGEMM, DeepEP, DualPipe, EPLB, 3FS, and Smallpond—that together overhaul data flow, algorithmic complexity, hardware utilization, MoE communication, and resource balancing, dramatically improving large‑model training efficiency and lowering entry barriers for the AI industry.
This article describes JD Retail's AI engine built on domestic NPU chips, covering challenges, heterogeneous GPU‑NPU scheduling, high‑performance training and inference engines, extensive model support, real‑world deployment cases, and future plans for large‑scale chip clusters and ecosystem development.
Taobao’s AIGC video generation platform, built on a large‑scale “Faxiang” model that evolved from UNet to DiT, leverages over 2 billion curated e‑commerce videos, expert alignment, Lora fine‑tuning, and multi‑control capabilities to deliver diverse, high‑quality product videos that dramatically boost conversion metrics across the marketplace.
During DeepSeek’s Open‑Source Week (Feb 24‑28), five production‑tested projects were released, spanning GPU‑optimized MLA kernels, MoE communication libraries, high‑performance FP8 GEMM, dual‑pipeline parallelism, and a AI‑focused distributed file system, each delivering significant performance and efficiency gains for large‑scale AI workloads.
This issue reviews China's first open‑source short‑film model SkyReels‑V1, DeepSeek's Native Sparse Attention breakthrough, xAI's massive Grok‑3 deployment on 200k H100 GPUs, and a knowledge‑graph‑guided RAG framework, highlighting their performance gains, architectural innovations, and industry impact.
This report provides a comprehensive overview of large-scale AI models, explaining their definition, massive parameter and data requirements, underlying transformer architecture, classification into language, vision and multimodal models, notable examples such as DeepSeek, and a survey of popular AIGC tools and practical use cases.
WorldSense, a new benchmark of 1,662 real‑world video‑audio clips and 3,172 QA pairs across 26 cognitive tasks, reveals that current multimodal large models achieve only 25%–48% accuracy, highlighting the crucial role of combined visual‑audio input and the difficulty of audio‑ and emotion‑related reasoning.
The article defines Artificial Intelligence, explains how large models enable computers to mimic human intelligence for tasks and learning, and presents a personal view that machines may eventually surpass humans and evolve into a silicon‑based intelligent life with autonomous will.
The article presents DeepSeek founder Liang Wenfeng’s 60 reflections on artificial general intelligence, large‑model research, open‑source culture, talent strategy, and the broader AI ecosystem, while also highlighting his vision for democratizing AI and upcoming AI‑coding events in Beijing.
This article explains what defines a large AI model, compares parameter scales of GPT‑3, GPT‑4 and M6, and analyzes DeepSeek’s recent releases—V3, R1, and Janus‑Pro—highlighting their benchmark performance, reinforcement‑learning techniques, and cost efficiency versus leading proprietary models.
This article offers a comprehensive overview of large models, covering their historical evolution, technical foundations, the current "hundred‑model" competition, practical use cases across industries, and future challenges such as safety, controllability, and efficient deployment.
This guide outlines the hardware requirements for AI models ranging from lightweight 1.5 B parameters to massive 671 B models, detailing CPU cores, memory, GPU recommendations, storage needs, optimization tips, deployment suggestions, and suitable application scenarios.
FluxSR introduces a novel single‑step diffusion approach for real‑world image super‑resolution built on the 12‑billion‑parameter FLUX.1‑dev model, employing Flow‑Trajectory Distillation, TV‑LPIPS and attention‑diversity losses to achieve high fidelity, reduced artifacts, and lower memory and compute costs.
This article reviews the transformation of IT operations from manual processes to automation, AIOps, and ChatOps, and examines how large language models enhance intelligent assistance, automated diagnosis, and log analysis to improve efficiency, reliability, and rapid incident resolution.
The article analyzes the 2025 AI large‑model landscape, contrasting slowing foundational breakthroughs with fierce application competition, highlighting MiniMax’s low‑cost linear‑attention models, multimodal advances, and the strategic shift from price wars to sustainable, technology‑driven growth.
From January 16‑18, Huawei Cloud hosted a three‑day AI large‑model teacher training at Henan Information Statistics Vocational College, gathering over 40 educators from 18 schools; the program covered model fundamentals, prompt engineering, and industry‑education integration, boosting teachers’ AI expertise and fostering future AI talent development.
The interview explores how Baidu Cloud's Qianfan AppBuilder platform evolves from traditional coding to AI‑native low‑code development, detailing the impact of large‑model agents, Retrieval‑Augmented Generation, security, multimodal support, and future roadmap on enterprise productivity and digital transformation.
This article reviews the development of MLOps, introduces the emerging LMOps framework for large‑model engineering, outlines key architectural components, discusses current challenges and industry trends, and presents future directions and standardization efforts in AI operations.
The Wenxin China Tour’s Wuhan stop showcased the launch of Baidu’s PaddlePaddle AI Empowerment Center, presented industry‑wide AI ecosystem data, highlighted regional collaborations with universities and enterprises, and featured workshops and award ceremonies that illustrate the rapid adoption of large‑model technologies in Wuhan’s emerging AI market.
The article outlines how video search ranking has shifted from a tightly‑coupled multi‑stage cascade to an extensible, end‑to‑end, model‑centric framework called Rankflow, leveraging large‑model inference, decoupled recall, fine‑grained parallelism, and elastic compute allocation to boost performance, flexibility, and maintainability while paving the way for future retrieval‑augmented generation integration.
The article outlines how AI compute requirements have exploded since the early large‑model era, predicts a 500‑fold increase in the next ten years, presents market size and financing data, and notes a bundled collection of 44 technical PDFs for architects.
This article examines the rapid rise of AI large‑model technology across China’s financial sector and state‑owned enterprises, highlighting over 200 models deployed by 2023, detailed banking use‑cases, a growing portfolio of central‑enterprise projects worth millions, and the future shift from internal efficiency gains to outward customer‑facing innovation.
The article analyzes Microsoft’s 2024 AI initiatives—including a revamped business architecture, expanded Copilot capabilities, new Phi‑3.5 models, and ecosystem partnerships—to show how the company aims to boost enterprise efficiency and shape the future of AI‑driven productivity.
This article evaluates NVIDIA's China‑specific H20 and L20 inference chips, comparing their compute and memory‑bandwidth characteristics against A100, H100 and H200, and shows how they achieve superior throughput in large‑model inference despite reduced specifications.
This issue reviews the latest AI breakthroughs—including OpenAI’s O3 and o1 models, pricing cuts, new features in ChatGPT, product launches like Pika 2.0 and Gemini 2.0, a heated debate on pre‑training data bottlenecks sparked by Ilya Sutskever, a novel black‑box forgetting method, and DeepMind’s Genie 2 3D world generator—highlighting how industry dynamics and research directions are reshaping the field.
This article explains how large‑model AI can be integrated into security operations (AISECOPS) to simplify application integration, improve fault detection, and automate protection across complex north‑south and east‑west network layers, while addressing challenges such as data quality, cost control, model selection, and safety frameworks.
Tencent’s Angel machine learning platform, recognized for breakthroughs in trillion‑scale model training, inference, and deployment, won the 2024 World Internet Conference Leading Technology Award, highlighting its self‑developed hardware‑software stack, high‑performance networking, and extensive real‑world AI applications.
This presentation reviews recent research on data security and utilization in the large‑model era, covering privacy‑preserving federated learning, knowledge‑transfer techniques, prototype‑based modeling, multi‑model fusion methods such as FuseGen, and introduces the federated knowledge computing platform FLAIR for both horizontal and vertical federated scenarios.
At the 2024 Global Machine Learning Conference in Beijing, 360 AI Research Institute showcased cutting‑edge multimodal large‑model research, fine‑grained open‑world object detection, and document understanding technologies, highlighting open‑source releases, real‑world deployments, and competitive achievements in AI competitions.
Alibaba's anti-fraud platform DeepString uses large unsupervised models to detect abnormal traffic in real time across multiple advertising products, combining a foundation model for event mining, anomaly measurement, and an alignment model for online filtering, reducing reliance on manual labeling and domain expertise.
At Baidu World 2024, CEO Robin Li unveiled the iRAG retrieval‑augmented image generation model that dramatically reduces hallucinations and introduced the no‑code platform “Miaoda,” showcasing intelligent agents as the next mainstream AI application while highlighting explosive growth in daily model usage.
The article analyzes the development of AI compute infrastructure, detailing the three‑level architecture from chip to cluster, the scaling law linking model parameters to compute demand, the rapid growth of massive “ten‑thousand‑card” clusters worldwide, and the emerging demand for inference workloads driving new deployment and scheduling strategies.
This article surveys recent advances in automatically converting UI design drafts into code, reviewing solutions from Meituan, Xianyu, Microsoft, a range of design‑to‑code tools, Flutter‑specific generators, JD's Ling platform, and practical experiments with large language models for Flutter code generation.
At the 7th China International Import Expo forum, Baidu's AI chief explained how large models, with their superior performance, strong generalization, and standardized development processes, are driving a new wave of industrial transformation across sectors such as transportation, finance, and scientific research.
This article explores how AI large models are transforming the financial sector through intelligent advisory, automated strategy generation, risk prediction, asset allocation, and other applications, presenting detailed implementations, real-world case studies, and discussing future opportunities and challenges such as data privacy, model transparency, and regulatory compliance.
This article explains the concept of a technology maturity curve, why it should be evaluated, and how user profiling and tag systems evolve under the influence of large‑model AI, detailing seven key assessment dimensions and a comprehensive architecture that guides enterprises in strategic decision‑making.
This article examines the challenges of scaling large AI models across multiple GPUs, explores data, pipeline, and tensor parallelism, analyzes collective communication patterns and data‑channel technologies such as PCIe, NVLink and RDMA, and offers concrete optimization recommendations to boost training efficiency.
The article examines how large language models are reshaping JD Retail's e‑commerce search and recommendation pipelines, detailing industry evolution, technical challenges such as knowledge hallucination, intent understanding, personalization, cost, and safety, and presenting JD's end‑to‑end AIGC architecture, data preprocessing, alignment, evaluation, and next‑generation AI search solutions.
The article summarizes Alibaba Cloud's 2024 conference talks on AI‑enhanced observability, presenting a cloud‑native GitOps solution for massive clusters and showcasing large‑model applications in intelligent Q&A and diagnosis to improve operational stability, cost, and efficiency.
This article describes Shujia Technology's use of artificial intelligence to generate marketing images and videos, outlining the background, challenges of high-volume content production, detailed solutions for image and video assets—including layout models, diffusion models, and digital human synthesis—and future research directions.
This article analyzes Baidu's Baige 4.0 AI infrastructure, detailing its four‑layer architecture, XMAN 5.0 hardware, HPN network, BCCL communication library, and AIAK inference upgrades, and explains how these innovations address large‑model training and inference challenges while boosting performance, utilization, and cost efficiency.
The AI industry’s profit landscape mirrors a smile curve, with upstream GPU manufacturers and downstream application developers capturing most returns while costly large‑model R&D yields low margins, prompting predictions of GPU valuation corrections, a push for consumer‑facing killer apps, and massive application turnover through creative destruction.
At the 2024 Yunqi Conference, Alibaba Cloud’s AI Infra experts detailed the latest challenges of large‑model deployment—such as hardware costs, resource management, and software‑hardware coordination—and introduced PAI’s new capabilities, including stability tools, automated distributed training, reinforcement‑learning frameworks, inference optimizations, and integrated big‑data AI solutions.
This article explains how large language models enhance data governance by improving data quality, reducing implementation costs, and increasing operational efficiency through knowledge bases and interactive prompt libraries, and it also outlines practical empowerment pathways for organizations seeking to leverage AI-driven analytics.
The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.
JD's advertising research team combines deep learning, multimodal modeling, reinforcement‑learning auctions, and generative recommendation to boost ad relevance, improve long‑tail product exposure, and overcome large‑model inference challenges in a high‑traffic e‑commerce environment.
This article explains how the TAI platform leverages Kubernetes and Volcano to tackle fault, efficiency, and usability challenges in large‑model training and inference, detailing custom resources, automated fault detection, and advanced scheduling strategies that boost resource utilization and performance.
Training deep learning models on a thousand GPUs faces steep communication overhead, higher failure probability, and scaling inefficiencies, but by profiling each step, overlapping compute and communication, using gradient bucketing and accumulation, and employing elastic training techniques, practitioners can approach near‑linear performance while mitigating common pitfalls.
In his keynote at the 2024 Inclusion·Bund Conference, HKUST Board Chair Shen Xiangyang discusses how large‑model AI reshapes human‑computer interaction, introduces the concept of Intelligent Augmentation, emphasizes responsible AI governance, and outlines the practical steps needed to deploy AI agents in industry.
At the 2024 Inclusion·Bund Conference, the World Digital Technology Academy (WDTA) unveiled the first international standard for large‑model supply‑chain security, a collaborative effort by CSA Greater China, Ant Group, Microsoft, Google, Meta, PrivateAI and others, marking a significant step in global AI governance and trust.
This article explores how AI large‑model technology can be integrated into DevOps pipelines to automatically detect, classify, and resolve interruption events, dramatically reducing manual troubleshooting time and improving overall software development and operations efficiency.
The 2024 Inclusion·Bund Conference hosted a forum on "Large Model Creativity Boundaries and Application Imagination," featuring leading AI experts who discussed agents, multimodal technology, knowledge graphs, announced a new industry alliance, unveiled three major model products, and presented a trustworthy AI framework report for finance, healthcare, and government sectors.
Ant Group and the China Academy of Information and Communications Technology released a research report outlining a trustworthy application framework for large models in rigorous sectors such as finance and healthcare, detailing technical safeguards, industry case studies, and guidance for scalable, secure AI deployment.
The answer argues that fresh graduates should join pre‑training teams because the required engineering tasks—large‑scale data crawling, Hadoop/Spark pipelines, torch and CUDA setup, megatron code debugging, and scaling‑law experiments—rapidly sharpen coding skills, while SFT work focuses mainly on data labeling and offers slower technical growth.
This article explains the challenges of scaling large AI models, introduces PaddlePaddle 3.0's four‑dimensional hybrid parallelism and its unified automatic parallel framework, details core concepts such as ProcessMesh and Placements, provides step‑by‑step code examples, and outlines performance‑optimizing strategies like operator fusion and pipeline scheduling.
This article presents a comprehensive technical overview of how large AI models are reshaping financial data production, governance, multimodal document understanding, lakehouse storage, private‑domain model deployment, data‑centric engineering methods, and multi‑agent intelligent advisory within the finance sector.
This article explores how the rise of massive AI models reshapes data governance, detailing model fundamentals, architectural types, emerging challenges, a five‑domain governance framework, and practical AI‑driven applications for data standards, metadata, quality, and security, while also looking ahead to future trends.
In this interview, JD Retail’s AI director Zhai Zhouwei outlines the four historical stages of e‑commerce search, explains how large‑model AI is reshaping user interaction, retrieval and content generation, discusses practical challenges and solutions, and shares his vision and advice for enterprises adopting these technologies.
This article presents a comprehensive overview of AI‑enabled security operations, detailing the industry pain points, the AISECOPS workflow, model selection between OpenAI embeddings and ST5, classification methods, performance and cost evaluations, and future directions for integrating agents and secure AI pipelines.
The article surveys the rapid emergence of large AI models and AIGC, explains core concepts like AI, AGI, and LLMs, details prompt‑engineering techniques such as chain‑of‑thought, outlines a seven‑layer AIGC stack, discusses technical and ethical challenges, and highlights future multimodal and industry‑specific applications.
This article explains what artificial intelligence (AI) is, how it differs from traditional programming, outlines its main categories, introduces machine learning, deep learning, neural network models such as CNN, RNN, and Transformer, describes large models and GPT, and discusses AI’s wide‑range applications and societal implications.
This presentation details Alibaba Cloud's AI platform, covering the end‑to‑end pipeline from big‑data processing and feature engineering to large‑model training, inference optimization, recommendation system architecture, and RAG applications, highlighting practical engineering solutions and performance gains.
At the China Multimedia 2024 conference in Yinchuan, Kuaishou presented its latest AI‑driven large‑model technologies—including text‑to‑image, text‑to‑video, and audio models—alongside advances in intelligent video coding, a new research‑fund initiative, and recent industry awards.
This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.
This article examines how JD Express applies large‑language‑model‑based natural language processing to accurately recognize and classify shipped items, addressing low matching rates, improving packaging recommendations, reducing damage and claims, and outlining architecture, model selection criteria, caching strategies, and future operational benefits.
This article presents Ping An's comprehensive approach to data security compliance, detailing its evolving data management framework, the integration of large‑model AI for classification, risk monitoring, and assessment, and practical insights from a Q&A session on governance and operational challenges.
This article examines the Model-as-a-Service (MaaS) paradigm, tracing its origins, defining its expanded capabilities for large‑model ecosystems, outlining the full‑stack services it offers, and analyzing current industry adoption, deployment models, and the technical and regulatory challenges that must be addressed for scalable AI rollout.
The article analyzes the rapid evolution of AI large models—from their role in advancing AGI through massive pre‑training and fine‑tuning, to current market dynamics led by GPT and domestic Chinese players, and finally to future multimodal applications, content‑factory capabilities, and emerging AIGC revenue models projected to reach trillion‑yuan scales by 2030.
This article outlines key denoising methods—including data cleaning, augmentation, regularization, adversarial training, and self‑supervised learning—that improve the performance, generalization, and robustness of large neural network and transformer models.
The article analyzes current challenges in deploying large AI models, covering robot automation, scaling‑law limits, vertical‑domain use cases, multimodal breakthroughs, algorithmic evolution, and the hardware‑software trade‑offs of training and inference infrastructures, while questioning ROI and practical feasibility.
The 2024 WAIC Privacy Computing Forum gathered leading experts from academia and industry to discuss how encryption, anonymization, and secure multi‑party computation can protect data privacy while enabling large‑model training and inference, highlighting technical challenges, standards, and emerging solutions across AI, big data, and information security domains.
The two‑day DataFunCon 2024 Beijing conference gathered hundreds of big‑data and AI experts to discuss the evolution from data lakes to lake‑warehouses, large‑model development, practical applications, and future strategies for enterprises, while showcasing partner exhibitions and a vibrant community spirit.
The DataFunCon 2024 Beijing conference opened with keynote speeches on the evolution of Alibaba Cloud's big data platform, explored distributed data warehousing, large model research, and practical AI applications, and concluded with a round‑table discussing future trends and enterprise strategies for big data and AI integration.
This article reviews the transformation of IT operations from manual processes to automation, AIOps, and ChatOps, and explains how large language models can further enhance intelligent monitoring, automated diagnosis, and knowledge‑base assistance to improve system stability and response speed.
This presentation reviews the technical scope of document intelligence, its specific applications and challenges in finance, recent advances in document analysis, recognition, and understanding, and outlines future research directions for large‑model and multimodal solutions in processing complex financial documents.
This article examines how large pre‑trained models such as GPT‑4 and BERT are integrated into modern recommendation systems, detailing their advantages, implementation strategies, real‑world e‑commerce case studies, and the technical and privacy challenges that must be addressed for effective deployment.
This article provides a comprehensive overview of user profiling algorithms, covering the evolution from ontology‑based traditional methods to modern deep‑learning approaches, including structured label prediction, representation learning, active learning, and large‑model integration, while discussing challenges, practical applications, and future research directions.
The DataFunSummit2024 Recommendation System Architecture Summit invites participants to explore cutting‑edge advances in large‑model recommendation, training and inference optimization, feature engineering, multi‑task modeling, and graph‑based techniques through a series of expert talks and panel discussions from leading industry and academic researchers.
