This article reviews the 70‑year evolution of databases, explains how vector databases and Retrieval‑Augmented Generation (RAG) are reshaping AI applications, and details Baidu Intelligent Cloud's VectorDB architecture, performance advantages, real‑world use cases, and future trends in data engineering.
This article examines Baidu's AI‑driven overhaul of its app personal center, detailing the problems of the legacy design, the innovative container and recommendation framework, card‑based content structures, dialogue integration, experimental outcomes, and future design insights for AI‑enhanced user experiences.
This article explains how Elasticsearch’s advanced search capabilities—including vector and semantic search, hardware acceleration, hybrid retrieval, model re‑ranking, multi‑vector support, and integrated security—enable robust RAG implementations and outlines future directions such as a new compute engine, stronger vector engines, and cloud‑native serverless deployment.
This article explores the evolution of data lakes for AI, discusses the challenges of AI-era data management, introduces Apache Iceberg and its architecture, demonstrates PyIceberg-based AI training and inference pipelines, and presents vector table designs with LSH indexing and performance optimizations.
This article presents JD.com’s multimodal recommendation system architecture, covering content understanding, multimodal ranking and recall models, practical deployment pipelines, and future research directions such as large‑model integration and supply‑side generation, all illustrated with detailed diagrams and Q&A.
The article provides an in‑depth technical overview of NVIDIA's Grace Hopper superchip, detailing its heterogeneous CPU‑GPU architecture, high‑bandwidth NVLink‑C2C interconnect, unified memory model, programming support, and system‑level scaling features that together deliver unprecedented performance for high‑performance computing and large‑scale AI workloads.
The 2024 Inclusion Bund Conference in Shanghai announced the winners of its newly added AI Innovation Competition, including the AFAC Financial Intelligence Contest and the Global Deepfake Attack‑Defense Challenge, highlighting participation from over 7,000 teams across more than 20 countries and showcasing cutting‑edge deepfake detection achievements.
This article explains how to implement and run a Vision Transformer (ViT) model in PHP using the phpy extension, covering ViT fundamentals, installation of Python dependencies, full PHP and Python code examples, and practical application scenarios for PHP developers.
The answer explains that joining a pre‑training team accelerates a newcomer’s engineering abilities through hands‑on work with large‑scale data pipelines, distributed training code, and debugging, while SFT teams focus mainly on data labeling, making pre‑training the more effective path for rapid skill growth.
This article explains how Google’s Gemini Nano can run directly in the browser via the Prompt API, guides you through enabling the feature in Chrome Canary, checking model readiness, and provides JavaScript code examples for creating text sessions, streaming responses, and building a simple translation demo.
This article presents a comprehensive technical walkthrough on enhancing large language model mathematical reasoning by reviewing model architectures, introducing mixed CoT‑PoT instructions, generating and filtering synthetic data, and applying multi‑stage training optimizations such as RFT, PPO, and DPO, with detailed experimental results and Q&A insights.
This article explains why generic large language models struggle with domain‑specific data, introduces Retrieval‑Augmented Generation (RAG) as a solution, compares Word and PDF formats, outlines document‑parsing pipelines, reviews open‑source PDF tools, and presents Alibaba Cloud's rule‑based parsing architecture with performance results.
This article presents a collection of Python utility scripts that demonstrate how to clean CSV data, translate text files, synchronize folders, upload files to S3, count directory contents, classify files by type, perform OCR on images, convert video to audio, extract images from webpages, and generate text summaries using modern libraries.
The article outlines how to effectively deploy large language models by defining their capabilities, integrating high‑quality data, establishing scientific evaluation and statistical analysis methods, and illustrates these practices with a logistics industry case study.
This guide explains how to reduce the computational cost of large language models by preparing instruction data, optionally augmenting or refining it, deploying teacher and student models on PAI, and performing distillation training with detailed hyper‑parameter settings and sample Python scripts.
This article details Tencent Game's end‑to‑end approach to building intelligent NPCs, covering the opportunities brought by AI, the practical implementation of multimodal LLM‑driven dialogue, knowledge‑augmented retrieval, long‑context handling, safety measures, multimodal expression (voice and facial animation), and system‑level performance optimizations for real‑time deployment.
Matt Welsh argues that despite fifty years of programming advances, humans remain poor at coding, and in the post‑AI era large language models will reshape software engineering, prompting a shift toward prompt engineering, new team roles, and a revival of fundamental engineering practices.
The article outlines the role of the content compliance domain in e‑commerce, detailing user‑facing issues, business responsibilities, challenges in detection and mitigation, and technical solutions such as comparable‑price models, large‑scale price prediction, and merchant outreach, while also offering personal growth advice for compliance engineers.
This article explains how e‑commerce platforms use AI‑driven content compliance to detect malicious products, price manipulation, and counterfeit goods, outlining the technical challenges, core business metrics, model‑based solutions for price over‑pricing, and personal growth advice for compliance engineers.
This article explores how AI-powered large language models enable Chinese commercial banks to automate, accurately match, and predict regulatory requirements, detailing new use‑cases, a prompt‑engineering framework, and the resulting efficiency and risk‑reduction benefits for the financial sector.
This article explains the necessity of vector databases for AI workloads, reviews the PostgreSQL ecosystem, compares vector database options, provides detailed PgVector installation and usage steps, shares operational best‑practices, performance tuning tips, and real‑world deployment cases at Qunar and Tujia.
At the 2024 OpenSource Summit China in Hong Kong, Linus Torvalds and Dirk Hohndel discussed the Linux kernel’s development roadmap, release cadence, security practices, the slow adoption of Rust, and the potential role of AI, while sharing candid views on cloud, Kubernetes, and legacy kernel support.
This article examines the urgent need for early skin cancer detection in China, outlines the challenges of dermatological imaging, and details a low‑code PaddleX solution that leverages PP‑LiteSeg‑T for data preparation, model training, optimization, and deployment to improve diagnostic accuracy and efficiency.
This article explores how mature testing teams can leverage large language models for automated code review, outlining the advantages, challenges, and a practical implementation using FastGPT and GitLab CI to build a low‑cost, AI‑enhanced review system that improves efficiency and feedback quality.
This article explores the progression of IT operations—from manual processes through automated DevOps, to AI‑driven AIOps and chat‑based ChatOps—examining concepts, advantages, tools, and future possibilities, while also reflecting on how these trends reshape the role of operations engineers.
This article presents a comprehensive overview of EasyRec's recommendation system architecture, detailing training and inference optimizations, distributed deployment strategies, operator fusion techniques, online learning pipelines, and network-level improvements to enhance performance and scalability.
The paper proposes PODM‑MI, a mutual‑information‑driven, preference‑oriented diversity model that jointly optimizes accuracy and diversity in e‑commerce search re‑ranking by modeling user preferences with multivariate Gaussian distributions and adapting rankings via a learnable utility matrix, showing significant gains in JD's main search experiments.
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.
A recent evaluation had six leading large‑language‑model products (GPT‑4o, GLM‑4, Wenxin 4.0, Doubao, Baichuan 4, and Qwen‑2.5) answer the first 14 objective questions of the new Gaokao mathematics I paper, revealing that only GLM‑4 surpassed the 60% passing threshold while the others performed far below expectations.
This article presents a comprehensive approach to enhancing large language models' mathematical reasoning by reviewing model architectures, introducing mixed CoT‑PoT instructions, generating and filtering synthetic data, and applying multi‑stage training optimizations such as RFT, PPO, and DPO, with detailed experimental results and Q&A.
On August 24, 2024, Xiaohongshu’s technical team will livestream a four‑hour REDtech session across WeChat Channels, its recruitment account, and Bilibili, showcasing recent top‑conference papers—from ACL and CVPR to ICLR and AAAI—covering innovations such as KV‑cache compression, zero‑shot image generation, early‑stopping self‑consistency, negative‑sample‑aware distillation, and real‑time nearest‑neighbor search, while allowing live interaction and offering surprise merchandise.
This article describes an AI-driven solution that uses vector databases and large language models to automatically generate, evaluate, and maintain training exam questions for aviation maintenance personnel, addressing high document volume, frequent updates, and low training effectiveness.
TransformersPHP brings Hugging Face’s Transformer models to PHP, enabling developers to run thousands of pre‑trained NLP models locally for tasks like text generation, summarisation, and translation, with simple installation, ONNX‑based execution, and a Python‑like pipeline API.
This article presents JD's multimodal content‑understanding framework, detailing its five‑M business characteristics, the architecture of multimodal recall and ranking models, the GMF and MIN modules for semantic alignment and personalization, and future research directions involving large language models and end‑to‑end multimodal encoding.
This article surveys the most suitable programming languages for artificial intelligence, detailing why Python, R, Java, LISP, Prolog, C++, Haskell, JavaScript, and Julia each excel in AI development, and provides practical FAQs for developers choosing the right language.
This article explains how to construct a multi‑agent AI research assistant using LangGraph and the open‑source GPT‑Researcher project, detailing the system architecture, agent roles, state design, workflow creation, parallel sub‑processes, and code examples for autonomous online research and report generation.
This article explains how HuoLala leverages large language models, multimodal feature integration, and retrieval‑augmented generation to automatically determine responsibility for order cancellations, improving accuracy, explainability, and driver‑user experience.
The 2024 AI‑powered PPT market has surged to nearly ten million users, with Baidu Wenku capturing about 80% of the share, while competitors like WPS AI PPT and Ai PPT lag behind, driven by superior multi‑modal generation, higher quality layout, and extensive content resources.
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.
This tutorial walks you through building a fully functional Pong game in Python using the turtle library, featuring an AI paddle that automatically tracks the ball, complete with step‑by‑step code snippets and a complete source listing for easy copy‑paste implementation.
This article reviews the evolution of telephone sales, introduces large model technologies, outlines their integration into intelligent telemarketing workflows, discusses practical implementation methods, challenges, and future trends, and shares insights from industry experts on optimizing AI‑driven sales automation.
RAG technology leverages vector databases to provide context‑aware answers without updating model parameters, and this article explores how cloud search teams integrate multiple vector algorithms, balance cost, stability and latency, and adopt open‑source solutions like OpenSearch to build scalable, enterprise‑grade retrieval systems.
The article details how Taobao’s AI team automated repetitive UI tasks for B2C front‑end development, achieving a 15 % efficiency gain across five projects, and outlines key challenges—prompt cost, low OCR accuracy, hallucinations, excess nodes, and customization variance—along with practical solutions such as a dedicated evaluation platform, OCR translation, model upgrades, prompt segmentation, output simplification, and a reusable component library.
At the 2024 China Cryptography Society Cryptographic Chip Academic Conference in Chengdu, Ant Group’s Vice President Wei Tao highlighted the evolution from general to intelligent to secure computing, emphasizing the pivotal role of cryptographic chips in data protection, AI development, and cross‑industry applications while calling for deeper industry‑academia collaboration.
This article demonstrates how to diagnose and fix the MySQL ERROR 1292 caused by assigning an empty string to a DECIMAL column, using the ChatDBA AI assistant to analyze the issue, explain strict mode, and provide a safe SQL solution.
This article details how a large‑model AI combined with a vector‑based knowledge base can automatically generate, update, and validate exam questions for airline maintenance training, addressing the challenges of massive documentation, frequent updates, and manual question creation.
The article describes how JD Aviation’s maintenance department uses a vector‑based knowledge base and large‑language‑model services to automatically generate, evaluate, and maintain training exam questions, addressing the rapid growth of manuals, frequent updates, and the heavy manual workload of traditional test creation.
Understanding the technology flywheel—a model inspired by physics that links scientific breakthroughs, market demand, and funding—helps predict which emerging technologies, from AI to quantum computing, will accelerate rapidly, while highlighting the need for scientific thinking, market insight, and capital awareness.
This roundup covers India's TRAI leveraging AI and blockchain to curb telecom scams, Microsoft's new Windows 11 power‑mode for better battery life, the removal of the Aria download tool after abuse, the launch of Flutter 3.24 with Dart 3.5, and KDE Frameworks 6.5.0 updates.
In a candid Stanford talk, former Google CEO Eric Schmidt warned that AI breakthroughs demand massive investment, larger context windows, and aggressive talent strategies, while highlighting CUDA's dominance, the rise of AI agents, geopolitical energy concerns, and the need for organizational innovation to unlock AI's full potential.
This article explains what large language models (LLMs) are, how they are trained, their diverse applications across industries, the challenges they face, and provides a practical Python example using Replicate to run Meta's Llama 3‑70b‑instruct model.
In a Stanford talk, former Google CEO Eric Schmidt warned that massive token context windows, AI agents, and text‑to‑action capabilities will reshape the industry, while also sparking controversy over his remarks on work culture and prompting a public apology.
Effective prompt design for large language models requires clear goals, relevant context, explicit input/output formats, evaluation criteria, and illustrative examples, combined with specific language, step‑by‑step instructions, edge‑case handling, ethical considerations, and proper tokenization, encoding, decoding, and post‑processing to produce accurate, concise, low‑hallucination responses.
The presentation outlines the challenges of sparse data and delayed learning effects in youth programming education, introduces three technical breakthroughs—dual‑data model training, hierarchical knowledge‑graph prompting, and reinforcement‑based cognitive recommendation—and showcases product implementations such as the Frog Programming Platform, AI learning machine, and digital‑human recorded courses.
Redis has introduced a multithreaded query engine that dramatically boosts throughput and lowers latency for vector searches, enabling scalable, real‑time retrieval‑augmented generation (RAG) workloads while preserving the low‑latency performance of its core in‑memory database.
This article examines the pain points of a custom ticket system, introduces FastGPT’s knowledge‑base and query capabilities, outlines integration architecture and concrete features, and shows how the combined solution reduces ticket resolution time dramatically while improving overall operational efficiency.
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.
The article explains how to use PaddlePaddle’s sparse computing features—including basic sparse tensor formats, creation and manipulation of sparse tensors, and building and training sparse neural networks such as a sparse ResNet—to improve memory efficiency and accelerate training on large, zero‑rich datasets.
This article compares the high costs and technical challenges of building a private large language model with the benefits, flexibility, and lower risk of using Retrieval‑Augmented Generation (RAG) on a general LLM, offering practical guidance for testing teams seeking AI assistance.
The JD Group Project Management Talent Development Channel showcased a series of expert talks and a round‑table at the 3rd China PMO&PM Conference, covering R&D efficiency, AI‑driven project work, overseas delivery challenges, and best practices that underline JD's leadership in project management.
This article summarizes Alibaba Cloud researcher Xu Sheng's presentation on the company's big data and AI product portfolio, covering current offerings, market trends, lakehouse evolution, open‑source contributions, serverless solutions, search capabilities, and the future roadmap for integrated big data‑AI services.
The article examines 2024’s key AI developments—including California’s partnership with NVIDIA to boost AI education, novel robot‑training systems that bridge virtual and real worlds, and the growing deepfake threat—analyzing their societal impact and future implications.
Deploying large language models faces domain gaps, hallucinations, and high barriers, so Retrieval‑Augmented Generation (RAG) combines retrieval with generation, and advanced optimizations—such as RAPTOR’s hierarchical clustering, Self‑RAG’s self‑reflective retrieval, CRAG’s corrective evaluator, proposition‑level Dense X Retrieval, sophisticated chunking, query rewriting, and hybrid sparse‑dense methods—are essential for improving accuracy, reducing hallucinations, and achieving efficient, scalable performance.
This article presents a comprehensive overview of Xiaohongshu's heterogeneous training and inference engine, covering the challenges of model engineering, the design of elastic heterogeneous engines, future HPC training frameworks, AI compilation techniques, and a forward‑looking outlook on scalability and performance.
Discover twelve carefully curated open‑source utilities—from IDEs and API testers to AI‑powered terminals and container orchestrators—that can dramatically boost developer productivity and streamline everyday coding workflows.
This article explains the fundamentals of large language models, covering tokenization, probability prediction, Markov chain basics, training data limitations, context windows, and the transition to neural network architectures like Transformers, while providing Python examples and insights into model scaling and the illusion of intelligence.
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.
This article examines the hype versus reality of AI-powered developer tools, outlines their current limitations such as lack of context awareness and reliability, categorizes the various tool types, and proposes ways to integrate AI more effectively into the software development workflow.
This article introduces LangGraph, a Python library for creating stateful, multi‑agent LLM workflows, explains its loop, persistence, and human‑in‑the‑loop features, shows how to install it, and provides a complete code example that builds, runs, and reuses a searchable AI agent with thread‑level state saving.
The article examines DARPA's TRACTOR project, which aims to use large‑language‑model AI to automatically translate legacy C and C++ code into safe Rust, discusses the memory‑safety motivations, technical challenges, community reactions, and the broader push by US agencies to abandon unsafe languages.
This article explains the fundamentals of Microsoft GraphRAG, details its indexing pipeline—including text chunking, entity‑relationship extraction, community detection, and description generation—shows how to set up the graphrag library, create adaptive prompts, build the index, and import the resulting graph into Neo4j for visualization and analysis.
The article presents a practical method for evaluating 1‑million‑token LLMs by inserting structured medal data into a classic Chinese novel, provides a full Python script for the test, shares results on GLM‑4‑long, and discusses training techniques and open‑source resources for long‑context models.
This article presents JD Advertising's technical practices for improving GPU throughput and reducing latency in large‑scale recommendation scenarios, covering system challenges, storage and compute optimizations for training, low‑latency inference techniques, and compiler extensions to handle massive sparse models.
The announcement details a DataFun Summit 2024 session where Du Xiaoman AI researcher Huo Liangyu will present on the challenges, development, and alignment methods of the Xuan Yuan financial large language model, highlighting RLHF techniques, data collection, and real‑world deployment insights for the finance sector.
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 presents NetEase Cloud Music's multi‑scenario recommendation modeling work, detailing background, overall system architecture, key modules, modeling goals, technical difficulties, performance improvements, future outlook, and a comprehensive Q&A session that addresses practical deployment challenges.
The article presents a self‑supervised framework that uses a regional token structure within a Vision Transformer to accurately locate video plagiarism segments, dramatically reducing annotation costs and achieving state‑of‑the‑art performance without manual labeling, while also highlighting its real‑world deployment for copyright protection.
This article introduces an AI‑driven Smart Report Assistant Bot that automatically converts uploaded Excel files into recommended charts, allows users to customize reports, and details the underlying workflow—including Excel parsing, LLM‑generated SQL, dynamic table creation, chart rendering with ECharts, and image‑merging plugins.
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.
The article explains what AI large‑model integrated machines are, why they are needed for data‑sensitive sectors, and presents market data showing a rapidly growing demand in China that could reach a trillion‑yuan scale, especially as domestic alternatives replace imported GPUs.
The July 14 DeWu tech salon showcased how large language models are reshaping frontend development, featuring insights from NetEase, Alibaba, and DeWu experts on AI‑driven low‑code platforms, intelligent coding assistants, and practical implementation strategies, with over 20,000 online viewers.
This guide walks through creating a full‑featured AI‑native API gateway using Higress, covering architecture setup, AI agent integration, observability, content security, token rate limiting, caching, retrieval‑augmented generation, prompt templates, and intelligent request/response transformation with concrete configuration examples.
This article introduces heuristic algorithms—strategies that use experience and trial to quickly find approximate solutions for complex, often NP‑hard problems—detailing seven popular methods such as Greedy, Tabu Search, Simulated Annealing, Ant Colony, Genetic, Particle Swarm, and Artificial Bee Colony, and highlighting their principles, steps, and real‑world insights.
The article traces the lineage of Go’s technical leadership, explains Russ Cox’s shift to AI, and details the Oscar open‑source contributor‑agent architecture that uses large language models to automate maintenance tasks while preserving deterministic code execution.
Apple Intelligence, an on‑device AI suite debuting with iOS 18.1 beta, centers on the Apple Foundation Model—a 3‑billion‑parameter on‑device LLM (and a larger undisclosed cloud version) trained on TPUs with novel RL algorithms and mixed‑precision quantization, delivering Siri, writing assistance, photo search, and benchmark performance that surpasses GPT‑4, though currently limited to paid developers.
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 engineering‑focused document reviews transformer fundamentals, derives precise FLOP and memory formulas for attention and feed‑forward layers, defines the MFU performance metric, analyzes memory components and parallelism strategies, examines recent architecture variants such as MQA, GQA, sliding‑window attention and MoE, and provides practice problems applying these calculations.
MimicMotion is a controllable video generation framework that produces smooth, high-quality human motion videos by leveraging skeletal action guidance, addressing challenges in video generation such as limited length, weak controllability, and lack of dynamic detail.
Galileo’s Hallucination Index evaluates 22 leading generative AI models using a contextual‑adherence metric, ranking Claude 3.5 Sonnet as the overall RAG leader, Gemini 1.5 Flash as the most cost‑effective, and highlighting open‑source and context‑length performance nuances for AI practitioners.
This article explains why true observability is essential for modern software, outlines its five core pillars, details a four‑stage maturity model with benefits and drawbacks, and provides practical steps—including data collection, team organization, and AI automation—to advance from basic monitoring to predictive, self‑healing systems.
The paper introduces an AI‑driven loss‑prevention platform that augments manual risk analysis with automated field recognition to map database and code models, generate loss‑related methods and interfaces, and deliver pre‑emptive avoidance, real‑time detection, and post‑incident response, achieving over 1,200% growth in identified loss methods and near‑full field coverage.
In an interview with MoonBit founder Zhang Hongbo, the platform’s AI‑driven, cloud‑native architecture, its novel WebAssembly‑optimized language, deployment strategies for large models, and the roadmap for open‑source tooling are explained, highlighting technical advantages and future commercial plans.
This article reviews the state‑of‑the‑art applications of large language models in recommendation systems, summarizing background knowledge, recent advances such as LLM4Rec, various tuning strategies, agent‑based approaches, open research problems, and future directions for generative recommendation.
This article explains the fundamentals of reinforcement learning, demonstrates how PHP can be used with neural‑network libraries such as Keras or TensorFlow to implement a simple reinforcement‑learning agent, provides a complete PHP code example, and discusses its potential applications.
This guide introduces the open‑source Realtime Voice Cloning project, explains its key features, and provides detailed installation and usage instructions—including environment setup, dependency installation, cloning the repository, and running the demo tool—to enable real‑time voice transformation with Python.
This article reviews why large language models can be applied to graph learning, outlines their capabilities and graph data characteristics, surveys current research across different graph types and LLM roles, and proposes future research directions for unified cross‑domain graph learning.
The Numpy‑ML project, created by UC Berkeley’s David Bourgin, provides a comprehensive pure‑NumPy implementation of over 30 machine‑learning algorithms—including probabilistic models, neural‑network layers, optimizers, and reinforcement‑learning agents—along with extensive data‑preprocessing utilities, all in a single open‑source repository.
This comprehensive technical report details the development, architecture, training methodology, extensive benchmark evaluations, safety measures, and inference optimizations of Meta's open‑source Llama 3 large language model series, covering models up to 405 billion parameters and supporting multilingual, multimodal, and tool‑use capabilities.
This article explains how a leading insurance technology group leverages large language models to streamline data security compliance, detailing the evolution of data management, key governance challenges, multimodal AI architecture, and practical workflows for policy enforcement, risk monitoring, and asset management.
This article surveys the design of autonomous large‑language‑model agents, detailing their modular architecture—including profiling, memory, planning, and execution—while also reviewing common profiling methods, memory structures, planning techniques, action strategies, and various learning approaches such as exemplar, human‑in‑the‑loop, and environment‑feedback training.
