[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2540":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":25,"paper":26,"created_at":38},2540,"Artificial Intelligence, Machine Learning, and Deep Learning Concepts, Methods, and Real-World Impact","https:\u002F\u002Fdoi.org\u002F10.64823\u002Fijcsa.2601011","Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have evolved from rule-based symbolic systems into data-driven computational methods capable of perception, prediction, decision support, and content generation. This chapter presents an accessible yet technically grounded overview of the relationship among AI, ML, and DL, tracing their historical development from early symbolic reasoning and theoretical foundations to modern neural networks and transformer-based systems. It explains the principal ML paradigms supervised, unsupervised, reinforcement, semi-supervised, and self-supervised learning and introduces widely used algorithms including regression, decision trees, ensemble methods, support vector machines, k-nearest neighbors, Naive Bayes, gradient boosting, and k-means clustering. The chapter then examines deep-learning architectures such as Convolutional neural networks, recurrent and long short-term memory networks, transformers, generative adversarial networks, and diffusion models, together with training concepts including back propagation, gradient descent, regularization, transfer learning, and evaluation metrics. Applications across healthcare, finance, transportation, manufacturing, agriculture, education, cyber security, and creative work are discussed, alongside challenges involving bias, explainability, privacy, misinformation, employment, and computational and environmental costs. The chapter concludes by emphasizing human-centered deployment, responsible governance, interpretability, and continuous evaluation as AI systems become increasingly integrated into high-impact domains. Keywords: artificial intelligence; machine learning; deep learning; natural language processing; computer vision; generative AI; reinforcement learning; neural networks; Responsible AI; Transformers","人工智能（Artificial Intelligence, AI）、机器学习（Machine Learning, ML）和深度学习（Deep Learning, DL）已从基于规则的符号系统演变为能够感知、预测、决策支持和内容生成的数据驱动计算方法。本章对AI、ML和DL之间的关系进行了通俗易懂且具有技术深度的概述，追溯了它们从早期符号推理和理论基础到现代神经网络和基于Transformer的系统的历史发展。本章阐释了主要的机器学习范式——监督学习、无监督学习、强化学习、半监督学习和自监督学习——并介绍了广泛使用的算法，包括回归、决策树、集成方法、支持向量机、k近邻、朴素贝叶斯、梯度提升和k均值聚类。随后，本章考察了深度学习架构，如卷积神经网络、循环神经网络和长短期记忆网络、Transformer、生成对抗网络和扩散模型，以及包括反向传播、梯度下降、正则化、迁移学习和评估指标在内的训练概念。本章讨论了AI在医疗健康、金融、交通、制造、农业、教育、网络安全和创意工作等领域的应用，同时探讨了涉及偏见、可解释性、隐私、虚假信息、就业以及计算和环境成本等方面的挑战。本章最后强调，随着AI系统日益融入高影响力领域，应以人为中心进行部署，实施负责任的治理，注重可解释性，并持续进行评估。关键词：人工智能；机器学习；深度学习；自然语言处理；计算机视觉；生成式AI；强化学习；神经网络；负责任AI；Transformer",null,"International Journal of Computer Science and Artificial Intelligence","2026-09-14T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"AI\u002FML\u002FDL通用综述章节，仅在应用部分顺带提及农业，非三农或农业信息化主题，相关性不足，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"智慧农业","农业人工智能","机器学习","深度学习","10.64823\u002Fijcsa.2601011",{"doi":25,"openalex_id":27,"authors":28,"venue":10,"cited_by_count":15,"oa_url":6,"card":31,"direction":35,"ingested_from":37},"W7212755989",[29],{"name":30,"orcid":9},"Dr Nellutla Sasikala",{"tldr":32,"method":33,"finding":34,"direction":35,"opportunity":36},"系统综述AI、机器学习与深度学习的概念、方法、应用及伦理挑战。","文献综述，梳理符号推理到Transformer的算法与训练技术。","AI已渗透多领域，但需重视可解释性、偏见与负责任治理。","农业人工智能与决策模型","可聚焦农业场景下可解释AI与轻量化模型，解决小样本、边缘部署与伦理治理空白。","openalex","2026-09-15T23:30:33.611886Z"]